Modeling and Detection of Camouflaging Worm

ABSTRACT

Active worms causes major security threats to the Internet. This is due to the ability of active worms to propagate in an automated fashion as they continuously compromise computers on the Internet. Active worms evolve during their propagation and thus pose great challenges to defend against them. Here we investigate a new class of active worms, referred to as Camouflaging Worm (C-Worm in short). The C-Worm is different from traditional worms because of its ability to intelligently manipulate its scan traffic volume over time. Thereby, the C-Worm camouflages its propagation from existing worm detection systems based on analyzing

the propagation traffic generated by worms. we design a novel spectrum-based scheme to detect the C-Worm. Our scheme uses the Power Spectral Density (PSD) distribution of the scan traffic volume and its corresponding Spectral Flatness Measure (SFM) to distinguish the C-Worm traffic from background traffic.

INTRODUCTION:

An active worm refers to a malicious software program that propagates itself on the Internet to infect other computers. The propagation of the worm is based on exploiting vulnerabilities of computers on the Internet. Many real-world worms have caused notable damage on the Internet. These worms include Code-Red worm in 2001 , Slammer worm in 2003 ,and Witty/Sasser worms in 2004 . Many active worms are used to infect a large number of computers and recruit them as bots or zombies, which are networked together to form botnets These botnets can be used to: (a) launch massive Distributed Denial-of-Service (DDoS) attacks that disrupt the Internet utilities , (b) access confidential information that can be misused , through large scale traffic sniffing, key logging, identity theft etc, (c) destroy data that has a high monetary value , and (d) distribute large-scale unsolicited advertisement emails (as spam) or software (as malware).There is evidence showing that infected computers are being rented out as Botnets for creating an entire black-market industry for renting, trading, and managing owned computers,leading to economic incentives for attackers . Researchers also showed possibility of super-botnets, networks of independent botnets that can be coordinated for attacks of unprecedented scale .For an adversary, super botnets would also be extremely versatile and resistant to counter measures. Due to the substantial damage caused by worms in the past years, there have been significant efforts on developing detection and defense mechanisms against worms. A network based worm detection system plays a major role by monitoring, collecting, and analyzing the scan traffic (messages to identify vulnerable computers) generated during worm attacks. In this system, the detection is commonly based on the self propagating behavior of worms that can be described as follows: after a worm-infected computer identifies and infects a vulnerable computer on the Internet, this newly infected computer1 will automatically and continuously scan several IP addresses to identify and infect other vulnerable computers. As such, numerous existing detection schemes are based on a tacit assumption that each worm-infected computer keeps scanning the Internet and propagates itself at the highest possible speed. Furthermore, it has been shown that the worm scan traffic volume and the number of worm-infected computers exhibit exponentially increasing patterns .

LITERATURE SURVEY:

Worm:

A computer worm is a self-replicating malware computer program. It uses a computer network to send copies of itself to other nodes (computers on the network) and it may do so without any user intervention. This is due to security shortcomings on the target computer. Unlike a virus, it does not need to attach itself to an existing program. Worms almost always cause at least some harm to the network, if only by consuming bandwidth, whereas viruses almost always corrupt or modify files on a targeted computer. Many worms that have been created are only designed to spread, and don't attempt to alter the systems they pass through. However, as the Morris worm and Mydoom showed, the network traffic and other unintended effects can often cause major disruption. A "payload" is code designed to do more than spread the wormit might delete files on a host system (e.g., the ExploreZip worm), encrypt files in a cryptoviral extortion attack, or send documents via e-mail. A very common payload for worms is to install a backdoor in the infected computer to allow the creation of a "zombie" computer under control of the worm author. Networks of such machines are often referred to as botnets and are very commonly used by spam senders for sending junk email or to cloak their website's address.[1] Spammers are therefore thought to be a source of funding for the creation of such worms,[2][3] and the worm writers have been caught selling lists of IP addresses of infected machines.[4] Others try to blackmail companies with threatened DoS attacks.[5]

Backdoors can be exploited by other malware, including worms. Examples include Doomjuice, which spreads better using the backdoor opened by Mydoom, and at least one instance of malware taking advantage of the rootkit and backdoor installed by the Sony/BMG DRM software utilized by millions of music CDs prior to late 2005.[dubious discuss]

Camouflage:

Which is a method of crypsisavoidance of observationthat allows an otherwise visible organism or object to remain indiscernible from the surrounding environment through deception. Examples include a tiger's stripes, the battledress of a modern soldier and a butterfly camouflaging itself as a leaf. The theory of camouflage covers the various strategies which are used to achieve this effect Cryptic coloration is the most common form of camouflage, found to some extent in the majority of species. The simplest way is for an animal to be of a color similar to its surroundings. Examples include the "earth tones" of deer, squirrels, or moles (to match trees or dirt), or the combination of blue skin and white underbelly of sharks via countershading (which makes them difficult to detect from both above and below). More complex patterns can be seen in animals such as flounder, moths, and frogs, among many others.

Anomaly Detection:

Anomaly detection, also referred to as outlier detection[1] refers to detecting patterns in a given data set that do not conform to an established normal behavior.[2] The patterns thus detected are called anomalies and often translate to critical and actionable information in several application domains. Anomalies are also referred to as outliers, surprise, aberrant, deviation, peculiarity, etc.

Three broad categories of anomaly detection techniques exist. Supervised anomaly detection techniques learn a classifier using labeled instances belonging to normal and anomaly class, and then assign a normal or anomalous label to a test instance. Semi-supervised anomaly detection techniques construct a model representing normal behavior from a given normal training data set, and then test the likelihood of a test instance to be generated by the learnt model. Unsupervised anomaly detection techniques detect anomalies in an unlabeled test data set under the assumption that majority of the instances in the data set are normal. Anomaly detection is applicable in a variety of domains, such as intrusion detection, fraud detection, fault detection, system health monitoring, event detection in sensor networks, and detecting eco-system disturbances. It is often used in preprocessing to remove anomalous data from the dataset.

EXISTING SYSTEM

The C-Worm is quite different from traditional worms in which it camouflages any noticeable trends in the number of infected computers over time. The camouflage is achieved by manipulating the scan traffic volume of worm-infected computers. Such a manipulation of the scan traffic volume prevents exhibition of any exponentially increasing trends or even crossing of thresholds that are tracked by existing detection schemes.

DRAWBACK IN EXISTING SYSTEM

C-Worm scan traffic shows no noticeable trends in the time domain, it demonstrates a distinct pattern in the frequency domain. Specifically, there is an obvious concentration within a narrow range of frequencies. This concentration within a narrow range of frequencies is inevitable since the C-Worm adapts to the dynamics of the Internet in a recurring manner for manipulating and controlling its overall scan traffic volume.

PROPOSED SYSTEM

we adopt frequency domain analysis techniques and develop a detection scheme against Wide-spreading of the C-Worm. Particularly, we develop a novel spectrum-based detection scheme that uses the Power Spectral Density (PSD) distribution of scan traffic volume in the frequency domain and its corresponding Spectral Flatness Measure (SFM) to distinguish the C-Worm traffic from non worm traffic (background traffic).

ADVANTAGES IN PROPOSED SYSTEM

Our evaluation data clearly demonstrate that our spectrum-based detection scheme achieves much better detection performance against the C-Worm propagation compared with existing detection schemes. Our evaluation also shows that our spectrum-based detection scheme is general enough to be used for effective detection of traditional worms as well.

(Centralized data centerMonitor 1Monitor 3User 1User 2User 4User 5Monitor 2User 3)System Architecture:

Data flow Diagram:

(IP addressIP addressTraffic logsTraffic logsCentralized data centerMonitor 1Monitor 2Client 1Client 2Client 3Client 4Message)

Module:

User

Monitoring

Centralized Data Center

Report Preparation

Report Distribution

Module Description:

User:

In this module user can login to the centralized server for authentication, once the client is treated as authorized then it can share data with the neighbors in the network.

Monitoring:

It will monitor the authorized clients for their transaction and it will identify the traffic log (IP address which are not commonly used and dark IP address).

Centralized Data Center:

It will collect all the traffic logs from various network monitors for identifying the worms by their IP address.

Report Preparation:

The purpose of this module is to identify the actual worm by its ratio not by scan traffic time in order to detect the active worm and the normal worm.

Report Distribution:

The centralized data center has to distribute the report logs (dark IP address) to all the users in the network.

User:

(Data CenterServer) ( Client)

( Client 1 Client nMonitor)Monitoring:

...

Centralized Data Center:

(Client 1Client 1 Monitor 1Server)

...

(Client 1Client 1Client 1Client 1 Monitor 1 Monitor nServer)Report Preparation:

.

Report Distribution

(Client 1Client nClient 1Client n Monitor 1 Monitor nServer)

.

Use Case Diagram:

Class Diagram:

Sequence Diagram:

Collaboration Diagram:

Activity Diagram:

SYSTEM REQUIREMENTS

HARDWARE

PROCESSOR: PENTIUM IV 2.6 GHz, Intel Core 2 Duo.

RAM:512 MB DD RAM

MONITOR:15 COLOR

HARD DISK :40 GB

CDDRIVE:LG 52X

SOFTWARE

Front End : JAVA (SWINGS)

Back End: MS SQL 2000/05

Operating System : Windows XP/07

IDE:Net Beans, Eclipse

Conclusion:

In this paper we presented an analytical framework, based on Interactive Markov Chains, that can be used to study the dynamics of malware propagation on a network. The exact solution of a stochastic model intended to capture the probabilistic nature of malware propagation on an arbitrary topology appears to be a major challenge, because of the high computational complexity necessary to analyze very large systems. However, one can resort to simple bounds and approximations in order to obtain a gross-level prediction of the system behavior that can help to understand important characteristics of malware propagation. Although we have focused on the modeling aspects of the problem, we believe our methodology can be usefully applied to evaluate different countermeasures against future malware activity, as well as fundamental issues on network vulnerability assessment. Moreover, the flexibility of the approach based on IMCs allows to apply our work beyond the problem of malware spreading, addressing a wide variety of dynamic interactions on networks. Our modeling effort is to be considered a first step in a rather novel research area that we expect to gain more and more relevance in the next future.

Reference:

[1] D. Moore, C. Shannon, and J. Brown, Code-red: a case study on the spread and victims of an internet worm, in Proceedings of the 2-th Internet Measurement Workshop (IMW), Marseille, France, November 2002.

[2] D. Moore, V. Paxson, and S. Savage, Inside the slammer worm, in IEEE Magazine of Security and Privacy, July 2003.

[3] CERT, CERT/CC advisories, http://www.cert.org/advisories/.

[4] P. R. Roberts, Zotob Arrest Breaks Credit Card Fraud Ring, http://www.eweek.com/article2/0,1895,1854162,00.asp.

[5] W32/MyDoom.B Virus, http://www.us-cert.gov/cas/techalerts/ TA04-028A.html.

[6] W32.Sircam.Worm@mm, http://www.symantec.com/avcenter/venc/data/w32.sircam.worm@mm.html.

Centralized data

center

User

Monitor

UserLogin

Monitoring

DataCollection

Detection

Distribution

DataCenter

userDetails

monitorDetails

authentication

dataCollection

getUserDetails()

acceptUsers()

provideAuthentication()

getDataCollection()

ClientMonitor

monitorDetails

ipAddress

portNumber

authentication

getAuthentication()

getMonitorDetails()

forwardToDataCenter()

User Login

userDetails

ipAddress

portNumber

getUserDetails()

getConnection()

dataTransfer()

DataDistribution

dataDistribution

ipAddress

collectIP()

dataDistribution()

WormDetection

userIP

monitorIP

findRatio

report

getUserIP()

getMonitorIP()

getWormRatio()

prepareReport()

DataCenterMonitorLogCollectionLogDistributionClient

Login

Monitoring

TrafficLog

DetectWorm

PrepareReport

Distribution

Monitor

DataCen

ter

LogColle

ction

LogDistri

bution

Client

1: Login

2: Monitoring

3: TrafficLog

4: DetectWorm

5: PrepareReport

6: Distribution

Start

DataCenter

Monitor

User

End