Agent Based Efficient Anomaly Intrusion Detection System in Adhoc Networks

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  IACSIT International Journal of Engineering and Technology Vol. 2, No.1, February, 2010 ISSN: 1793-8236

Agent BasedSystem Efficient Intrusion Detection in Anomaly Adhoc networks. Abstract: Networks Netwo rks are protec protecte ted d using using many many firew firewall allss and enc encryp ryptio tion n software’s. But many of them are not sufficient and effective. effective. Most in intr trus usio ion n dete detect ctio ion n syst system emss for for mobi mobile le ad ho hocc netw networ orks ks are are focusing on either routing protocols or its efficiency, but it fails to address the security issues. Some of the nodes may be selfish, for  example, by not forwarding the packets to the destination, thereby savi sa ving ng th thee batt batter ery y powe power. r. Some Some othe others rs may may act act ma mali lici ciou ouss by la laun unch chin ing g secu securi rity ty at atta tack ckss li like ke deni denial al of serv servic icee or hack hack the the in info form rmat atio ion. n. The The ul ulti tima mate te goal goal of the the se secu curi rity ty solu soluti tion onss for  for  wire wi relless ess netw networ orks ks is to prov provid idee secur ecurit ity y serv serviices, ces, suc such as autthe au hen ntica ticati tion on,, conf onfid ideent ntia iallit ity y, inte ntegrit grity y, anony nonymi mitty, and availability, to mobile users. This paper incorporates agents and data mining techniques to prevent anomaly intrusion in mobile adhoc networks. Home agents present in each system collects the data from its own system and using data mining techniques to observed the local anomalies. The Mobile agents monitoring the neighboring nodes and collect the information from neighboring home ho me ag agen ents ts to dete determ rmin inee th thee corr correl elat atio ion n am amon ong g the the ob obse serv rved ed anomalous patterns before it will send the data. This system was able to stop all of the successful attacks in an adhoc networks and reduce the false alarm positives.

 

EXISTING SYSTEM In general, the normal behavior of a computing system can be characterized by observing its properties over time . The  problem of detecting anomalies (or intrusions) can be viewed as filtering non-permitted deviations of the characteristic properties in the monitored network system. This assumption is based on the fact that intruders’ activities in some way must be different from th thee norm normaal us useers rs’’ act ctiivi vittie iess . That That ass ssum umpt ptio ion n can lea lead to falsepositives when any new behavior is considered anomalous and causes detection failure when intrusive behavior closely matches normal behavior. Accordingly, one type of anomaly detection in use tod today ay is cal called led Profi Profile-B le-Base ased d Anom Anomaly aly Detec Detectio tion n which which focuses on characterizing the past behavior of individual users or  related groups of users and then detecting significant deviations. A  profile may consist of a set of parameters, so that deviation on just a single parameter may not be sufficient in itself to signal an alert. Unli Un like ke conv conven enti tion onal al in intr trus usio ion n dete detect ctio ion n sy syst stem emss (I (IDS DS), ), this this security system attempts to emulate mechanisms of the natural immune system using Java-based mobile software agents.

PROPOSED SYSTEM Mobile-agents Mobile-agen ts can be used used to build network network monitorin monitoring g Syst System em   pre prese sent nted ed is dyna dynami micc co conf nfig igur urab able le and and ex exte tens nsib ible le,, ac acti tive vely ly monito mon itors rs networ networks, ks, secure secure,, rob robust ust and has acc accept eptabl ablee resour resource ce usage This project surveys the risks connected with the use of  mobile agents, and security techniques available to protect mobile agents and their hosts. The inadequacies of the security techniques developed from the information fortress model are identified. They are the result of using a good model in an inappropriate context (i.e., a closed system model in a globally distributed networking com co mput putin ing g

bas base) e)..

Prob Probllems

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te tech chni niqu ques es in incl clud ude: e: 1) conf confli lict ctss betw betwee een n secu securi rity ty tech techni niqu ques es  protecting hosts and mobile agents, 2) inability to handle multiple collaborative mobile agents, and 3) emphasis on the credentials of  software instead of on the integrity of software to determine the level of trust. This proj This projec ectt desc descri ribe bess a mobi mobile le se secu curi rity ty agen agentt arch archit itec ectu ture rem m detect det ecting ing coordi coordinat nated ed and sop sophis histic ticate ated d attac attacks. ks. The app approa roach ch combines two anomaly detection methods by both profiling user   behavior and also correlating it to network statistical behavior. The idea is that an intruder can be differentiated from a normal user by his activity and its associated impact on the system resources. This agent-based intrusion detection system (IDS) . attempts to emulate mechanisms of the natural immune system by detecting anomalies in a distributed manner.

Modules: 1. Home agent Home agent is present in each system and it gathers information abou ab outt its its syst system em fr from om appl applic icat atio ion n laye layerr to rout routin ing g laye layer. r. Our  Our    pro propo pose sed d syst system em pr prov ovid ides es so solu luti tion on in thre threee tech techni niqu ques es.. 1. It monitors its own system and its environment dynamically. It uses classifier construction to find out the local anomaly. 2. Whenever  the node want to transfer the information from the node F to B. It  broadcast the message to E and A. Before it sends the message, it gathers the neighboring nodes (E &B) information using mobile agent. It calls the classifier rule to find out the attacks with help of  test train data. 3. It provides same type of solution through out the

 

Home Agent is present in the system and it monitors its own system continuously. If an attacker sends any packet to gather  information or broadcast through this system, it calls the classifier  construction to find out the attacks. If an attack has been made, it will filter the respective system from the global networks.

2. Mobile Agents module “The agent learns about its environment and actions to  be more effective. The agent must be able to communicate not only with the master agent at the host but with other  agen ag ents ts,, to too. o. Th Thro roug ugh h th this is comm commun unic icat atio ion, n, an ag agen entt ca can n collaborate with other agents in the intention to reach its goals”

3. Intrusion detection in Mobile Ad-Hoc Networks. A three-level hierarchical system for data collection, processing and an d tran transm smis issi sion on is desc descri ribe bed. d. Loca Locall ID IDS S (int (intru rusi sion on de dete tect ctio ion n systems) are attached to each node of the MANET, collecting raw data of network operation, and computing a local anomaly index measuring the mismatch between the current node operation and a  baseline normal operation We haveof detect intruder for for Four Level I. User Level II. II. Proce rocess ss Le Level vel II III. I. Pack Packet et Leve Levell IV.. Wo IV Worm rm or Tro Troja jan n Dete Detect ctio ion n

4. Abnormal behavior & Anomaly detection Module.

 

Anomaly detection describes the abnormal patterns of behavior, wher wh eree "abn "abnor orma mal" l" patt patter erns ns ar aree defi define ned d be befo fore reha hand nd.. Misu Misuse se dete de tect ctio ion n re reli lies es on th thee use use of spec specif ific ical ally ly kn know own n patt patter erns ns of  unauth una uthori orized zed behavi behavior. or. Thus Thus these these tec techni hnique quess rely rely on sniff sniffing ing   packets and using the sniffed packets for analysis. In order to realize these ID techniques the packets can be sniffed on each of  the end hosts. This is called as host intrusion detection (HID). It is al also so poss possib ible le to sn snif ifff th thes esee pack packet etss on ce cert rtai ain n pr pred edet eter ermi mine ned d mach ma chin ines es in th thee netw networ ork. k. Th This is is call called ed as ne netw twor ork k intr intrusi usion on detection (NID).

System Configuration  

System Configuration  

H/W System Configuration Processor

-

Pentium –III

Speed

-

1.1 Ghz

RAM

-

256 MB(min)

Hard Disk

- 20 GB

Floppy Drive

-

1.44 MB

Key Board

-

Standard Windows Keyboard

Mouse

-

Two or Three Button Mouse

Monitor

-

SVGA

 

Software Requirements :-

Language :

Java RMI, SWING

Deve De velo lopm pmen entt Tool Tool::

My Ecl Eclip ipse se 3.0 3.0

O/S

WIN2000/XP, TCP/IP

:

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