We propose and analyze a behavior-rule specification-based technique for intrusion detection of medical devices embedded in a medical cyber physical system (MCPS) in which the patient’s safety is of the utmost importance. We propose a methodology to transform behavior rules to a state machine, so that a device that is being monitored for its behavior can easily be checked against the transformed state machine for deviation from its behavior specification. Using vital sign monitor medical devices as an example; we demonstrate that our intrusion detection technique can effectively trade false positives off for a high detection probability to cope with more sophisticated and hidden attackers to support ultra safe and secure MCPS applications. Moreover, through a comparative analysis, we demonstrate that our behavior-rule specification based IDS technique outperforms two existing anomaly-based techniques for detecting abnormal patient behaviors in pervasive healthcare applications.
- INTRODUCTION
The most prominent characteristic of a medical cyber physical system (MCPS) is its feedback loop that acts on the physical environment. In other words, the physical environment provides data to the MCPS sensors whose data feed the MCPS control algorithms that drive the actuators which change the physical environment. MCPSs are often characterized by sophisticated patient treatment algorithms interacting with the physical environment including the patient. In this paper, we are concerned with intrusion detection mechanisms for detecting compromised sensors or actuators embedded in an MCPS for supporting safe and secure MCPS applications upon which patients and healthcare personnel can depend with high confidence.
Intrusion detection system (IDS) design for cyber physical systems (CPSs) has attracted considerable attention because of the dire consequence of CPS failure. However, IDS techniques for MCPSs is still in its infancy with very little work reported. Intrusion detection techniques in general can be classified into four types: signature, anomaly, trust, and specification-based techniques. In this paper, we consider specification rather than signature-based detection to deal with unknown attacker patterns. We consider specification rather than anomaly based techniques to avoid using resource constrained sensors or actuators in an MCPS for profiling anomaly patterns (e.g., through learning) and to avoid high false positives. We consider specification rather than trust based techniques to avoid delay due to trust aggregation and propagation to promptly react to malicious behaviors in safety critical MCPSs.
To accommodate resource-constrained sensors and actuators in an MCPS, we propose behavior-rule specification-based intrusion detection (BSID) which uses the notion of behavior rules for specifying acceptable behaviors of medical devices in an MCPS. Rule-based intrusion detection thus far has been applied only in the context of communication networks which have no concern of physical environments and the closed-loop control structure as in an MCPS. For example, Da Silva et al. propose an IDS that applies seven types of traffic-based rules to detect intruders: interval, retransmission, integrity, delay, repetition, radio transmission range and jamming. Ioannis et al. propose a multi trust IDS with traffic-based collection that audits the forwarding behavior of suspects to detect black hole and grey hole attacks launched by captured devices based on the rate of specification violations.
Our contribution relative to prior work cited above is that we specifically consider behavior rules for MCPS actuators controlling patient treatment algorithms as well as for physiological sensors providing information concerning the physical environment. Further, we propose a methodology to transform behavior rules to a state machine, so that a device that is being monitored for its behavior can easily be checked against the transformed state machine for deviation from its behavior specification. Existing work only considered specification-based state machines for intrusion detection of communication protocol misbehaving patterns.
Untreated in the literature, in this paper we also investigate the impact of attacker behaviors on the effectiveness of MCPS intrusion detection. We demonstrate that our specification based IDS technique can effectively trade higher false positives off for lower false negatives to cope with more sophisticated and hidden attackers. We show results for a range of configurations to illustrate this trade. Because the key motivation in MCPS is safety, our solution is deployed in a configuration yielding a high detection rate without compromising the false positive probability. Our approach is monitoring-based relying on the use of peer devices to monitor and measure the compliance degree of a trustee device connected to the monitoring node by the CPS network. The rules comparing monitor and trustee physiology (blood pressure, oxygen saturation, pulse, respiration and temperature) exceeds protection possible by considering devices in isolation.
The fundamental difference in designing IDSs for safety critical CPSs versus for other brands of systems is that the intrusion detection is closely tied with the physical components of the CPS, so the detection is less about communication protocol compliance but more about behavior compliance specific to the physical components to be controlled in the CPS. Thus, instead of monitoring packet routing or packet loss data for misbehavior detection of communication protocol compliance during packet transmission, IDSs for MCPSs may test medical sensor measurements and actuator settings for misbehavior detection of physical properties manifested because of attacks. For example, a patient requesting analgesic must have a pulse greater than some threshold, otherwise it may cause an overdose of analgesic delivered. Thus, if a patient requests analgesic while having a pulse below the threshold then an intruder may be involved. The behavior rules proposed in our work specifically address the expected behavior of individual physical components in the MCPS. The compliance threshold proposed in this paper specifically measures the goodness of a physical component. A challenge is to provide a high detection rate without introducing high false positives. We demonstrate that our IDS design based on the compliance threshold can effectively distinguish benign abnormalities from malicious attacks. To the best of our knowledge, there is no prior work discussing the difference between CPS intrusion detection and communication systems intrusion detection.
It is necessary to build an IDS per CPS domain/application since the behavior rules for specifying the behaviors of physical components/devices in a CPS are inherently domain/application specific. In the literature, ISML and T-Rex are also specification-based approaches for intrusion detection in CPSs. However, none of them considered MCPSs. In the field of intrusion detection for MCPSs or healthcare systems, Asfaw et al. studied an anomaly-based IDS for MCPSs. The authors focus on attacks that violate privacy of an MCPS; in contrast, our investigation focuses on attacks that violate the integrity of an MCPS. They use an anomaly-based approach while we use a specification-based approach. Asfaw et al. do not provide numerical results in the form of false negatives or positives which are the critical metrics for this research area; our investigation does provide these results.
Venkatasubramanian and Gupta survey security solutions for pervasive healthcare applications. Like , the authors focus on attacks on a passive pervasive healthcare system that violate patient privacy while our investigation considers integrity attacks on an MCPS that harm a patient. Their countermeasures focus on encryption and authentication/access control.
Yang and Hwang investigated an approach to fraud and abuse detection in healthcare applications. In contrast, our investigation focuses on the treatment, rather than the administrative, domain of healthcare. The authors use an anomaly-based approach while we use a specification-based approach. They provide numerical results that measure internal validity (the effectiveness of the data mining implementation) but do not provide externally valid metrics like Receiver Operating Characteristic(ROC) which can reveal the tradeoff between the detection rate vs. the false positive probability Porras and Neumann study a hierarchical multi trust behavior-based IDS called Event Monitoring Enabling Responses to Anomalous Live Disturbances (EMERALD) using complementary signature based and anomaly-based analysis. The authors identify a signature-based analysis trade between the state space created/runtime burden imposed by rich rule sets and the increased false negatives that stem from a less expressive rule set.
Porras and Neumann highlight two specific anomaly-based techniques using statistical analysis: one studies user sessions (to detect live intruders), and the other studies the runtime behavior of programs (to detect malicious code). EMERALD provides a generic analysis framework that is flexible enough to allow anomaly detectors to run with different scopes of multi trust data (service, domain or enterprise). However, Porras and Neumann did not report false positive or false negative probability data. While EMERALD pursues a domain-independent CPS security solution combining anomaly and signature-based analysis, our investigation focuses on one that is relevant for MCPSs using specification-based analysis. Park et al.propose a semi-supervised anomaly-based IDS targeted for assisted living environments. Their design is behavior-based and audits series of events which they call episodes. The authors’ events are 3-tuples comprising sensor ID, start time and duration. Park et al. test data sets using four similarity functions based on: LCS, count of common events not in LCS, event start times and event durations. They control episode length and similarity function as independent variables. The authors provide excellent ROC data which we use for a comparative analysis.
Tsang and Kwong propose a multi trust IDS called Multi-agent System (MAS) that includes an analysis function called Ant Colony Clustering Model (ACCM). The authors intend for ACCM to reduce the characteristically high false positive rate of anomaly-based approaches while minimizing the training period by using an unsupervised approach to machine learning. MAS is hierarchical and contains a large number of roles: monitor agents collect audit data, decision agents perform analysis, action agents effect responses, coordination agents manage multi trust communication, user interface agents interact with human operators and registration agents manage agent appearance and disappearance. Their results indicate ACCM slightly outperforms the detection rates and significantly outperforms the false positive rates of k means and expectation-maximization approaches. Like, MAS pursues a domain-independent CPS security solution using anomaly-based analysis; our investigation focuses on MCPS-specific IDS using specification-based analysis. We will use Park et al. and Tsang and Kwong as base schemes against which BSID will be compared because no others provide meaningful pfp/pfn data for a comparative analysis.
Our study of IDS warrants distinct treatment for medical versus generic CPSs because the behavior rule set we propose is application specific. CPSs in other domains will not have temperature sensors, medication dispensers or actuators supporting cardiac function. Furthermore, each CPS domain will have a unique environment: For example, while the population in an MCPS may be around 1000 based on the number of beds in a hospital, the population for a smart grid CPS may be in the millions. Also, while the geography of a MCPS may span a single square kilometer based on the size of a medical campus, the area of operation for a unmanned air vehicle (UAV) may be thousands of km2.
1.3 LITRATURE SURVEY
REDUNDANCY MANAGEMENT OF MULTIPATH ROUTING FOR INTRUSION TOLERANCE IN HETEROGENEOUS WIRELESS SENSOR NETWORKS.
PUBLICATION: H. Al-Hamadi and I. R. Chen. IEEE Transactions on Network and Service Management, 10(2):189–203, 2013.
In this paper we propose redundancy management of heterogeneous wireless sensor networks (HWSNs), utilizing multipath routing to answer user queries in the presence of unreliable and malicious nodes. The key concept of our redundancy management is to exploit the tradeoff between energy consumption vs. the gain in reliability, timeliness, and security to maximize the system useful lifetime. We formulate the tradeoff as an optimization problem for dynamically determining the best redundancy level to apply to multipath routing for intrusion tolerance so that the query response success probability is maximized while prolonging the useful lifetime. Furthermore, we consider this optimization problem for the case in which a voting-based distributed intrusion detection algorithm is applied to detect and evict malicious nodes in a HWSN. We develop a novel probability model to analyze the best redundancy level in terms of path redundancy and source redundancy, as well as the best intrusion detection settings in terms of the number of voters and the intrusion invocation interval under which the lifetime of a HWSN is maximized. We then apply the analysis results obtained to the design of a dynamic redundancy management algorithm to identify and apply the best design parameter settings at runtime in response to environment changes, to maximize the HWSN lifetime.
TELECOMMUNICATIONS DEMAND AND PRICING STRUCTURE: AN ECONOMETRIC ANALYSIS.
PUBLICATION: M. Aldebert, M. Ivaldi, and C. Roucolle. Telecommunication Systems, 25:89–115, 2004.
The main objective of this paper is to analyse residential demand by traffic destination, using a translogarithmic indirect utility function. We focus on five traffic directions, in order to construct a model adapted to evaluate the characteristics of telecommunications demand in a competitive market. The resulting price elasticities express high reactivity to own price changes for the main traffic directions, as well as little interactions between the different types of traffic. Moreover the high values of income elasticities confirm the importance of income effects when analysing residential telecommunications demand. This model shows useful for welfare analysis. The computation of customers’ income equivalent variation shows, on average, a higher willingness to pay for some traffic directions than the bill actually paid. Finally we show that the optimal prices for the operator, in a cost minimisation point of view, are higher than the observed prices for local and national traffic directions. This emphasises the existence of important cross-subsidies among the different segments of customers.
SECURITY CHALLENGES IN NEXT GENERATION CYBER PHYSICAL SYSTEMS.
PUBLICATION: M. Anand, E. Cronin, M. Sherr, M. Blaze, Z. Ives, and I. Lee. Beyond SCADA: Networked Embedded Control for Cyber Physical Systems, 2006.
The advent of low-powered wireless networks of embedded sensors has spurred the development of new applications at the interface between the real world and its digital manifestation. Following this trend, the next generation Supervisory Control And Data Acquisition (SCADA) system is expected to replace traditional data gathering – a distributed network of Remote Terminal Units (RTU) or Programmable Logic Controllers (PLC), with devices such as the wireless sensing devices. Before these intelligent systems can be deployed in critical infrastructure such as emergency rooms and power plants, the security properties of sensors must be fully understood. Existing wisdom has been to apply the traditional security models and techniques to sensor networks: as in conventional computing environments, the goal has been to protect physical entities: devices, packets, links, and ultimately networks. Sensors have unique characteristics that warrant novel security considerations: the geographic distribution of the devices allows an attacker to physically capture nodes and learn secret key material, or to intercept or inject messages; the hierarchical nature of sensor networks and their route maintenance protocols permit the attacker to determine where the root node is placed. Perhaps most importantly, most sensor networks rely on redundancy (followed by aggregation) to accurately capture environmental information even with poorly calibrated and unreliable devices. This results in a fundamental distinction between a physical message in a sensor network and a logical unit of sensed information: a message with a single sensor reading may reveal very little information about the real environment, whereas a message containing an aggregate or collection of readings may reveal a great deal more.
HOST-BASED ANOMALY DETECTION FOR PERVASIVE MEDICAL SYSTEMS.
PUBLICATION: B. Asfaw, D. Bekele, B. Eshete, A. Villafiorita, and K. Weldemariam. In Fifth International Conference on Risks and Security of Internet and Systems, pages 1–8, October 2010.
Intrusion detection systems are deployed on hosts in a computing infrastructure to tackle undesired events in the course of usage of the systems. One of the promising domains of applying intrusion detection is the healthcare domain. A typical healthcare scenario is characterized by high degree of mobility, frequent interruptions and above all demands access to sensitive medical records by concerned stakeholders. Migrating this set of concerns in pervasive healthcare environments where the traditional characteristics are more intensified in terms of uncertainty, one ends up with more challenges on security due to nature of pervasive devices and wireless communication media along with classic security problems for desktop based systems. Despite evolution of automated healthcare services and sophistication of attacks against such services, there is a reasonable lack of techniques, tools and experimental setups for protecting hosts against intrusive actions. This paper presents a contribution to provide a host-based, anomaly modeling and detection approach based on data mining techniques for pervasive healthcare systems. The technique maintains normal usage profile of pervasive healthcare applications and inspects current work flow against normal usage profile so as to classify it as anomalous or normal. The technique is implemented as a prototype with sample data set and the results obtained revealed that the technique is able to perform classification of anomalous activities.
CHAPTER 2
2.0 SYSTEM ANALYSIS
2.1 EXISTING SYSTEM:
Existing work only considered specification-based state machines for intrusion detection of communication protocol misbehaving patterns. Before that not using trust based techniques to avoid delay due to trust aggregation and propagation to promptly react to malicious behaviors in safety critical MCPSs.
2.1.1 DISADVANTAGES:
2.2 PROPOSED SYSTEM:
We propose a methodology to transform behavior rules to a state machine, so that a device that is being monitored for its behavior can easily be checked against the transformed state machine for deviation from its behavior specification. We also investigate the impact of attacker behaviors on the effectiveness of MCPS intrusion detection. We demonstrate that our specification based IDS technique can effectively trade higher false positives off for lower false negatives to cope with more sophisticated and hidden attackers. We show results for a range of configurations to illustrate this trade. Because the key motivation in MCPS is safety, our solution is deployed in a configuration yielding a high detection rate without compromising the false positive probability. Our approach is monitoring-based relying on the use of peer devices to monitor and measure the compliance degree of a trustee device connected to the monitoring node by the CPS network. The rules comparing monitor and trustee physiology (blood pressure, oxygen saturation, pulse, respiration and temperature) exceeds protection possible by considering devices in isolation.
2.2.1
ADVANTAGES:
2.3 HARDWARE & SOFTWARE REQUIREMENTS:
2.3.1 HARDWARE REQUIREMENT:
v Processor – Pentium –IV
- Speed –
1.1 GHz
- RAM – 256 MB (min)
- Hard Disk – 20 GB
- Floppy Drive – 1.44 MB
- Key Board – Standard Windows Keyboard
- Mouse – Two or Three Button Mouse
- Monitor – SVGA
2.3.2 SOFTWARE REQUIREMENTS:
- Operating System : Windows XP
- Front End : Microsoft Visual Studio .NET 2008
- Back End : MS-SQL Server 2005
- Document : MS-Office 2007
CHAPTER 3
3.0 SYSTEM DESIGN:
Data Flow Diagram / Use Case Diagram / Flow Diagram:
- The DFD is also called as bubble chart. It is a simple graphical formalism that can be used to represent a system in terms of the input data to the system, various processing carried out on these data, and the output data is generated by the system
- The data flow diagram (DFD) is one of the most important modeling tools. It is used to model the system components. These components are the system process, the data used by the process, an external entity that interacts with the system and the information flows in the system.
- DFD shows how the information moves through the system and how it is modified by a series of transformations. It is a graphical technique that depicts information flow and the transformations that are applied as data moves from input to output.
- DFD is also known as bubble chart. A DFD may be used to represent a system at any level of abstraction. DFD may be partitioned into levels that represent increasing information flow and functional detail.
NOTATION:
SOURCE OR DESTINATION OF DATA:
External sources or destinations, which may be people or organizations or other entities
DATA SOURCE:
Here the data referenced by a process is stored and retrieved.
PROCESS:
People, procedures or devices that produce data. The physical component is not identified.
DATA FLOW:
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:
- All processes must have at least one data flow in and one data flow out.
- All processes should modify the incoming data, producing new forms of outgoing data.
- Each data store must be involved with at least one data flow.
- Each external entity must be involved with at least one data flow.
- A data flow must be attached to at least one process.
3.1 BLOCK DIAGRAM
3.2 DATAFLOW DIAGRAM
UML DIAGRAMS:
3.2 USE CASE DIAGRAM:
3.3 CLASS DIAGRAM:
3.4 SEQUENCE DIAGRAM:
3.5
ACTIVITY DIAGRAM:
CHAPTER 4
4.0 IMPLEMENTATION:
4.1 ALGORITHM
4.2 MODULES:
The system is proposed to have the following modules along with functional requirements.
- THREAT MODEL
- ATTACKER ARCHETYPES
- BEHAVIOR RULES
- INTRUSION DETECTION SYSTEM
4.3 MODULE DESCRIPTION:
1. THREAT MODEL
We focus on defeating inside attackers that violate the integrity of the MCPS with the objective to disable the MCPS functionality. Our design is also effective against attacks such as subtle manipulations that change medical doses slightly to cause long term harm to patients or medical or billing record exfiltrations which violate privacy. There are two distinct stages in an attack: before a node is compromised and after a node is compromised. Before a node is compromised, the adversary focuses on the tactical goal of achieving a foothold on the target system.
2. ATTACKER ARCHETYPES
We differentiate two attacker archetypes: reckless, random and opportunistic. A reckless attacker performs attacks whenever it has a chance to impair the MCPS functionality as soon as possible. A random attacker, on the other hand, performs attacks only randomly to avoid detection. It is thus insidious and hidden with the objective to cripple the MCPS functionality. We model the attacker behavior by a random attack probability pa. When pa = 1 the attacker is a reckless adversary. Random attacks are typically implemented with on off attacks in real-world scenarios, so pa is not a random variable drawn from uniform distribution U(0, 1) but rather a probability that a malicious node is performing attacks at any time with this on-off attack behavior. An opportunistic attacker is the third archetype. An opportunistic attacker exploits ambient noise modeled by perr (probability of mis-monitoring)to perform attacks.
3. BEHAVIOR RULES
Behavior rules for a device are specified during the design and testing phase of an MCPS. Our intrusion detection protocol takes a set of behavior rules for a device as input and detects if a device’s behavior deviates from the expected behavior specified by the set of behavior rules. Since the intrusion detection activity is performed in the background, it allows behavior rules to be changed if incomplete or imprecise specifications are discovered during the operational phase
Without disrupting the MCPS operation. Our IDS design for the reference MCPS model relies on
The use of lightweight specification-based behavior rules for each sensor or actuator medical device.
4. INTRUSION DETECTION SYSTEM
Intrusion detection system (IDS) design for cyber physical systems (CPSs) has attracted considerable because of the dire consequence of CPS failure. In this paper, we consider specification rather than signature-based detection to deal with unknown attacker patterns. We consider specification rather than anomaly based techniques to avoid using resource constrained
Sensors or actuators in an MCPS for profiling
anomaly patterns (e.g., through learning) and to avoid high false positives. We
consider specification rather than trust based techniques to avoid delay due to
trust aggregation and propagation to promptly react to malicious behaviors in Safety
critical MCPSs.
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
- ECONOMICAL FEASIBILITY
- TECHNICAL FEASIBILITY
- SOCIAL FEASIBILITY
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.
5.1.2 TECHNICAL FEASIBILITY:
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:
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.
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. |
5.1.3 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.
FUNCTIONAL TESTING:
| 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. 4 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:
- Load testing
- Performance testing
- Usability testing
- Reliability testing
- Security testing
5.1.5 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.
Load Testing
| 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.
PERFORMANCE TESTING:
| 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.
RELIABILITY TESTING:
| 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.
SECURITY TESTING:
| 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. |
5.1.7 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.
5.1.8 WHITE BOX TESTING:
| 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.
5.1.10 BLACK BOX TESTING:
| 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 SPECIFICATION:
6.1 FEATURES OF .NET:
Microsoft .NET is a set of Microsoft software technologies for rapidly building and integrating XML Web services, Microsoft Windows-based applications, and Web solutions. The .NET Framework is a language-neutral platform for writing programs that can easily and securely interoperate. There’s no language barrier with .NET: there are numerous languages available to the developer including Managed C++, C#, Visual Basic and Java Script.
The .NET framework provides the foundation for components to interact seamlessly, whether locally or remotely on different platforms. It standardizes common data types and communications protocols so that components created in different languages can easily interoperate.
“.NET” is also the collective name given to various software components built upon the .NET platform. These will be both products (Visual Studio.NET and Windows.NET Server, for instance) and services (like Passport, .NET My Services, and so on).
6.2 THE .NET FRAMEWORK
The .NET Framework has two main parts:
1. The Common Language Runtime (CLR).
2. A hierarchical set of class libraries.
The CLR is described as the “execution engine” of .NET. It provides the environment within which programs run. The most important features are
- Conversion from a low-level assembler-style language, called Intermediate Language (IL), into code native to the platform being executed on.
- Memory management, notably including garbage collection.
- Checking and enforcing security restrictions on the running code.
- Loading and executing programs, with version control and other such features.
- The following features of the .NET framework are also worth description:
Managed Code
The code that targets .NET, and which contains certain extra Information – “metadata” – to describe itself. Whilst both managed and unmanaged code can run in the runtime, only managed code contains the information that allows the CLR to guarantee, for instance, safe execution and interoperability.
Managed Data
With Managed Code comes Managed Data. CLR provides memory allocation and Deal location facilities, and garbage collection. Some .NET languages use Managed Data by default, such as C#, Visual Basic.NET and JScript.NET, whereas others, namely C++, do not. Targeting CLR can, depending on the language you’re using, impose certain constraints on the features available. As with managed and unmanaged code, one can have both managed and unmanaged data in .NET applications – data that doesn’t get garbage collected but instead is looked after by unmanaged code.
Common Type System
The CLR uses something called the Common Type System (CTS) to strictly enforce type-safety. This ensures that all classes are compatible with each other, by describing types in a common way. CTS define how types work within the runtime, which enables types in one language to interoperate with types in another language, including cross-language exception handling. As well as ensuring that types are only used in appropriate ways, the runtime also ensures that code doesn’t attempt to access memory that hasn’t been allocated to it.
Common Language Specification
The CLR provides built-in support for language interoperability. To ensure that you can develop managed code that can be fully used by developers using any programming language, a set of language features and rules for using them called the Common Language Specification (CLS) has been defined. Components that follow these rules and expose only CLS features are considered CLS-compliant.
6.3 THE CLASS LIBRARY
.NET provides a single-rooted hierarchy of classes, containing over 7000 types. The root of the namespace is called System; this contains basic types like Byte, Double, Boolean, and String, as well as Object. All objects derive from System. Object. As well as objects, there are value types. Value types can be allocated on the stack, which can provide useful flexibility. There are also efficient means of converting value types to object types if and when necessary.
The set of classes is pretty comprehensive, providing collections, file, screen, and network I/O, threading, and so on, as well as XML and database connectivity.
The class library is subdivided into a number of sets (or namespaces), each providing distinct areas of functionality, with dependencies between the namespaces kept to a minimum.
6.4 LANGUAGES SUPPORTED BY .NET
The multi-language capability of the .NET Framework and Visual Studio .NET enables developers to use their existing programming skills to build all types of applications and XML Web services. The .NET framework supports new versions of Microsoft’s old favorites Visual Basic and C++ (as VB.NET and Managed C++), but there are also a number of new additions to the family.
Visual Basic .NET has been updated to include many new and improved language features that make it a powerful object-oriented programming language. These features include inheritance, interfaces, and overloading, among others. Visual Basic also now supports structured exception handling, custom attributes and also supports multi-threading.
Visual Basic .NET is also CLS compliant, which means that any CLS-compliant language can use the classes, objects, and components you create in Visual Basic .NET.
Managed Extensions for C++ and attributed programming are just some of the enhancements made to the C++ language. Managed Extensions simplify the task of migrating existing C++ applications to the new .NET Framework.
C# is Microsoft’s new language. It’s a C-style language that is essentially “C++ for Rapid Application Development”. Unlike other languages, its specification is just the grammar of the language. It has no standard library of its own, and instead has been designed with the intention of using the .NET libraries as its own.
Microsoft Visual J# .NET provides the easiest transition for Java-language developers into the world of XML Web Services and dramatically improves the interoperability of Java-language programs with existing software written in a variety of other programming languages.
Active State has created Visual Perl and Visual Python, which enable .NET-aware applications to be built in either Perl or Python. Both products can be integrated into the Visual Studio .NET environment. Visual Perl includes support for Active State’s Perl Dev Kit.
Other languages for which .NET compilers are available include
- FORTRAN
- COBOL
- Eiffel
| ASP.NET XML WEB SERVICES | Windows Forms |
| Base Class Libraries | |
| Common Language Runtime | |
| Operating System |
Fig1 .Net Framework
C#.NET is also compliant with CLS (Common Language Specification) and supports structured exception handling. CLS is set of rules and constructs that are supported by the CLR (Common Language Runtime). CLR is the runtime environment provided by the .NET Framework; it manages the execution of the code and also makes the development process easier by providing services.
C#.NET is a CLS-compliant language. Any objects, classes, or components that created in C#.NET can be used in any other CLS-compliant language. In addition, we can use objects, classes, and components created in other CLS-compliant languages in C#.NET .The use of CLS ensures complete interoperability among applications, regardless of the languages used to create the application.
CONSTRUCTORS AND DESTRUCTORS:
Constructors are used to initialize objects, whereas destructors are used to destroy them. In other words, destructors are used to release the resources allocated to the object. In C#.NET the sub finalize procedure is available. The sub finalize procedure is used to complete the tasks that must be performed when an object is destroyed. The sub finalize procedure is called automatically when an object is destroyed. In addition, the sub finalize procedure can be called only from the class it belongs to or from derived classes.
GARBAGE COLLECTION
Garbage Collection is another new feature in C#.NET. The .NET Framework monitors allocated resources, such as objects and variables. In addition, the .NET Framework automatically releases memory for reuse by destroying objects that are no longer in use.
In C#.NET, the garbage collector checks for the objects that are not currently in use by applications. When the garbage collector comes across an object that is marked for garbage collection, it releases the memory occupied by the object.
OVERLOADING
Overloading is another feature in C#. Overloading enables us to define multiple procedures with the same name, where each procedure has a different set of arguments. Besides using overloading for procedures, we can use it for constructors and properties in a class.
MULTITHREADING:
C#.NET also supports multithreading. An application that supports multithreading can handle multiple tasks simultaneously, we can use multithreading to decrease the time taken by an application to respond to user interaction.
STRUCTURED EXCEPTION HANDLING
C#.NET supports structured handling, which enables us to
detect and remove errors at runtime. In C#.NET, we need to use
Try…Catch…Finally statements to create exception handlers. Using
Try…Catch…Finally statements, we can create robust and effective exception
handlers to improve the performance of our application.
6.5 THE .NET FRAMEWORK
The .NET Framework is a new computing platform that simplifies application development in the highly distributed environment of the Internet.
OBJECTIVES OF .NET FRAMEWORK
1. To provide a consistent object-oriented programming environment whether object codes is stored and executed locally on Internet-distributed, or executed remotely.
2. To provide a code-execution environment to minimizes software deployment and guarantees safe execution of code.
3. Eliminates the performance problems.
There are
different types of application, such as Windows-based applications and
Web-based applications.
6.6 FEATURES OF SQL-SERVER
The OLAP Services feature available in SQL Server version 7.0 is now called SQL Server 2000 Analysis Services. The term OLAP Services has been replaced with the term Analysis Services. Analysis Services also includes a new data mining component. The Repository component available in SQL Server version 7.0 is now called Microsoft SQL Server 2000 Meta Data Services. References to the component now use the term Meta Data Services. The term repository is used only in reference to the repository engine within Meta Data Services
SQL-SERVER database consist of six type of objects,
They are,
1. TABLE
2. QUERY
3. FORM
4. REPORT
5.
MACRO
TABLE:
A database is a collection of data about a specific topic.
VIEWS OF TABLE:
We can work with a table in two types,
1. Design View
2. Datasheet View
Design View
To build or modify the structure of a table we work in the table design view. We can specify what kind of data will be hold.
Datasheet View
To add, edit or analyses the data itself we work in tables datasheet view mode.
QUERY:
A query is a question that has to be asked the data. Access gathers data that answers the question from one or more table. The data that make up the answer is either dynaset (if you edit it) or a snapshot (it cannot be edited).Each time we run query, we get latest information in the dynaset. Access either displays the dynaset or snapshot for us to view or perform an action on it, such as deleting or updating.
CHAPTER 7
APPENDIX
7.1 SAMPLE SOURCE CODE
7.2
SAMPLE OUTPUT
CHAPTER 8
8.1 CONCLUSION For safety-critical MCPSs, being able to detect attackers while limiting the false alarm probability to protect the welfare of patients is of utmost importance. In this paper we proposed a behavior-rule specification-based IDS technique for intrusion detection of medical devices embedded in a MCPS. We exemplified the utility with VSMs and demonstrated that the detection probability of the medical device approaches one (that is, we can always catch the attacker without false negatives) while bounding the false alarm probability to below 5% for reckless attackers and below 25% for random and opportunistic attackers over a wide range of environment noise levels. Through a comparative analysis, we demonstrated that our behavior-rule specification-based IDS technique outperforms existing techniques based on anomaly intrusion detection. In future work, we plan to analyze the overheads of our detection techniques such as the various distance-based methods in comparison with contemporary approaches. We also plan to deepen adversary modeling research based on stochastic Petri net techniques such that the system can dynamically adjust CT to maximize intrusion detection performance in response to changing attacker behaviors at runtime.
