In this paper, we will focus on the security of advanced metering infrastructure (AMI), which is one of the most crucial components of SG. AMI serves as a bridge for providing bidirectional information flow between user domain and utility domain. AMI’s main functionalities encompass power measurement facilities, assisting adaptive power pricing and demand side management, providing self-healing ability, and interfaces for other systems.
AMI is usually composed of three major types of components, namely, smart meter, data concentrator, and central system (a.k.a. AMI headend) and bidirectional communication networks among those components. AMI is exposed to various security threats such as privacy breach, energy theft, illegal monetary gain, and other malicious activities. As AMI is directly related to revenue earning, customer power consumption, and privacy, of utmost importance is securing its infrastructure. In order to protect AMI from malicious attacks, we look into the intrusion detection system (IDS) aspect of security solution.
We can define IDS as a monitoring system
for detecting any unwanted entity into a targeted system (such as AMI in our context).
We treat IDS as a second line security measure after the first line of primary
AMI security techniques such as encryption, authorization, and authentication,
Hence, changing specifications in all key IDS sensors would be expensive and
cumbersome. In this paper, we choose to employ anomaly-based IDS using data
mining approaches.
1.2 INTRODUCTION
Smart grid (SG) is a set of technologies that integrate modern information technologies with present power grid system. Along with many other benefits, two-way communication, updating users about their consuming behavior, controlling home appliances and other smart components remotely, and monitoring power grid’s stability are unique features of SG. To facilitate such kinds of novel features, SG needs to incorporate many new devices and services. For communicating, monitoring, and controlling of these devices/services, there may also be a need for many new protocols and standards. However, the combination of all these new devices, services, protocols, and standards make SG a very complex system that is vulnerable to increased security threats—like any other complex systems are. In particular, because of its bidirectional, interoperable, and software-oriented nature, SG is very prone to cyber attacks. If proper security measures are not taken, a cyber attack on SG can potentially bring about a huge catastrophic impact on the whole grid and, thus, to the society. Thus, cyber security in SG is treated as one of the vital issues by the National Institute of Standards and Technology and the Federal Energy Regulatory Commission.
In this paper, we will focus on the
security of advanced metering infrastructure (AMI), which is one of the most
crucial components of SG. AMI serves as a bridge for providing bidirectional
information flow between user domain and utility domain [2]. AMI’s main
functionalities encompass power measurement facilities, assisting adaptive
power pricing and demand side management, providing self-healing ability, and
interfaces for other systems. AMI is usually composed of three major types of
components, namely, smart meter, data concentrator, and central system (a.k.a.
AMI headend) and bidirectional communication networks among those components.
Being a complex system in itself, AMI is exposed to various security threats
such as privacy breach, energy theft, illegal monetary gain, and other
malicious activities. As AMI is directly related to revenue earning, customer
power consumption, and privacy, of utmost importance is securing its
infrastructure.
1.3 LITRATURE SURVEY
EFFICIENT AUTHENTICATION SCHEME FOR DATA AGGREGATION IN SMART GRID WITH FAULT TOLERANCE AND FAULT DIAGNOSIS
PUBLISH: IEEE Power Energy Soc. Conf. ISGT, 2012, pp. 1–8.
AUTOHR: D. Li, Z. Aung, J. R. Williams, and A. Sanchez
EXPLANATION:
Authentication schemes
relying on per-packet signature and per-signature verification introduce heavy
cost for computation and communication. Due to its constraint resources, smart
grid’s authentication requirement cannot be satisfied by this scheme. Most
importantly, it is a must to underscore smart grid’s demand for high
availability. In this paper, we present an efficient and robust approach to
authenticate data aggregation in smart grid via deploying signature
aggregation, batch verification and signature amortization schemes to less
communication overhead, reduce numbers of signing and verification operations,
and provide fault tolerance. Corresponding fault diagnosis algorithms are contributed
to pinpoint forged or error signatures. Both experimental result and
performance evaluation demonstrate our computational and communication gains.
CYBER SECURITY ISSUES FOR ADVANCED METERING INFRASTRUCTURE (AMI)
PUBLISH: IEEE Power Energy Soc. Gen. Meet. – Convers. Del. Electr. Energy 21st Century, 2008, pp. 1–5.
AUTOHR: F. M. Cleveland
EXPLANATION:
Advanced Metering
Infrastructure (AMI) is becoming of increasing interest to many stakeholders,
including utilities, regulators, energy markets, and a society concerned about
conserving energy and responding to global warming. AMI technologies, rapidly overtaking
the earlier Automated Meter Reading (AMR) technologies, are being developed by
many vendors, with portions being developed by metering manufacturers,
communications providers, and back-office Meter Data Management (MDM) IT
vendors. In this flurry of excitement, very little effort has yet been focused
on the cyber security of AMI systems. The comment usually is “Oh yes, we
will encrypt everything – that will make everything secure.” That comment
indicates unawareness of possible security threats of AMI – a technology that
will reach into a large majority of residences and virtually all commercial and
industrial customers. What if, for instance, remote connect/disconnect were
included as one AMI capability – a function of great interest to many utilities
as it avoids truck rolls. What if a smart kid hacker in his basement cracked
the security of his AMI system, and sent out 5 million disconnect commands to
all customer meters on the AMI system.
INTRUSION DETECTION FOR ADVANCED METERING INFRASTRUCTURES: REQUIREMENTS AND ARCHITECTURAL DIRECTIONS
PUBLISH: IEEE Int. Conf. SmartGridComm, 2010, pp. 350–355.
AUTOHR: R. Berthier, W. H. Sanders, and H. Khurana,
EXPLANATION:
The security of
Advanced Metering Infrastructures (AMIs) is of critical importance. The use of
secure protocols and the enforcement of strong security properties have the
potential to prevent vulnerabilities from being exploited and from having
costly consequences. However, as learned from experiences in IT security,
prevention is one aspect of a comprehensive approach that must also include the
development of a complete monitoring solution. In this paper, we explore the
practical needs for monitoring and intrusion detection through a thorough
analysis of the different threats targeting an AMI. In
order to protect AMI from malicious attacks, we look into the intrusion
detection system (IDS) aspect of security solution. We can define IDS as a
monitoring system for detecting any unwanted entity into a targeted system
(such as AMI in our context). We treat IDS as a second line security measure
after the first line of primary AMI security techniques such as encryption,
authorization, and authentication, such as [3]. However, Cleveland [4] stressed
that these first line security solutions alone are not sufficient for securing
AMI.
MOA: MASSIVE ONLINE ANALYSIS, A FRAMEWORK FOR STREAM CLASSIFICATION AND CLUSTERING
PUBLISH: JMLR Workshop Conf. Proc., Workshop Appl. Pattern Anal., 2010, vol. 11, pp. 44–50.
AUTOHR: A. Bifet, G. Holmes, B. Pfahringer, P. Kranen, H. Kremer, T. Jansen, and T. Seidl
EXPLANATION:
In today’s applications, massive, evolving data streams are
ubiquitous. Massive Online Analysis (MOA) is a software environment for
implementing algorithms and running experiments for online learning from
evolving data streams. MOA is designed to deal with the challenging problems of
scaling up the implementation of state of the art algorithms to real world
dataset sizes and of making algorithms comparable in benchmark streaming settings.
It contains a collection of offline and online algorithms for both
classification and clustering as well as tools for evaluation. Researchers
benefit from MOA by getting insights into workings and problems of different
approaches, practitioners can easily compare several algorithms and apply them
to real world data sets and settings. MOA supports bi-directional interaction
with WEKA, the Waikato Environment for Knowledge Analysis, and is released
under the GNU GPL license. Besides providing algorithms and measures for
evaluation and comparison, MOA is easily extensible with new contributions and
allows the creation of benchmark scenarios through storing and sharing setting
files.
SECURING ADVANCED METERING INFRASTRUCTURE USING INTRUSION DETECTION SYSTEM WITH DATA STREAM MINING
PUBLISH: Proc. PAISI, 2012, vol. 7299, pp. 96–111.
AUTOHR: M. A. Faisal, Z. Aung, J. Williams, and A. Sanchez
EXPLANATION:
Advanced metering
infrastructure (AMI) is an imperative component of the smart grid, as it is
responsible for collecting, measuring, analyzing energy usage data, and
transmitting these data to the data concentrator and then to a central system
in the utility side. Therefore, the security of AMI is one of the most
demanding issues in the smart grid implementation. In this paper, we propose an
intrusion detection system (IDS) architecture for AMI which will act as a
complimentary with other security measures. This IDS architecture consists of
three local IDSs placed in smart meters, data concentrators, and central system
(AMI headend). For detecting anomaly, we use data stream mining approach on the
public KDD CUP 1999 data set for analysis the requirement of the three
components in AMI. From our result and analysis, it shows stream data mining
technique shows promising potential for solving security issues in AMI.
DATA STREAM MINING ARCHITECTURE FOR NETWORK INTRUSION DETECTION
PUBLISH: IEEE Int. Conf. IRI, 2004, pp. 363–368
AUTOHR: N. C. N. Chu, A. Williams, R. Alhajj, and K. Barker
EXPLANATION:
In this paper, we propose
a stream mining architecture which is based on a single-pass approach. Our
approach can be used to develop efficient, effective, and active intrusion
detection mechanisms which satisfy the near real-time requirements of
processing data streams on a network with minimal overhead. The key idea is
that new patterns can now be detected on-the-fly. They are flagged as network
attacks or labeled as normal traffic, based on the current network trend, thus
reducing the false alarm rates prevalent in active network intrusion systems
and increasing the low detection rate which characterizes passive approaches.
RESEARCH ON DATA MINING TECHNOLOGIES APPLYING INTRUSION DETECTION
PUBLISH: Proc. IEEE ICEMMS, 2010, pp. 230–233
AUTOHR: Z. Qun and H. Wen-Jie
EXPLANATION:
Intrusion detection is one
of network security area of technology main research directions. Data mining
technology was applied to network intrusion detection system (NIDS), may
automatically discover the new pattern from the massive network data, to reduce
the workload of the manual compilation intrusion behavior patterns and normal
behavior patterns. This article reviewed the current intrusion detection
technology and the data mining technology briefly. Focus on data mining
algorithm in anomaly detection and misuse detection of specific applications.
For misuse detection, the main study the classification algorithm; for anomaly
detection, the main study the pattern comparison and the cluster algorithm. In
pattern comparison to analysis deeply the association rules and sequence rules
. Finally, has analysed the difficulties which the current data mining
algorithm in intrusion detection applications faced at present, and has
indicated the next research direction.
AN EMBEDDED INTRUSION DETECTION SYSTEM MODEL FOR APPLICATION PROGRAM
PUBLISH: IEEE PACIIA, 2008, vol. 2, pp. 910–912.
AUTOHR: S. Wu and Y. Chen
EXPLANATION:
Intrusion detection is an
effective security mechanism developed in the recent decade. Because of its
wide applicability, intrusion detection becomes the key part of the security
mechanism. The modern technologies and models in intrusion detection field are
categorized and studied. The characters of current practical IDS are
introduced. The theories and realization of IDS based on applications are
presented. The basic ideas concerned with how to design and realize the
embedded IDS for application are proposed.
ACCURACY UPDATED ENSEMBLE FOR DATA STREAMS WITH CONCEPT DRIFT
PUBLISH: Proc. 6th Int. Conf. HAIS Part II, 2011, pp. 155–163.
AUTOHR: D. Brzeziñski and J. Stefanowski
EXPLANATION:
In this paper we study the problem of constructing accurate
block-based ensemble classifiers from time evolving data streams. AWE is the
best-known representative of these ensembles. We propose a new algorithm called
Accuracy Updated Ensemble (AUE), which extends AWE by using online component
classifiers and updating them according to the current distribution. Additional
modifications of weighting functions solve problems with undesired classifier
excluding seen in AWE. Experiments with several evolving data sets show that,
while still requiring constant processing time and memory, AUE is more accurate
than AWE.
ACTIVE LEARNING WITH EVOLVING STREAMING DATA
PUBLISH: Proc. ECML-PKDD Part III, 2011, pp. 597–612.
AUTOHR: I. liobaitë, A. Bifet, B. Pfahringer, and G. Holmes
EXPLANATION:
In learning to classify
streaming data, obtaining the true labels may require major effort and may
incur excessive cost. Active learning focuses on learning an accurate model with
as few labels as possible. Streaming data poses additional challenges for
active learning, since the data distribution may change over time (concept
drift) and classifiers need to adapt. Conventional active learning strategies
concentrate on querying the most uncertain instances, which are typically
concentrated around the decision boundary. If changes do not occur close to the
boundary, they will be missed and classifiers will fail to adapt. In this paper
we develop two active learning strategies for streaming data that explicitly
handle concept drift. They are based on uncertainty, dynamic allocation of
labeling efforts over time and randomization of the search space. We
empirically demonstrate that these strategies react well to changes that can occur
anywhere in the instance space and unexpectedly.
LEARNING FROM TIME-CHANGING DATA WITH ADAPTIVE WINDOWING
PUBLISH: Proc. SIAM Int. Conf. SDM, 2007, pp. 443–448.
AUTOHR: A. Bifet and R. Gavaldà,
EXPLANATION:
We present a new approach for dealing with
distribution change and concept drift when learning from data sequences that
may vary with time. We use sliding windows whose size, instead of being fixed a
priori, is recomputed online according to the rate of change observed from the
data in the window itself. This delivers the user or programmer from having to
guess a time-scale for change. Contrary to many related works, we provide
rigorous guarantees of performance, as bounds on the rates of false positives and
false negatives. Using ideas from data stream algorithmics, we develop a time-
and memory-efficient version of this algorithm, called ADWIN2. We show how to
combine ADWIN2 with the Naïve Bayes (NB) predictor, in two ways: one, using it
to monitor the error rate of the current model and declare when revision is
necessary and, two, putting it inside the NB predictor to maintain up-to-date
estimations of conditional probabilities in the data. We test our approach
using synthetic and real data streams and compare them to both fixed-size and
variable-size window strategies with good results.
CHAPTER 2
2.0 SYSTEM ANALYSIS
2.1 EXISTING SYSTEM:
Existing methods in protecting an AMI against malicious activities is to create a monitoring solution that covers the heterogeneity of communication technologies through their requirements (e.g., encryption and real time) and constraints (e.g., topology and bandwidth). It is critical to identify these elements, for two reasons: 1) they can help to define the potential impact of malicious activities targeting the AMI; and 2) they can impose limits on the functionalities and security of a monitoring solution. For instance, the fact that large portions of an AMI network are wireless and use a mesh network topology facilitates network-related attacks such as traffic interception, and the design of the monitoring architecture is more challenging than in a traditional wired network. Moreover, a large number of nodes are deployed in the field or in consumer facilities, which means that attacks requiring physical access are easier to conduct.
These detection techniques are different for two fundamental reasons.
First, signature-based IDS uses a blacklist approach, while anomaly- and specification-based IDS use a white list approach. A blacklist approach requires creation of a knowledge base of malicious activity, while a white list approach requires training of the system and identification of its normal or correct behavior list approaches is that they provide little information about the root causes of attacks.
A second fundamental difference lies in the level of
understanding required by each approach. Signature- and anomaly-based IDSes
belong to the same group by monitoring activity at a low level, while
specification-based IDS requires a high-level and stateful understanding of the
activity monitored.
2.1.1 DISADVANTAGES:
2.2 PROPOSED SYSTEM:
We propose a new AMI IDS architecture based on the AMI architecture presented by OPENMeter, which is a project deployed by several European countries to reduce gap between the state-of-the-art technologies and AMI’s requirements. We use the data stream mining algorithms available in the Massive Online Analysis (MOA) in order to simulate the IDSs of the proposed architecture.
Our proposed IDS architecture follows a sequential process. Communication data from various sources are inserted to Acceptor Module. Preprocessing Unit is responsible for producing data according to predetermined attributes by monitoring the communication data. This generated data would be treated as input for Stream Mining Module. Stream Mining Module runs a data stream mining algorithm over the data generated by Preprocessing Unit. Decision Maker Unit decides whether it should trigger an alarm or not.
This module also keeps records of the information associated with attacks. These records will be used for further analysis and improving the attack database. The proposed IDS architecture for the other two types of AMI components, namely, data concentrator and AMI head end, is more or less similar to that of smart meter IDS. Again, the security boxes for those components can be either inside (in the form of software or add-on hardware card) or outside (in the form of a dedicated box or server). In order to simultaneously our proposed IDS architecture follows a sequential process.
Preprocessing Unit is responsible for
producing data according to predetermined attributes by monitoring the
communication data. This generated data would be treated as input for Stream
Mining Module. Stream Mining Module runs a data stream mining algorithm over
the data generated by Preprocessing Unit. Decision Maker Unit decides whether
it should trigger an alarm or not. This module also keeps records of the
information associated with attacks. These records will be used for further
analysis and improving the attack database.
2.2.1 ADVANTAGES:
2.3.1 HARDWARE REQUIREMENT:
CHAPTER 3
3.0 SYSTEM DESIGN:
Data Flow Diagram / Use Case Diagram / Flow Diagram:
External sources or destinations, which may be people or organizations or other entities
Here the data referenced by a process is stored and retrieved.
People, procedures or devices that produce data’s in the physical component is not identified.
Data moves in a specific direction from an origin to
a destination. The data flow is a “packet” of data.
MODELING RULES:
There are several common modeling rules when creating DFDs:
3.1 ARCHITECTURE DIAGRAM
3.2 DATAFLOW DIAGRAM
LEVEL 0
| Base station |
| IP Address |
| Generate Authentication Key |
| Send Packet Data |
LEVEL 1
| Base station |
| IP Address |
| Send Data |
| File transfer |
| Socket connection |
| Connecting |
LEVEL 2
| Router |
| IP Address |
| Socket connection |
| Routing |
| Verify file transaction |
| IDS Attack |
| Hash implementation Authentication key infrastructure Certificate revocation list |
| Security analysis |
| Encrypt |
LEVEL 3
| Node |
| IP Address |
| Received path |
| IDS Attack |
| Data Received |
UML DIAGRAMS:
3.2 USE CASE DIAGRAM:
| Base station |
| Router |
| IP Address |
| Socket connection |
| Data Transfer |
| Authentication |
| Hash implementation Public key infrastructure Certificate revocation list |
| IDS |
| Received Node |
| Received |
| Node |
3.3 CLASS DIAGRAM:
| Node |
| IP Adress |
| Browse received path |
| Connecting () |
| Base station |
| IP Address |
| Browse file |
| Connecting |
| Socket connection () () |
| File transfer () () |
| Router |
| IP Address |
| Select Connection |
| Routing () |
| File received () |
| Start receiving |
| Security analysis () |
| Encrypt () |
3.4 SEQUENCE DIAGRAM:
| Connection established |
Send data
Generating Authentication
Form routing
Routing Finished
Decode data and view
Connection terminate
Source
Base station
Destination
Establish communication
Connection established
IDS Attack
Data received
Routing Success
3.5 ACTIVITY DIAGRAM:
| Node |
| Router |
| Check |
| File not receive |
| IP address & select connection |
| Security analysis IDS |
| Routing |
| Yes Start file receive |
| No |
| IP Address & browse file |
| IP Address |
| Browse received path & connecting |
| File Received |
| Base station |
| Socket connection & connecting |
| File transfer |
CHAPTER 4
4.0 IMPLEMENTATION:
AMI:
AMI is an updated version of automatic or automated meter reading (AMR) [2]. Present traditional AMR helps a utility company in reading meters through one-way communication. However, as AMR cannot meet the current requirements for two-way communication and others, AMI is introduced. AMI is composed of smart meters, data concentrators, and central system (AMI headend) and the communication networks among them. These AMI components are usually located in various networks and different realms such as public and private ones. Fig. 1 gives a pictorial view of AMI integration in a broader context of power generation, distribution, etc. From this figure, we can see that the smart meter, responsible for monitoring and recording power usage of home appliances, etc., is the key equipment for consumers.
Home appliances and other integrated devices/systems such as water and gas meters, in-home display, plug-in electric vehicle/plug-in hybrid electric vehicle, smart thermostat, rooftop photovoltaic system, etc., constitute a home area network (HAN), which is connected to the smart meter. For communicating among these constituents, Zegbee or power line communication can be used. A number of individual smart meters communicate to a data concentrator through neighborhood area network (NAN). WiMAX, cellulartechnologies, etc., are possible means for this network. A number of data concentrators are connected to an AMI headend in the utility side using wide area network (WAN). Various long-distance communication technologies such as fiber optic, digital subscriber line, etc., are used in WAN. The AMI headend located in the utility side consists of meter data management system, geographic information system (GIS), configuration system, etc. These subsystems may build a local area network (LAN) for intercommunication.
Let us look at the first component of AMI, namely, the smart meter. Along with the houses of ordinary people, smart meters are also installed in crucial places such as companies, banks, hospitals, educational institutes, government agencies, parliaments, and presidential residences. Thus, the security of smart meters is a vital issue. To our best knowledge, current smart meters do not possess IDS facility yet. If we are to furnish smart meters with IDS, one possible approach is to develop embedded software for IDS, such as the one proposed in [20], and update the firmware of the smart meter to include these embedded IDS. Although this can be done with relative ease, the main problem is the limitation of computing resources in the current smart meters. They are mostly equipped with low-end processors and limited amounts of main memory (in kilobytes to a few megabytes range). Although this may change in the near future, since a good number of smart meters have already been deployed in many developed countries, it is not very easy to replace them orupgrade those existing ones with more powerful resources.
Since a smart meter is supposed to consume most of
its processor and main memory resources for its core businesses (such as
recording electricity usage, interaction with other smart home appliances, and
two-way communication with its associated data concentrator and, ultimately,
the headend), only a small fraction of its already limited resources is
available for IDS’ data processing purpose. We try to solve this problem of
resource scarceness by proposing to use a separate IDS entity, either installed
outside the smart meter (for existing ones) or integrated within the smart
meter (for new ones). We name such an entity a “security box.” A possible
design of a smart meter with this security box is provided in Fig. 2(a) based
on the one presented in [22]. Here, we the show security box as a simple meter
IDS. However, it is open for this component to cover other security functions
such as firewall, encryption, authorization, authentication, etc. Care should
be taken that the security box for the smart meter should not be too expensive
and hence should be equipped with resources just enough to perform computations
for IDS (and other security-related calculations, if applicable) at the meter
level.
4.1 ALGORITHM
DATA STREAM MINING ALGORITHMS
We use MOA in our experiment. It is an open source data stream mining framework. Although there are various static and evolving stream mining classification algorithms available in this software environment, we are only interested inthe evolving ones. Evolving classification algorithms care about the concept change or the distribution change in the data stream. There are 16 evolving data stream classifiers in MOA. After an initial trail on those 16 classifiers, the 7 ensemble classifiers listed in Table II are selected. (From now on, we will write classifier names in MOA in italic. In many cases, MOA’s classifiernames are self-explanatory.) These seven classifiers are chosen because they offered the highest accuracies (evaluated with EvaluatePrequential method in MOA) on the training set. Different variants of a Hoeffding tree are used as the base learner in MOA.
The algorithm establishes the Hoeffding bound, which quantifies the number of observations required to estimate necessary statistics within a prescribed precision. Mathematically, the Hoeffding bound can be expressed using (1), which says that the true mean of a random variable of range R will not differ from estimated mean after n independent examples or observations by more than with probability. The descriptions of these seven selected ensemble learners are as follows. (For more detailed descriptions, refer to the original papers [23]–[30].) AccuracyUpdatedEnsemble (AUE): It is a block-based ensemble classifier, an improved version of Accuracy Weighted Ensemble (AWE), for evolving data stream. Accuracy Updated Ensemble (AUE) makes this enhancement by using online component classifiers, which updates the base classifiers rather than only adjust their weights, as well as updating them according to present distribution. In addition, this method also leverages the drawback of AWE by redefining the weighting function.
4.2 MODULES:
SERVER CLIENT MODULE:
SMART GRID AMI:
DATA STREAM MINING METHODS:
INTRUSION DETECTION SYSTEM (IDS):
4.3 MODULE DESCRIPTION:
SERVER CLIENT MODULE:
Client-server computing or networking is a distributed application architecture that partitions tasks or workloads between service providers (servers) and service requesters, called clients. Often clients and servers operate over a computer network on separate hardware. A server machine is a high-performance host that is running one or more server programs which share its resources with clients. A client also shares any of its resources; Clients therefore initiate communication sessions with servers which await (listen to) incoming requests. Network-accessible resources may be deployed in a network as surveillance and early-warning tools, as the detection of attackers are not normally accessed for legitimate purposes.
Techniques used by the attackers that attempt to compromise
these decoy resources are studied during and after an attack to keep an eye on
new exploitation techniques. Such analysis may be used
to further tighten security of the actual network being protected by the data’s.
Data forwarding can also direct an attacker’s attention away from legitimate
servers. A user encourages attackers to spend their time and energy on the
decoy server while distracting their attention from the data on the real
server. Similar to a server, a user is a network set up with intentional
vulnerabilities. Its purpose is also to invite attacks so that the attacker’s
methods can be studied and that information can be used to increase network
security.
SMART GRID AMI:
We developing a specification for AMI networks is effective gradually, fresh specifications need to be included. Hence, changing specifications in all key IDS sensors would be expensive and cumbersome. In this paper, we choose to employ anomaly-based IDS using data mining approaches. However, instead of considering conventional static mining techniques, we select stream mining, precisely “evolving data stream mining,” as this approach is a more realistic approach in real-world monitoring and intrusion detection for AMI as various novel attacks can be introduced in AMI. Rodrigues and Gama mentioned that SG networks have various distributed sources of high-speed data streams. These stream data can be attributed as open ended and high speed and are produced in non stationary distributions. Thus, the dynamics of data are unpredictable. As the number of smart meters is suppose to eventually grow and their roles in AMI evolve over time, the topology of SG networks may change. Thus, data distribution in AMI networks can be changed. Hence, the model should be able to cope with evolved data.
AMI is composed of smart meters, data
concentrators, and central system (AMI headend) and the communication networks among
them. These AMI components are usually located in various networks and
different realms such as public and private ones a pictorial view of AMI
integration in a broader context of power generation, distribution, etc. From
this figure, we can see that the smart meter, responsible for monitoring and
recording power usage of home appliances, etc., is the key equipment for
consumers. Home appliances and other integrated devices/systems such as water
and gas meters, in-home display, plug-in electric vehicle/plug-in hybrid
electric vehicle, smart thermostat, rooftop photovoltaic system, etc., constitute
a home area network (HAN), which is connected to the smart meter.
DATA STREAM MINING METHODS:
We will focus on the security of advanced metering infrastructure (AMI), which is one of the most crucial components of SG. AMI serves as a bridge for providing bidirectional information flow between user domain and utility domain. AMI’s main functionalities encompass power measurement facilities, assisting adaptive power pricing and demand side management, providing self-healing ability, and interfaces for other systems.
AMI is usually composed of three major types of components, namely, smart meter, data concentrator, and central system (a.k.a. AMI headend) and bidirectional communication networks among those components. Being a complex system in itself, AMI is exposed to various security threats such as privacy breach, energy theft, illegal monetary gain, and other malicious activities. As AMI is directly related to revenue earning, customer power consumption, and privacy, of utmost importance is securing its infrastructure.
Our data streaming protect AMI from
malicious attacks, we look into the intrusion detection system (IDS) aspect of
security solution. We can define IDS as a monitoring system for detecting any unwanted
entity into a targeted system (such as AMI in our context). We treat IDS as a
second line security measure after the first line of primary AMI security techniques
such as encryption, authorization, and authentication solutions alone are not
sufficient for securing AMI.
INTRUSION DETECTION SYSTEM (IDS):
We develop embedded software for IDS, such as the one proposed in, and update the firmware of the smart meter to include these embedded IDS. Although this can be done with relative ease, the main problem is the limitation of computing resources in the current smart meters. They are mostly equipped with low-end processors and limited amounts of main memory (in kilobytes to a few megabytes range). Although this may change in the near future, since a good number of smart meters have already been deployed in many developed countries, it is not very easy to replace them or upgrade those existing ones with more powerful resources.
Since a smart meter is supposed to consume most of its processor and main memory resources for its core businesses (such as recording electricity usage, interaction with other smart home appliances, and two-way communication with its associated data concentrator and, ultimately, the head end), only a small fraction of its already limited resources is available for IDS’ data processing purpose. We try to solve this problem of resource scarceness by proposing to use a separate IDS entity, either installed outside the smart meter (for existing ones) or integrated within the smart meter (for new ones). We name such an entity a “security box.” A possible design of a smart meter with this security box is provided in Fig. 2(a) based on the one presented in show security box as a simple meter IDS.
However, it is open for this component to cover other security functions such as firewall, encryption, authorization, authentication, etc. IDS architecture for the other two types of AMI components, namely, data concentrator and AMI headend, is more or less similar to that of smart meter IDS. Again, the security boxes for those components can be either inside (in the form of software or add-on hardware card) or outside (in the form of a dedicated box or server). In order to simultaneously monitor a large number of data lows received from a large number of smart meters and detect security threats, such security box hardware (or the host equipment, in the case of software) must be rich in computing resources.
CHAPTER 5
5.0 SYSTEM STUDY:
5.1 FEASIBILITY STUDY:
The feasibility of the project is analyzed in this phase and business proposal is put forth with a very general plan for the project and some cost estimates. During system analysis the feasibility study of the proposed system is to be carried out. This is to ensure that the proposed system is not a burden to the company. For feasibility analysis, some understanding of the major requirements for the system is essential.
Three key considerations involved in the feasibility analysis are
5.1.1 ECONOMICAL FEASIBILITY:
This study is carried out to check the economic impact that the system will have on the organization. The amount of fund that the company can pour into the research and development of the system is limited. The expenditures must be justified. Thus the developed system as well within the budget and this was achieved because most of the technologies used are freely available. Only the customized products had to be purchased.
This study is carried out to check the technical feasibility, that is, the technical requirements of the system. Any system developed must not have a high demand on the available technical resources. This will lead to high demands on the available technical resources. This will lead to high demands being placed on the client. The developed system must have a modest requirement, as only minimal or null changes are required for implementing this system.
5.1.3 SOCIAL FEASIBILITY:
The aspect of study is to check the level of acceptance of the system by the user. This includes the process of training the user to use the system efficiently. The user must not feel threatened by the system, instead must accept it as a necessity. The level of acceptance by the users solely depends on the methods that are employed to educate the user about the system and to make him familiar with it. His level of confidence must be raised so that he is also able to make some constructive criticism, which is welcomed, as he is the final user of the system.
5.2 SYSTEM TESTING:
Testing is a process of checking whether the developed system is working according to the original objectives and requirements. It is a set of activities that can be planned in advance and conducted systematically. Testing is vital to the success of the system. System testing makes a logical assumption that if all the parts of the system are correct, the global will be successfully achieved. In adequate testing if not testing leads to errors that may not appear even many months.
This creates two problems, the time lag between the cause and the appearance of the problem and the effect of the system errors on the files and records within the system. A small system error can conceivably explode into a much larger Problem. Effective testing early in the purpose translates directly into long term cost savings from a reduced number of errors. Another reason for system testing is its utility, as a user-oriented vehicle before implementation. The best programs are worthless if it produces the correct outputs.
5.2.1 UNIT TESTING:
| Description | Expected result |
| Test for application window properties. | All the properties of the windows are to be properly aligned and displayed. |
| Test for mouse operations. | All the mouse operations like click, drag, etc. must perform the necessary operations without any exceptions. |
A program
represents the logical elements of a system. For a program to run
satisfactorily, it must compile and test data correctly and tie in properly
with other programs. Achieving an error free program is the responsibility of
the programmer. Program testing checks
for two types
of errors: syntax
and logical. Syntax error is a
program statement that violates one or more rules of the language in which it
is written. An improperly defined field dimension or omitted keywords are
common syntax errors. These errors are shown through error message generated by
the computer. For Logic errors the programmer must examine the output
carefully.
5.1.2 FUNCTIONAL TESTING:
Functional testing of an application is used to prove the application delivers correct results, using enough inputs to give an adequate level of confidence that will work correctly for all sets of inputs. The functional testing will need to prove that the application works for each client type and that personalization function work correctly.When a program is tested, the actual output is compared with the expected output. When there is a discrepancy the sequence of instructions must be traced to determine the problem. The process is facilitated by breaking the program into self-contained portions, each of which can be checked at certain key points. The idea is to compare program values against desk-calculated values to isolate the problems.
| Description | Expected result |
| Test for all modules. | All peers should communicate in the group. |
| Test for various peer in a distributed network framework as it display all users available in the group. | The result after execution should give the accurate result. |
5.1. 3 NON-FUNCTIONAL TESTING:
The Non Functional software testing encompasses a rich spectrum of testing strategies, describing the expected results for every test case. It uses symbolic analysis techniques. This testing used to check that an application will work in the operational environment. Non-functional testing includes:
5.1.4 LOAD TESTING:
An important tool for implementing system tests is a Load generator. A Load generator is essential for testing quality requirements such as performance and stress. A load can be a real load, that is, the system can be put under test to real usage by having actual telephone users connected to it. They will generate test input data for system test.
| Description | Expected result |
| It is necessary to ascertain that the application behaves correctly under loads when ‘Server busy’ response is received. | Should designate another active node as a Server. |
5.1.5 PERFORMANCE TESTING:
Performance tests are utilized in order to determine the widely defined performance of the software system such as execution time associated with various parts of the code, response time and device utilization. The intent of this testing is to identify weak points of the software system and quantify its shortcomings.
| Description | Expected result |
| This is required to assure that an application perforce adequately, having the capability to handle many peers, delivering its results in expected time and using an acceptable level of resource and it is an aspect of operational management. | Should handle large input values, and produce accurate result in a expected time. |
5.1.6 RELIABILITY TESTING:
The software reliability is the ability of a system or component to perform its required functions under stated conditions for a specified period of time and it is being ensured in this testing. Reliability can be expressed as the ability of the software to reveal defects under testing conditions, according to the specified requirements. It the portability that a software system will operate without failure under given conditions for a given time interval and it focuses on the behavior of the software element. It forms a part of the software quality control team.
| Description | Expected result |
| This is to check that the server is rugged and reliable and can handle the failure of any of the components involved in provide the application. | In case of failure of the server an alternate server should take over the job. |
5.1.7 SECURITY TESTING:
Security testing evaluates system characteristics that relate to the availability, integrity and confidentiality of the system data and services. Users/Clients should be encouraged to make sure their security needs are very clearly known at requirements time, so that the security issues can be addressed by the designers and testers.
| Description | Expected result |
| Checking that the user identification is authenticated. | In case failure it should not be connected in the framework. |
| Check whether group keys in a tree are shared by all peers. | The peers should know group key in the same group. |
5.1.8 WHITE BOX TESTING:
White box testing, sometimes called glass-box testing is a test case design method that uses the control structure of the procedural design to derive test cases. Using white box testing method, the software engineer can derive test cases. The White box testing focuses on the inner structure of the software structure to be tested.
| Description | Expected result |
| Exercise all logical decisions on their true and false sides. | All the logical decisions must be valid. |
| Execute all loops at their boundaries and within their operational bounds. | All the loops must be finite. |
| Exercise internal data structures to ensure their validity. | All the data structures must be valid. |
5.1.9 BLACK BOX TESTING:
Black box testing, also called behavioral testing, focuses on the functional requirements of the software. That is, black testing enables the software engineer to derive sets of input conditions that will fully exercise all functional requirements for a program. Black box testing is not alternative to white box techniques. Rather it is a complementary approach that is likely to uncover a different class of errors than white box methods. Black box testing attempts to find errors which focuses on inputs, outputs, and principle function of a software module. The starting point of the black box testing is either a specification or code. The contents of the box are hidden and the stimulated software should produce the desired results.
| Description | Expected result |
| To check for incorrect or missing functions. | All the functions must be valid. |
| To check for interface errors. | The entire interface must function normally. |
| To check for errors in a data structures or external data base access. | The database updation and retrieval must be done. |
| To check for initialization and termination errors. | All the functions and data structures must be initialized properly and terminated normally. |
All
the above system testing strategies are carried out in as the development,
documentation and institutionalization of the proposed goals and related
policies is essential.
CHAPTER 6
6.0 SOFTWARE DESCRIPTION:
Java technology is both a programming language and a platform.
With most programming languages, you either compile or interpret a program so that you can run it on your computer. The Java programming language is unusual in that a program is both compiled and interpreted. With the compiler, first you translate a program into an intermediate language called Java byte codes —the platform-independent codes interpreted by the interpreter on the Java platform. The interpreter parses and runs each Java byte code instruction on the computer. Compilation happens just once; interpretation occurs each time the program is executed. The following figure illustrates how this works.
You can think of Java byte codes as the machine code instructions for the Java Virtual Machine (Java VM). Every Java interpreter, whether it’s a development tool or a Web browser that can run applets, is an implementation of the Java VM. Java byte codes help make “write once, run anywhere” possible. You can compile your program into byte codes on any platform that has a Java compiler. The byte codes can then be run on any implementation of the Java VM. That means that as long as a computer has a Java VM, the same program written in the Java programming language can run on Windows 2000, a Solaris workstation, or on an iMac.
A platform is the hardware or software environment in which a program runs. We’ve already mentioned some of the most popular platforms like Windows 2000, Linux, Solaris, and MacOS. Most platforms can be described as a combination of the operating system and hardware. The Java platform differs from most other platforms in that it’s a software-only platform that runs on top of other hardware-based platforms.
The Java platform has two components:
You’ve already been introduced to the Java VM. It’s the base for the Java platform and is ported onto various hardware-based platforms.
The Java API is a large collection of ready-made software components that provide many useful capabilities, such as graphical user interface (GUI) widgets. The Java API is grouped into libraries of related classes and interfaces; these libraries are known as packages. The next section, What Can Java Technology Do? Highlights what functionality some of the packages in the Java API provide.
The following figure depicts a program that’s running on the Java platform. As the figure shows, the Java API and the virtual machine insulate the program from the hardware.
Native code is code that after you compile it, the compiled code runs on a specific hardware platform. As a platform-independent environment, the Java platform can be a bit slower than native code. However, smart compilers, well-tuned interpreters, and just-in-time byte code compilers can bring performance close to that of native code without threatening portability.
The most common types of programs written in the Java programming language are applets and applications. If you’ve surfed the Web, you’re probably already familiar with applets. An applet is a program that adheres to certain conventions that allow it to run within a Java-enabled browser.
However, the Java programming language is not just for writing cute, entertaining applets for the Web. The general-purpose, high-level Java programming language is also a powerful software platform. Using the generous API, you can write many types of programs.
An application is a standalone program that runs directly on the Java platform. A special kind of application known as a server serves and supports clients on a network. Examples of servers are Web servers, proxy servers, mail servers, and print servers. Another specialized program is a servlet.
A servlet can almost be thought of as an applet that runs on the server side. Java Servlets are a popular choice for building interactive web applications, replacing the use of CGI scripts. Servlets are similar to applets in that they are runtime extensions of applications. Instead of working in browsers, though, servlets run within Java Web servers, configuring or tailoring the server.
How does the API support all these kinds of programs? It does so with packages of software components that provides a wide range of functionality. Every full implementation of the Java platform gives you the following features:
The Java platform also has APIs for 2D and 3D graphics, accessibility, servers, collaboration, telephony, speech, animation, and more. The following figure depicts what is included in the Java 2 SDK.
We can’t promise you fame, fortune, or even a job if you learn the Java programming language. Still, it is likely to make your programs better and requires less effort than other languages. We believe that Java technology will help you do the following:
Microsoft Open Database Connectivity (ODBC) is a standard programming interface for application developers and database systems providers. Before ODBC became a de facto standard for Windows programs to interface with database systems, programmers had to use proprietary languages for each database they wanted to connect to. Now, ODBC has made the choice of the database system almost irrelevant from a coding perspective, which is as it should be. Application developers have much more important things to worry about than the syntax that is needed to port their program from one database to another when business needs suddenly change.
Through the ODBC Administrator in Control Panel, you can specify the particular database that is associated with a data source that an ODBC application program is written to use. Think of an ODBC data source as a door with a name on it. Each door will lead you to a particular database. For example, the data source named Sales Figures might be a SQL Server database, whereas the Accounts Payable data source could refer to an Access database. The physical database referred to by a data source can reside anywhere on the LAN.
The ODBC system files are not installed on your system by Windows 95. Rather, they are installed when you setup a separate database application, such as SQL Server Client or Visual Basic 4.0. When the ODBC icon is installed in Control Panel, it uses a file called ODBCINST.DLL. It is also possible to administer your ODBC data sources through a stand-alone program called ODBCADM.EXE. There is a 16-bit and a 32-bit version of this program and each maintains a separate list of ODBC data sources.
From a programming perspective, the beauty of ODBC is that the application can be written to use the same set of function calls to interface with any data source, regardless of the database vendor. The source code of the application doesn’t change whether it talks to Oracle or SQL Server. We only mention these two as an example. There are ODBC drivers available for several dozen popular database systems. Even Excel spreadsheets and plain text files can be turned into data sources. The operating system uses the Registry information written by ODBC Administrator to determine which low-level ODBC drivers are needed to talk to the data source (such as the interface to Oracle or SQL Server). The loading of the ODBC drivers is transparent to the ODBC application program. In a client/server environment, the ODBC API even handles many of the network issues for the application programmer.
The advantages
of this scheme are so numerous that you are probably thinking there must be
some catch. The only disadvantage of ODBC is that it isn’t as efficient as
talking directly to the native database interface. ODBC has had many detractors
make the charge that it is too slow. Microsoft has always claimed that the
critical factor in performance is the quality of the driver software that is
used. In our humble opinion, this is true. The availability of good ODBC
drivers has improved a great deal recently. And anyway, the criticism about
performance is somewhat analogous to those who said that compilers would never
match the speed of pure assembly language. Maybe not, but the compiler (or
ODBC) gives you the opportunity to write cleaner programs, which means you
finish sooner. Meanwhile, computers get faster every year.
6.6 JDBC:
In an effort to set an independent database standard API for Java; Sun Microsystems developed Java Database Connectivity, or JDBC. JDBC offers a generic SQL database access mechanism that provides a consistent interface to a variety of RDBMSs. This consistent interface is achieved through the use of “plug-in” database connectivity modules, or drivers. If a database vendor wishes to have JDBC support, he or she must provide the driver for each platform that the database and Java run on.
To gain a wider acceptance of JDBC, Sun based JDBC’s framework on ODBC. As you discovered earlier in this chapter, ODBC has widespread support on a variety of platforms. Basing JDBC on ODBC will allow vendors to bring JDBC drivers to market much faster than developing a completely new connectivity solution.
JDBC was announced in March of 1996. It was released for a 90 day public review that ended June 8, 1996. Because of user input, the final JDBC v1.0 specification was released soon after.
The remainder of this section will cover enough information about JDBC for you to know what it is about and how to use it effectively. This is by no means a complete overview of JDBC. That would fill an entire book.
Few software packages are designed without goals in mind. JDBC is one that, because of its many goals, drove the development of the API. These goals, in conjunction with early reviewer feedback, have finalized the JDBC class library into a solid framework for building database applications in Java.
The goals that were set for JDBC are important. They will give you some insight as to why certain classes and functionalities behave the way they do. The eight design goals for JDBC are as follows:
SQL Level API
The designers felt that their main goal was to define a SQL interface for Java. Although not the lowest database interface level possible, it is at a low enough level for higher-level tools and APIs to be created. Conversely, it is at a high enough level for application programmers to use it confidently. Attaining this goal allows for future tool vendors to “generate” JDBC code and to hide many of JDBC’s complexities from the end user.
SQL Conformance
SQL syntax varies as you move from database vendor to database vendor. In an effort to support a wide variety of vendors, JDBC will allow any query statement to be passed through it to the underlying database driver. This allows the connectivity module to handle non-standard functionality in a manner that is suitable for its users.
JDBC must be implemental on top of common database interfaces
The JDBC SQL API must “sit” on top of other common SQL level APIs. This goal allows JDBC to use existing ODBC level drivers by the use of a software interface. This interface would translate JDBC calls to ODBC and vice versa.
Because of Java’s acceptance in the user community thus far, the designers feel that they should not stray from the current design of the core Java system.
This goal probably appears in all software design goal listings. JDBC is no exception. Sun felt that the design of JDBC should be very simple, allowing for only one method of completing a task per mechanism. Allowing duplicate functionality only serves to confuse the users of the API.
Strong typing allows for more error checking to be done at compile time; also, less error appear at runtime.
Because more often than not, the usual SQL calls
used by the programmer are simple SELECT’s,
INSERT’s,
DELETE’s
and UPDATE’s,
these queries should be simple to perform with JDBC. However, more complex SQL
statements should also be possible.
Finally we decided to precede the implementation using Java Networking.
And for dynamically updating the cache table we go for MS Access database.
Java ha two things: a programming language and a platform.
Java is a high-level programming language that is all of the following
Simple Architecture-neutral
Object-oriented Portable
Distributed High-performance
Interpreted Multithreaded
Robust Dynamic Secure
Java is also unusual in that each Java program is both compiled and interpreted. With a compile you translate a Java program into an intermediate language called Java byte codes the platform-independent code instruction is passed and run on the computer.
Compilation happens just once; interpretation occurs each time the program is executed. The figure illustrates how this works.
| Java Program |
| Compilers |
| Interpreter |
| My Program |
The TCP/IP stack is shorter than the OSI one:
TCP is a connection-oriented protocol; UDP (User Datagram Protocol) is a connectionless protocol.
The IP layer provides a connectionless and unreliable delivery system. It considers each datagram independently of the others. Any association between datagram must be supplied by the higher layers. The IP layer supplies a checksum that includes its own header. The header includes the source and destination addresses. The IP layer handles routing through an Internet. It is also responsible for breaking up large datagram into smaller ones for transmission and reassembling them at the other end.
UDP is also connectionless and unreliable. What it adds to IP is a checksum for the contents of the datagram and port numbers. These are used to give a client/server model – see later.
TCP supplies logic to give a reliable connection-oriented protocol above IP. It provides a virtual circuit that two processes can use to communicate.
In order to use a service, you must be able to find it. The Internet uses an address scheme for machines so that they can be located. The address is a 32 bit integer which gives the IP address.
Class A uses 8 bits for the network address with 24 bits left over for other addressing. Class B uses 16 bit network addressing. Class C uses 24 bit network addressing and class D uses all 32.
Internally, the UNIX network is divided into sub networks. Building 11 is currently on one sub network and uses 10-bit addressing, allowing 1024 different hosts.
8 bits are finally used for host addresses within our subnet. This places a limit of 256 machines that can be on the subnet.
The 32 bit address is usually written as 4 integers separated by dots.
A service exists on a host, and is identified by its port. This is a 16 bit number. To send a message to a server, you send it to the port for that service of the host that it is running on. This is not location transparency! Certain of these ports are “well known”.
A socket is a data structure maintained by the system
to handle network connections. A socket is created using the call socket. It returns an integer that is like a file descriptor.
In fact, under Windows, this handle can be used with Read File and Write File
functions.
#include <sys/types.h>
#include <sys/socket.h>
int socket(int family, int type, int protocol);
Here “family” will be AF_INET for IP communications, protocol will be zero, and type will depend on whether TCP or UDP is used. Two
processes wishing to communicate over a network create a socket each. These are
similar to two ends of a pipe – but the actual pipe does not yet exist.
6.8 JFREE CHART:
JFreeChart is a free 100% Java chart library that makes it easy for developers to display professional quality charts in their applications. JFreeChart’s extensive feature set includes:
A consistent and well-documented API, supporting a wide range of chart types;
A flexible design that is easy to extend, and targets both server-side and client-side applications;
Support for many output types, including Swing components, image files (including PNG and JPEG), and vector graphics file formats (including PDF, EPS and SVG);
JFreeChart is “open source” or, more specifically, free software. It is distributed under the terms of the GNU Lesser General Public Licence (LGPL), which permits use in proprietary applications.
Charts showing values that relate to geographical areas. Some examples include: (a) population density in each state of the United States, (b) income per capita for each country in Europe, (c) life expectancy in each country of the world. The tasks in this project include: Sourcing freely redistributable vector outlines for the countries of the world, states/provinces in particular countries (USA in particular, but also other areas);
Creating an appropriate dataset interface (plus
default implementation), a rendered, and integrating this with the existing
XYPlot class in JFreeChart; Testing, documenting, testing some more,
documenting some more.
Implement a new (to JFreeChart) feature for interactive time series charts — to display a separate control that shows a small version of ALL the time series data, with a sliding “view” rectangle that allows you to select the subset of the time series data to display in the main chart.
There is currently a lot of interest in dashboard displays. Create a flexible dashboard mechanism that supports a subset of JFreeChart chart types (dials, pies, thermometers, bars, and lines/time series) that can be delivered easily via both Java Web Start and an applet.
The property editor mechanism in JFreeChart only
handles a small subset of the properties that can be set for charts. Extend (or
reimplement) this mechanism to provide greater end-user control over the
appearance of the charts.
CHAPTER 7
7.0 APPENDIX
7.1 SAMPLE SCREEN SHOTS:
7.2
SAMPLE SOURCE CODE:
CHAPTER 8
8.1 CONCLUSION
In this paper, we have proposed
architecture for the comprehensive IDS in AMI, which is designed to be
reliable, dynamic, and considering the real-time nature of traffic for each
component in AMI. Then, we conduct a performance analysis experiment of the
seven existing state-of-the-art data stream mining algorithms on a public IDS
data set. Finally, we elucidate the strengths and weaknesses of those
algorithms and assess the suitability of each of them to serve as the IDSs for
the three different components of AMI. We have observed that some algorithms
that use very minimal amount of computing resources and offer moderate level of
accuracy can potentially be used for the smart meter IDS. On the other hand,
the algorithms that require more computing resources and offer higher accuracy
levels can be useful for the IDSs in data concentrators and AMI headends.
8.2 FUTURE ENHANCEMENT:
As future work, we plan to develop our
own lightweight yet accurate data stream mining algorithms to be used for the
smart meter IDS and to set up a small-scale hardware platform for AMI to test
our algorithms. In conclusion, we hope our research can make contributions toward
more secure AMI deployments by the use of steam datamining-based IDSs.