Large organizations need rigorous security tools for analyzing potential vulnerabilities in their networks. However, managing large-scale networks with complex configurations is technically challenging. For example, organizational networks are usually dynamic with frequent configuration changes. These changes may include changes in the availability and connectivity of hosts and other devices, and services added to or removed from the network. Network administrators also need to respond to newly discovered vulnerabilities by applying patches and modifications to the network configuration and security policies, or utilizing defensive security resources to minimize the risk from external attacks. For instance, to prevent a remote attack targeting a host it is useful to analyze the candidate defensive strategies in choosing installation and runtime parameters for one or several intrusion prevention systems. To facilitate a scalable security analysis of organizational networks, attack graphs were proposed. Attack graphs show possible attack paths with respect to a particular network setting, which provide the necessary elements for modeling and improving the security of the network.
Existing work utilizes attack graphs for analyzing the security risks by quantifying attack graphs using a variety of techniques such as Bayesian belief propagation basic laws of probability and vertex ranking algorithms. These models lack a systematic and scalable computation of optimized network configurations. Current attack graph quantification models assume a network with known and fixed configurations in terms of the connectivity, availability and policies of the network services and components disregarding the dynamic nature of modern networks. Moreover, except for a few attempts previous work has solely focused on computing a numerical representation of the risk without addressing the more challenging problem of risk management and reduction.
In this paper, we present a rigorous probabilistic model that measures the security risk as the proba- bility of success in an attack. Our probabilistic model referred to as the success measurement model has three main features: (i) rigorous and scalable model with a clear probabilistic semantic, (ii) computation of risk probabilities with the goal of finding the maximum attack capabilities, and (iii) considering dynamic network features and the availability of mobile devices in the network. As an application of our success measurement model, we formalize the problem of utilizing network security resources as an optimization problem with the goal of computing an optimal placement of security products across a network. Our new contribution is to define this optimization problem and provide an efficient algorithm based on a standard technique called sequential linear programming. Our algorithm is proved to converge and it is scalable to large networks with thousands of components and attack paths.
Our contributions in this paper include:
• A scalable probabilistic model that uses a Bernoulli model to measure the risk in terms of the probability of success to achieve an attack goal.
• An efficient security optimization model, generated based on a quantified attack graph, to compute an optimal placement of security products according to organizational and technical constraints.
• Modeling dynamic network features for a realistic and accurate analysis of the risk associated with modern networks.
The results of our experiments confirm
three key properties of our model. First, the vulnerability values computed
from our model are accurate. Our manual inspection of the results confirms that
the probability values obtained in the experiments correlate to the
vulnerabilities of components in the network. Second, our security improvement
method efficiently finds the optimal placement of security products subject to
constraints. Third, we quantify the additional vulnerabilities introduced by
mobile devices of a dynamic network. Our results indicate that an infected
mobile device within the trusted region creates a preferred attack direction
towards the attack target, which increases the chance of success at the target
host. Our implementation efficiently computes the probabilities throughout
large attack graphs with a quadratic execution performance.
1.3 LITRATURE SURVEY
DYNAMIC SECURITY RISK MANAGEMENT USING BAYESIAN ATTACK GRAPHS
AUTHOR: N. Poolsappasit, R. Dewri, and I. Ray
PUBLISH: IEEE Transactions on Dependable and Secure Computing, vol. 9, no. 1, pp. 61–74, Jan 2012.
EXPLANATION:
Security risk assessment and mitigation
are two vital processes that need to be executed to maintain a productive IT
infrastructure. On one hand, models such as attack graphs and attack trees have
been proposed to assess the cause-consequence relationships between various
network states, while on the other hand, different decision problems have been
explored to identify the minimum-cost hardening measures. However, these risk
models do not help reason about the causal dependencies between network states.
Further, the optimization formulations ignore the issue of resource
availability while analyzing a risk model. In this paper, we propose a risk
management framework using Bayesian networks that enable a system administrator
to quantify the chances of network compromise at various levels. We show how to
use this information to develop a security mitigation and management plan. In
contrast to other similar models, this risk model lends itself to dynamic
analysis during the deployed phase of the network. A multi objective
optimization platform provides the administrator with all trade-off information
required to make decisions in a resource constrained environment.
TIME-EFFICIENT AND COST EFFECTIVE NETWORK HARDENING USING ATTACK GRAPHS
AUTHOR: M. Albanese, S. Jajodia, and S. Noel
PUBLISH: Dependable Systems and Networks (DSN), 2012 42nd Annual IEEE/IFIP International Conference on, june 2012
EXPLANATION:
Attack graph analysis has been
established as a powerful tool for analyzing network vulnerability. However,
previous approaches to network hardening look for exact solutions and thus do
not scale. Further, hardening elements have been treated independently, which
is inappropriate for real environments. For example, the cost for patching many
systems may be nearly the same as for patching a single one. Or patching a
vulnerability may have the same effect as blocking traffic with a firewall,
while blocking a port may deny legitimate service. By failing to account for
such hardening interdependencies, the resulting recommendations can be
unrealistic and far from optimal. Instead, we formalize the notion of hardening
strategy in terms of allowable actions, and define a cost model that takes into
account the impact of interdependent hardening actions. We also introduce a
near-optimal approximation algorithm that scales linearly with the size of the
graphs, which we validate experimentally.
AUTHOR: L. Wang, S. Noel, and S. Jajodia
PUBLISH: Computer Communications, vol. 29, no. 18, pp. 3812–3824, Nov. 2006. [Online]. Available: http://dx.doi.org/10.1016/j.comcom.2006.06.018
EXPLANATION:
In defending one’s network against
cyber attack, certain vulnerabilities may seem acceptable risks when considered
in isolation. But an intruder can often infiltrate a seemingly well-guarded
network through a multi-step intrusion, in which each step prepares for the
next. Attack
graphs can reveal the
threat by enumerating possible sequences of exploits that can be followed to
compromise given critical resources. However, attack graphs do not directly
provide a solution to remove the threat. Finding a solution by hand is
error-prone and tedious, particularly for larger and less secure networks whose
attack graphs are overly complicated. In this paper, we propose a solution to
automate the task of hardening a network against multi-step intrusions. Unlike
existing approaches whose solutions require removing exploits, our solution is
comprised of initially satisfied conditions only. Our solution is thus more
enforceable, because the initial conditions can be independently disabled,
whereas exploits are usually consequences of other exploits and hence cannot be
disabled without removing the causes. More specifically, we first represent
given critical resources as a logic proposition of initial conditions. We then
simplify the proposition to make hardening options explicit. Among the options
we finally choose solutions with the minimum cost. The key improvements over the
preliminary version of this paper include a formal framework of the minimum
network hardening problem, and an improved one-pass algorithm in deriving the
logic proposition while avoiding logic loops.
CHAPTER 2
2.0 SYSTEM ANALYSIS
2.1 EXISTING SYSTEM:
Existing work utilizes attack graphs for analyzing the security risks by quantifying attack graphs using a variety of techniques such as Bayesian belief propagation basic laws of probability and vertex ranking algorithms. These models lack a systematic and scalable computation of optimized network configurations. Current attack graph quantification models assume a network with known and fixed configurations in terms of the connectivity, availability and policies of the network services and components disregarding the dynamic nature of modern networks. Moreover, except for a few attempts previous work has solely focused on computing a numerical representation of the risk without addressing the more challenging problem of risk management and reduction.
Security risk assessment and mitigation are two vital processes that need to be executed to maintain a productive IT infrastructure. On one hand, models such as attack graphs and attack trees have been proposed to assess the cause-consequence relationships between various network states, while on the other hand, different decision problems have been explored to identify the minimum-cost hardening measures. However, these risk models do not help reason about the causal dependencies between network states.
Further, the optimization formulations ignore the issue of resource availability while analyzing a risk model management framework using Bayesian networks that enable a system administrator to quantify the chances of network compromise at various levels to use this information to develop a security mitigation and management plan. In contrast to other similar models, this risk model lends itself to dynamic analysis during the deployed phase of the network. A multi objective optimization platform provides the administrator with all trade-off information required to make decisions in a resource constrained environment.
2.1.1 DISADVANTAGES:
2.2 PROPOSED SYSTEM:
We present a rigorous probabilistic model that measures the security risk as the probability of success in an attack. Our new contribution is to define this optimization problem and provide an efficient algorithm based on a standard technique called sequential linear programming. Our algorithm is proved to converge and it is scalable to large networks with thousands of components and attack paths.
Our experiments confirm three key properties of our model.
First, the vulnerability values computed
from our model are accurate. Our manual inspection of the results confirms that
the probability values obtained in the experiments correlate to the
vulnerabilities of components in the network. Second, our security improvement
method efficiently finds the optimal placement of security products subject to
constraints. Third, we quantify the additional vulnerabilities introduced by
mobile devices of a dynamic network. Our results indicate that an infected
mobile device within the trusted region creates a preferred attack direction
towards the attack target, which increases the chance of success at the target
host. Our implementation efficiently computes the probabilities throughout
large attack graphs with a quadratic execution performance.
2.2.1 ADVANTAGES:
Our probabilistic model referred to as the success measurement model main features:
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
UML DIAGRAMS:
3.2 USE CASE DIAGRAM:
3.3 CLASS DIAGRAM:
3.4 SEQUENCE DIAGRAM:
| Nearest Router |
3.5 ACTIVITY DIAGRAM:
CHAPTER 4
4.0 IMPLEMENTATION:
ECSA ATTACK MODEL
Our probabilistic quantification model, referred to as success measurement model, quantifies the vulnerabilities of networked components and resources, by computing the expected chance of successful attack (ECSA) at every attack step, which is represented by an attack graph node. Our security improvement model uses the computed probabilities from the success measurement model to find optimal security defense strategies given a set of available options in the success measurement model requires three sets of inputs, which are a set of attack steps, a set of network configuration and potential vulnerabilities, and a set of ground facts. The first set includes the steps necessary to execute a targeted attack in a network.
These steps represent intermediate attack goals such as compromising a machine that has an internal connectivity with a targeted server. In addition, the attack steps also describe the various parallel choices available to an attack when achieving a specific target. The second set includes the network configurations and vulnerability data that collectively provide host software installations, inter host connectivity, running services and connections, and known or potential software vulnerabilities. The third set contains the ground fact values that describe the vulnerability, availability, and connectivity of various network configurations.
In our implementation, the first two sets of inputs (i.e., the attack steps and the network configuration data) are taken from dependency attack graphs. The system administrators use vulnerability assessment tools to explore the configurations and vulnerability data in their networks. The output of such assessment is provided as an input to attack graph generation tools. Attack graph generation tools (such as MulVAL often include customized predefined attack step rules that are applied to the configurations and vulnerability data of a network and produce a plain (that is, not quantified) attack graph.
Our model is to develop a set of ground fact values bootstrap the computation of success probabilities throughout an attack graph. The output of the computation based on our success measurement model is the input to the security optimization model (Figure 1). Using the security improvement model, we transform the quantified attack graph from the success measurement model into a mathematical program.
The resulting mathematical program includes an additional set of data that represent various network security defense strategies. In the tool that we developed, the security administrators simply feed this information as logical predicates such as ips_installed(T, E), which describes a potential installation of an intrusion prevention system of type T and security effectiveness E. The effectiveness value E is a score estimated by the system administrator based on prior experiences and available effectiveness data.
We present our success measurement model
to compute the expected chance of a successful attack on a network with respect
to the attack’s ultimate goal. We first present the definitions of the expected
chance of a successful attack (ECSA) followed by the description of an
efficient method to compute ECSA values. Our success measurement model computes
probabilities as a function of initial belief probabilities without the need
for specifying conditional probabilities required by Bayes’ theorem. Our model
measures the success of an attacker based on the attack dependencies determined
by a logical attack graph.
4.1 ALGORITHM
GNU LINEAR PROGRAMMING KIT
We implemented a tool for our computational procedures (Section 4.3) in Java (with approximately 3500 lines of code). We use (GNU Linear Programming Kit) GLPK, a well known open source linear programming API for our SLP-based procedure. Our tool parses an attack graph input file (obtained from MulVAL, computes the ECSA values according to various parameters, and performs security improvement analysis based on a set of improvement options and constraints.
We demonstrate the performance of our implementation. For each graph, we repeat the corresponding experiment to measure the time to compute the final expected chance of a successful attack at the graph’s root vertex. We compute ECSA values for the target graphs using our tool. We run our tool as a single threaded program on a machine with a 2.4 GHz Intel Core i7 processor and a 8 GB DDR3 memory. All our experiments converged with at most 20 iterations towards the solution. On average, 87.99% of the execution time for Procedure 2 is spent on the Taylor expansion from which on average 78.27% of the execution time is spent on symbolic differentiation performed using DJep1 Java library for symbolic operations. The Taylor expansion is parallelizable, and scales with the number of vertices, hence can be done efficiently offline.
SLP LINEAR ALGORITHM
For a network configuration w, let Gw be the corresponding attack graph. The complete procedure to compute the ECSA values of nodes (Definition 2) for an attack graph (Definition 1) is given next. To prepare the attack graph for computation, we execute the following procedure. Our procedureis a technique called sequential linear programming (SLP). SLP is a standard technique for solving nonlinear optimization problems, which is found to be computationally efficient and converges to an optimal solution.
4.2 MODULES:
NETWORK SECURITY:
PROBABILISTIC MODEL:
GENERATING ATTACK GRAPH:
SECURITY
OPTIMIZATION:
4.3 MODULE DESCRIPTION:
NETWORK SECURITY:
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.
PROBABILISTIC MODEL:
Our probabilistic model referred to as the success measurement model has three main features: (i) rigorous and scalable model with a clear probabilistic semantic, (ii) computation of risk probabilities with the goal of finding the maximum attack capabilities, and (iii) considering dynamic network features and the availability of mobile devices in the network.
Our probabilistic quantification model, referred to as success measurement model, quantifies the vulnerabilities of networked components and resources, by computing the expected chance of successful attack (ECSA) at every attack step, which is represented by an attack graph node. Our security improvement model uses the computed probabilities from the success measurement model to find optimal security defense strategies given a set of available options.
Probabilistic risk assessment is to accurately capture attack step dependencies and correlations. Attack dependencies in the form of attack preconditions are intrinsically captured by our model. That is because we base our analysis on attack graphs that are formed based on the dependency relations among the nodes. Therefore, the probabilities of success are computed by considering the dependency relations determined in an attack graph.
GENERATING ATTACK GRAPH:
Attack graph has several goal nodes dependencies is a logical disjunction. In reality, this disjunction indicates that there are multiple attack choices for an attacker towards a specific attack goal. For instance, consider a server with a local privilege escalation vulnerability (which is exploitable remotely in a multistep attack) and runs a network service with multiple remote vulnerabilities. An attacker must exploit one (or more) of these vulnerabilities to gain privileges on the target server. In the lack of observable evidence, one needs to compute the ECSA of a goal node with a function that correctly captures the probabilities of such attack choices. Our approach is to computationally determine attack choice probabilities according to various attack patterns.
SECURITY OPTIMIZATION:
To achieve our main research goal of reducing the probability of success in an attack, and thus optimizing the overall security of the network, we point out the necessity to model this problem as an optimization problem. Further, we attempt to model an important feature that is to consider the availability of machines in the network. In this section we describe these two contributions of our work as summarized below.
Optimizing the security of the networks given a set of security hardening products (e.g., a host based firewall), we compute an optimal distribution of these resources subject to given placement constraints. Using the rigorous probabilistic model introduced in Section 4.1, this is the first work in which a logical attack graph (Definition 1) is transformed into a system of linear and nonlinear equations with the global objective of reducing the probability of success on the graph’s ultimate attack goal. This transformation is performed efficiently and naturally and directly captures our research goal.
Machine availability and the effect of mobile devices:
Our work is the first to show how to represent and assess devices with variable availability (frequently joining and leaving the network), which is one of the characteristics of mobile devices with variable connectivity. Resources for hardening an organizational network, it is important to install a single or a combination of security hardening products so that the expected chance of a successful attack on the network is minimized. To find the best placement of a set of security products in a network, we extend the attack graph to define a security product as a special fact node referred to as an improvement node, which is a fact node that represents a security hardening product, service, practice, or policy. The objective of solving the problem of optimal placement of security products is to compute the effects of various placements of one or more improvement nodes subject to certain constraints and choose the placement that minimizes the attack goal’s ECSA value.
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.
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 8
8.1 CONCLUSION & FUTURE WORK:
In this work we formalized, implemented, and evaluated a new probabilistic model for measuring the security threats in large enterprise networks. The novelty of our work is the ability to quantitatively analyze the chance of successful attack in the presence of uncertainties about the configuration of a dynamic network and routes of potential attacks.
The results of our experiments confirm three key properties of our model. First, the vulnerability values computed from our model are accurate. Our manual inspection of the results confirms that the probability values obtained in the experiments correlate to the vulnerabilities of components in the network. Second, our security improvement method efficiently finds the optimal placement of security products subject to constraints. Third, we quantify the additional vulnerabilities introduced by mobile devices of a dynamic network.
Our results indicate that an infected mobile device within the trusted region creates a preferred attack direction towards the attack target, which increases the chance of success at the target host. Our implementation efficiently computes the probabilities throughout large attack graphs with a quadratic execution performance.
For future work, we plan to utilize and extend our success measurement model and optimal security placement algorithm to solve more complex network security optimization problems. For instance, an important issue is noise elimination in the initial belief set of values. This is an important problem that if solved will lead to the production of more accurate results.