BEHAVIORAL MALWARE DETECTION IN DELAY TOLERANT NETWORKS
By
A
PROJECT REPORT
Submitted to the Department of Computer Science & Engineering in the FACULTY OF ENGINEERING & TECHNOLOGY
In partial fulfillment of the requirements for the award of the degree
Of
MASTER OF TECHNOLOGY
IN
COMPUTER SCIENCE & ENGINEERING
APRIL 2015
BONAFIDE CERTIFICATE
Certified that this project report titled “BEHAVIORAL MALWARE DETECTION IN DELAY TOLERANT NETWORKS” is the bonafide work of Mr. _____________Who carried out the research under my supervision Certified further, that to the best of my knowledge the work reported herein does not form part of any other project report or dissertation on the basis of which a degree or award was conferred on an earlier occasion on this or any other candidate.
Signature of the Guide Signature of the H.O.D
Name Name
CHAPTER 1
1.0 ABSTRACT:
The delay-tolerant-network (DTN) model is becoming a viable communication alternative to the traditional infrastructural model for modern mobile consumer electronics equipped with short-range communication technologies such as Bluetooth, NFC, and Wi-Fi Direct. Proximity malware is a class of malware that exploits the opportunistic contacts and distributed nature of DTNs for propagation. Behavioral characterization of malware is an effective alternative to pattern matching in detecting malware, especially when dealing with polymorphic or obfuscated malware.
In this paper, we first propose a general
behavioral characterization of proximity malware which based on naive Bayesian
model, which has been successfully applied in non-DTN settings such as
filtering email spams and detecting botnets. We identify two unique challenges
for extending Bayesian malware detection to DTNs (“insufficient evidence versus
evidence collection risk” and “filtering false evidence sequentially and
distributedly”), and propose a simple yet effective method, look ahead, to address
the challenges. Furthermore, we propose two extensions to look ahead, dogmatic
filtering, and adaptive look ahead, to address the challenge of “malicious
nodes sharing false evidence.” Real mobile network traces are used to verify
the effectiveness of the proposed methods.
1.1 INTRODUCTION
MALWARE:
Malware, short for malicious software, is any software used to disrupt computer operation, gather sensitive information, or gain access to private computer systems. It can appear in the form of executable code, scripts, active content, and other software. ‘Malware’ is a general term used to refer to a variety of forms of hostile or intrusive software. The term badware is sometimes used, and applied to both true (malicious) malware and unintentionally harmful software, viruses, worms, trojan horses, ransomware, spyware, adware, scareware and other malicious programs of active malware threats were worms or trojans rather than viruses. In law, malware is sometimes known as a computer contaminant, as in the legal codes of several malware is often disguised as, or embedded in, non-malicious files.
Spyware or other malware is sometimes found embedded in programs supplied officially by companies, e.g., downloadable from websites, that appear useful or attractive, but may have, for example, additional hidden tracking functionality that gathers marketing statistics. An example of such software, which was described as illegitimate, is the Sony rootkit, a Trojan embedded into CDs sold by Sony, which silently installed and concealed itself on purchasers’ computers with the intention of preventing illicit copying; it also reported on users’ listening habits, and created vulnerabilities that were exploited by unrelated malware.
PURPOSES:
Many early infectious programs, including the first Internet Worm, were written as experiments or pranks. Today, malware is used by both black hat hackers and governments, to steal personal, financial, or business information and sometimes for sabotage (e.g., Stuxnet). Malware is sometimes used broadly against government or corporate websites to gather guarded information, or to disrupt their operation in general. However, malware is often used against individuals to gain information such as personal identification numbers or details, bank or credit card numbers, and passwords. Left unguarded, personal and networked computers can be at considerable risk against these threats. (These are most frequently defended against by various types of firewall, anti-virussoftware, and network hardware).
Since the rise of widespread broadband Internet access, malicious software has more frequently been designed for profit. Since 2003, the majority of widespread viruses and worms have been designed to take control of users’ computers for illicit purposes. Infected “zombie computers” are used to send email spam, to host contraband data such as child pornography, or to engage in distributed denial-of-service attacks as a form of extortion.
Programs designed to monitor users’ web browsing, display unsolicited advertisements, or redirect affiliate marketing revenues are called spyware. Spyware programs do not spread like viruses; instead they are generally installed by exploiting security holes. They can also be packaged together with user-installed software, such as peer-to-peer applications.
Ransomware affects an infected computer in some way, and demands payment to reverse the damage. For example, programs such as CryptoLocker encrypt files securely, and only decrypt them on payment of a substantial sum of money.
INFECTIOUS MALWARE: VIRUSES AND WORMS:
The best-known types of malware, viruses and
worms, are known for the manner in which they spread, rather than any specific
types of behavior. The term computer
virus is used for a program that embeds itself in
some other executable software
(including the operating system itself) on the target system without the users
consent and when that is run causes the virus to spread to other executables.
On the other hand, a worm is
a stand-alone malware program that actively transmits itself over
a network to
infect other computers. These definitions lead to the observation that a virus requires
the user to run an infected program or operating system for the virus to
spread, whereas a worm spreads itself.
CONCEALMENT: Viruses, trojan horses, rootkits, and backdoors
TROJAN HORSES
For a malicious program to accomplish its goals, it must be able to run without being detected, shut down, or deleted. When a malicious program is disguised as something normal or desirable, users may unwittingly install it. This is the technique of the Trojan horse or trojan. In broad terms, a Trojan horse is any program that invites the user to run it, concealing harmful or malicious executable code of any description. The code may take effect immediately and can lead to many undesirable effects, such as encrypting the user’s files or downloading and implementing further malicious functionality.[citation needed]
In the case of some spyware, adware, etc. the supplier may require the user to acknowledge or accept its installation, describing its behavior in loose terms that may easily be misunderstood or ignored, with the intention of deceiving the user into installing it without the supplier technically in breach of the law.[citation needed]
ROOTKITS
Once a malicious program is installed on a system, it is essential that it stays concealed, to avoid detection. Software packages known as rootkits allow this concealment, by modifying the host’s operating system so that the malware is hidden from the user. Rootkits can prevent a malicious process from being visible in the system’s list of processes, or keep its files from being read.
Some malicious programs contain routines to defend against removal, not merely to hide them. An early example of this behavior is recorded in the Jargon File tale of a pair of programs infesting a Xerox CP-V time sharing system:
Each ghost-job would detect the fact that the other
had been killed, and would start a new copy of the recently-stopped program
within a few milliseconds. The only way to kill both ghosts was to kill them
simultaneously (very difficult) or to deliberately crash the system.
BACKDOORS
A backdoor is a method of bypassing normal authentication procedures, usually over a connection to a network such as the Internet. Once a system has been compromised, one or more backdoors may be installed in order to allow access in the future,[30] invisibly to the user.
The idea has often been suggested that computer manufacturers preinstall backdoors on their systems to provide technical support for customers, but this has never been reliably verified. It was reported in 2014 that US government agencies had been diverting computers purchased by those considered “targets” to secret workshops where software or hardware permitting remote access by the agency was installed, considered to be among the most productive operations to obtain access to networks around the world. Backdoors may be installed by Trojan horses, worms, implants, or other methods.
Malware authors target bugs, or loopholes, to exploit. A common method is exploitation of vulnerability, where software designed to store data in a specified region of memory does not prevent more data than the buffer can accommodate being supplied. Malware may provide data that overflows the buffer, with malicious executable code or data after the end; when this payload is accessed it does what the attacker, not the legitimate software, determines.
INSECURE DESIGN OR USER ERROR
Early PCs had to be booted from floppy disks; when built-in hard drives became common the operating system was normally started from them, but it was possible to boot from another boot device if available, such as a floppy disk, CD-ROM, DVD-ROM, or USB flash drive. It was common to configure the computer to boot from one of these devices when available. Normally none would be available; the user would intentionally insert, say, a CD into the optical drive to boot the computer in some special way, for example to install an operating system. Even without booting, computers can be configured to execute software on some media as soon as they become available, e.g. to autorun a CD or USB device when inserted.
Malicious software distributors would trick the user into booting or running from an infected device or medium; for example, a virus could make an infected computer add autorunnable code to any USB stick plugged into it; anyone who then attached the stick to another computer set to autorun from USB would in turn become infected, and also pass on the infection in the same way. More generally, any device that plugs into a USB port-—”including gadgets like lights, fans, speakers, toys, even a digital microscope”—can be used to spread malware. Devices can be infected during manufacturing or supply if quality control is inadequate.
This form of infection can largely be avoided by setting up computers by default to boot from the internal hard drive, if available, and not to autorun from devices. Intentional booting from another device is always possible by pressing certain keys during boot.
Older email software would automatically open HTML
email containing potentially malicious JavaScript code;
users may also execute disguised malicious email attachments and infected
executable files supplied in other ways.
1.2 SCOPE OF THE PROJECT:
We present a general behavioral characterization of proximity malware, which captures the functional but imperfect nature in detecting proximity malware characterization, and with a simple cut-off malware containment strategy, we formulate the malware detection process as a distributed decision problem. We analyze the risk associated with the decision, and design a simple, yet effective, strategy, look ahead, which naturally reflects individual nodes’ intrinsic risk inclinations against malware infection. Look ahead extends the naive Bayesian model, and addresses the DTN specific, malware-related, “insufficient evidence versus evidence collection risk” problem.
We consider the benefits of sharing
assessments among nodes, and address challenges derived from the DTN model:
liars (i.e., bad-mouthing and false praising malicious nodes) and defectors
(i.e., good nodes that have turned rogue due to malware infections). We present
two alternative techniques, dogmatic filtering and adaptive look ahead, that
naturally extend look ahead to consolidate evidence provided by others, while
containing the negative effect of false evidence. A nice property of the
proposed evidence consolidation methods is that the results will not worsen
even if liars are the majority in the neighborhood traces are used to verify
the effectiveness of the methods.
CHAPTER 2
2.0 SYSTEM ANALYSIS
2.1 EXISTING SYSTEM:
Existing worms, spam, and phishing exploit gaps in traditional threat models that usually revolve around preventing unauthorized access and information disclosure. The new threat landscape requires security researchers to consider a wider range of attacks: opportunistic attacks in addition to targeted ones; attacks coming not just from malicious users, but also from subverted (yet otherwise benign) hosts; coordinated/distributed attacks in addition to isolated, single-source methods; and attacks blending flaws across layers, rather than exploiting a single vulnerability. Some of the largest security lapses in the last decade are due to designers ignoring the complexity of the threat landscape.
The increasing penetration of wireless networking, and more specifically wifi, may soon reach critical mass, making it necessary to examine whether the current state of wireless security is adequate for fending off likely attacks.
Three
types of threats that seem insufficiently addressed by
existing technology and deployment techniques. The first threat is wildfire
worms, a class of worms that spreads contagiously between hosts on
neighboring APs. We show that such worms can spread to a large fraction of hosts
in a dense urban setting, and that the propagation speed can be such that most
existing defenses cannot react in a timely fashion. Worse, such worms can
penetrate through networks protected by WEP and other security mechanisms. The
second threat we discuss is large-scale spoofing attacks that can be used for
massive phishing and spam campaigns. We show how an attacker can easily use a
botnet by acquiring access to wifi-capable zombie hosts, and can use these
zombies to target not just the local wireless LAN, but any LAN within
range, greatly increasing his reach across heterogeneous networks.
2.2 DISADVANTAGES:
To quantify
these threats, we rely on real-world data extracted from wifi maps of large
metropolitan areas in the country. Existing
results suggest that a carefully crafted wireless worm can infect up to
80% of all wifi connected hosts in some metropolitan areas within 20 minutes,
and that an attacker can launch phishing attacks or build a tracking system to
monitor the location of 10-50% of wireless users in these metropolitan areas
with just 1,000 zombies under his control.
2.3 PROPOSED SYSTEM:
In this paper, we present a simple, yet effective solution, look ahead, which naturally reflects individual nodes’ intrinsic risk inclinations against malware infection, to balance between these two extremes. Essentially, we extend the naive Bayesian model, which has been applied in filtering email, spams detecting botnets, and designing IDSs.
We analyze the risk associated with the decision, and design a simple, yet effective, strategy, look ahead, which naturally reflects individual nodes’ intrinsic risk inclinations against malware infection. Look ahead extends the naive Bayesian model, and addresses the DTN specific, malware-related, “insufficient evidence versus evidence collection risk” Proximity malware is a malicious program that disrupts the host node’s normal function and has a chance of duplicating itself to other nodes during (opportunistic) contact opportunities between nodes in the DTN.
We consider the benefits of sharing
assessments among nodes, and address challenges derived from the DTN model:
liars (i.e., bad-mouthing and false praising malicious nodes) and defectors
(i.e., good nodes that have turned rogue due to malware infections). We present
two alternative techniques, dogmatic filtering and adaptive look ahead, that
naturally extend look ahead to consolidate evidence provided by others, while
containing the negative effect of false evidence. A nice property of the proposed
evidence consolidation methods is that the results will not worsen even if
liars are the majority in the neighborhood traces are used to verify the
effectiveness of the methods.
2.4 ADVANTAGES:
Two DTN specific, malware-related:
1. Insufficient evidence versus evidence collection risk. In DTNs, evidence (such as Bluetooth connection or SSH session requests) is collected only when nodes come into contact. But contacting malware-infected nodes carries the risk of being infected. Thus, nodes must make decisions (such as whether to cut off other nodes and, if yes, when) online based on potentially insufficient evidence.
2. Filtering false evidence sequentially
and distributedly. Sharing evidence among opportunistic acquaintances helps
alleviating the aforementioned insufficient evidence problem; however, false
evidence shared by malicious nodes (the liars) may negate the benefits of
sharing. In DTNs, nodes must decide whether to accept received evidence sequentially
and distributedly.
HARDWARE REQUIREMENT:
CHAPTER 3
3.0 SYSTEM DESIGN
ARCHITECTURE DIAGRAM / DATA FLOW DIAGRAM / UML 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. 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.
There are several common modeling rules when creating DFDs:
3.0 ARCHITECTURE DIAGRAM
DATAFLOW DIAGRAM:
LEVEL 1
Server Client
LEVEL 2
UML DIAGRAMS:
USE CASE DIAGRAM:
CLASS DIAGRAM:
SEQUENCE DIAGRAM:
ACTIVITY DIAGRAMS:
CHAPTER 4
4.0 IMPLEMENTATION
4.1 MODULES:
DELAY TOLERANT NETWORKS:
PROXIMITY MALWARE:
MALWARE PROPAGATION:
BAYESIAN FILTERING:
4.1 MODULE DESCRIPTION:
DELAY TOLERANT NETWORKS:
Delay-tolerant networking (DTN) is an approach to computer network architecture that seeks to address the technical issues in heterogeneous networks that may lack continuous network connectivity. Examples of such networks are those operating in mobile or extreme terrestrial environments, or planned networks in space.
Recently, the term disruption-tolerant networking has gained currency in the United States due to support from DARPA, which has funded many DTN projects. Disruption may occur because of the limits of wireless radio range, sparsity of mobile nodes, energy resources, attack, and noise.
DTNs, including a growing number of academic conferences on delay and disruption-tolerant networking, and growing interest in combining work from sensor networks and MANETs with the work on DTN. This field saw many optimizations on classic ad hoc and delay-tolerant networking algorithms and began to examine factors such as security, reliability, verifiability, and other areas of research that are well understood in traditional computer networking.
PROXIMITY MALWARE:
Proximity malware is a malicious program that disrupts the host node’s normal function and has a chance of duplicating itself to other nodes during (opportunistic) contact opportunities between nodes in the DTN. When duplication occurs, the other node is infected with the malware. In our model, we assume that each node is capable of assessing the other party for suspicious actions after each encounter, resulting in a binary assessment. For example, a node can assess a Bluetooth connection or an SSH session for potential Cabir or Ikee infection.
The watchdog components in previous works on malicious behavior detection in MANETs and distributed reputation systems are other examples. A node is either evil or good, based on if it is or is not infected by the malware. The suspiciousaction assessment is assumed to be an imperfect but functional indicator of malware infections: It may occasionally assess an evil node’s actions as “nonsuspicious” or a good node’s actions as “suspicious,” but most suspicious actions are correctly attributed to evil nodes. A previous work on distributed IDS presents an example for such imperfect but functional binary classifier on nodes’ behaviors.
MALWARE PROPAGATION:
We analyzed malware propagation through proximity channels in social networks. Akritidis et al. quantified the threat of proximity malware in wide-area wireless networks in optimal malware signature distribution in heterogeneous, resource-constrained mobile networks in traditional, non-DTN, networks and Bayer et al.
We proposed to detect malware with learned behavioral model, in terms of system call and program flow. We extend the Naive Bayesian model, which has been applied in filtering email spams detecting botnets and designing IDSs and address DTN-specific, malware-related, problems. In the context of detecting slowly propagating Internet worm, Dash et al. presented a distributed IDS architecture of local/global detector that resembles the neighborhood-watch model, with the assumption of attested/honest evidence, i.e., without liars.
BAYESIAN FILTERING:
Naive Bayes classifiers can be trained very efficiently in a supervised learning setting. In many practical applications, parameter estimation for naive Bayes models uses the method of maximum likelihood; in other words, one can work with the naive Bayes model without accepting Bayesian probability or using any Bayesian methods.
The implications are as follows:
. Given enough assessments, honest nodes are likely to obtain a close estimation of a node’s suspiciousness (suppose they have not cut the node off yet), even if they only use their own assessments.
. The liars have to share a significant amount of false evidence to sway the public’s opinion on a node’s suspiciousness.
. The most susceptible victims of liars are the nodes that have little evidence Dogmatic filtering. Dogmatic filtering is based on the observation that one’s own assessments are truthful and, therefore, can be used to bootstrap the evidence consolidation process. A node shall only accept evidence that will not sway its current opinion too much. We call this observation the dogmatic principle.
We extend the Naive Bayesian model,
which has been applied in filtering email, spams detecting botnets and
designing IDSs and address DTN-specific, malware-related, problems. In the
context of detecting slowly propagating Internet worm, Dash et al. presented a
distributed IDS architecture of local/global detector that resembles the neighborhood-watch
model,
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:
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:
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
7.0 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.
7.6 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.
7.8 JFREE CHART:
JFreeChart is a free 100% Java chart library that makes it easy for developers to display professional quality charts in their applications. JFreeChart’s extensive feature set includes:
A consistent and well-documented API, supporting a wide range of chart types;
A flexible design that is easy to extend, and targets both server-side and client-side applications;
Support for many output types, including Swing components, image files (including PNG and JPEG), and vector graphics file formats (including PDF, EPS and SVG);
JFreeChart is “open source” or, more specifically, free software. It is distributed under the terms of the GNU Lesser General Public Licence (LGPL), which permits use in proprietary applications.
Charts showing values that relate to geographical areas. Some examples include: (a) population density in each state of the United States, (b) income per capita for each country in Europe, (c) life expectancy in each country of the world. The tasks in this project include: Sourcing freely redistributable vector outlines for the countries of the world, states/provinces in particular countries (USA in particular, but also other areas);
Creating an appropriate dataset interface (plus
default implementation), a rendered, and integrating this with the existing
XYPlot class in JFreeChart; Testing, documenting, testing some more,
documenting some more.
Implement a new (to JFreeChart) feature for interactive time series charts — to display a separate control that shows a small version of ALL the time series data, with a sliding “view” rectangle that allows you to select the subset of the time series data to display in the main chart.
There is currently a lot of interest in dashboard displays. Create a flexible dashboard mechanism that supports a subset of JFreeChart chart types (dials, pies, thermometers, bars, and lines/time series) that can be delivered easily via both Java Web Start and an applet.
The property editor mechanism in JFreeChart only
handles a small subset of the properties that can be set for charts. Extend (or
reimplement) this mechanism to provide greater end-user control over the
appearance of the charts.
CHAPTER 7
APPENDIX
7.1 SAMPLE SOURCE CODE
7.2
SAMPLE OUTPUT
CHAPTER 8
8.1 CONCLUSION
Behavioral characterization of malware is an effective alternative to pattern matching in detecting malware, especially when dealing with polymorphic or obfuscated malware. Naive Bayesian model has been successfully applied in non-DTN settings, such as filtering email spams and detecting botnets.
We propose a general behavioral
characterization of DTN-based proximity malware. We present look ahead, along
with dogmatic filtering and adaptive look ahead, to address two unique
challenging in extending Bayesian filtering to DTNs: “insufficient evidence
versus evidence collection risk” and “filtering false evidence sequentially and
distributedly.” In prospect, extension of the behavioral characterization of
proximity malware to account for strategic malware detection evasion with game
theory is a challenging yet interesting future work.
CHAPTER 9
9.0 REFERENCES:
[1] C. Kolbitsch, P. Comparetti, C. Kruegel, E. Kirda, X. Zhou, and X. Wang, “Effective and Efficient Malware Detection at the End Host,” Proc. 18th Conf. USENIX Security Symp., 2009.
[2] U. Bayer, P. Comparetti, C. Hlauschek, C. Kruegel, and E. Kirda, “Scalable, Behavior-Based Malware Clustering,” Proc. 16th Ann. Network and Distributed System Security Symp. (NDSS), 2009.
[3] G. Zyba, G. Voelker, M. Liljenstam, A. Me´hes, and P. Johansson, “Defending Mobile Phones from Proximity Malware,” Proc. IEEE INFOCOM, 2009.
[4] F. Li, Y. Yang, and J. Wu, “CPMC: An Efficient Proximity Malware Coping Scheme in Smartphone-Based Mobile Networks,” Proc. IEEE INFOCOM, 2010.
[6] J. Zdziarski, Ending Spam: Bayesian Content Filtering and the Art of Statistical Language Classification. No Starch Press, 2005.
[7] R. Villamarı´n-Salomo´n and J. Brustoloni, “Bayesian Bot Detection Based on DNS Traffic Similarity,” Proc. ACMymp. Applied Computing (SAC), 2013.