Proceeding of: Multi-agent systems and applications III : 3rd International Central and Eastern European Conference on Multi-Agent Systems, CEEMAS 2003 Prague, Czech Republic, June 16–18, 2003This paper proposes a system of agents that make predictions over the presence of intrusions. Some of the agents act as predictors implementing a given Intrusion Detection model, sniffing out the same traffic. An assessment agent weights the forecasts of such predictor agents, giving a final binary conclusion using a probabilistic model. These weights are continuously adapted according to the previous performance of each predictor agent. Other agent establishes if the prediction from the assessor agent was right or not, sending him back the results. This process is continually repeated and runs without human interaction. The effectiveness of our proposal is measured with the usual method applied in Intrusion Detection domain: Receiver Operating Characteristic curves (detection rate versus false alarm rate). Results of the adaptive agents applied to intrusion detection improve ROC curves as it is shown in this paper.Publicad

Carbó Rubiera, Javier Ignacio

Orfila Díaz-Pabon, Agustín

Ribagorda Garnacho, Arturo

Name not available

Adaptive agents applied to intrusion detection

Universidad Carlos III de Madrid e-Archivo

Adaptive Agents applied to Intrusion DetectionJavier Carbo´, Agust´ın Orfila, and Arturo RibagordaCarlos III University of Madrid,Computer Science Department,28911 Legane´s, Madrid, Spain{jcarbo,adiaz,arturo}@inf.uc3m.esAbstract. This paper proposes a system of agents that make predic-tions over the presence of intrusions. Some of the agents act as predic-tors implementing a given Intrusion Detection model, sniffing out thesame traffic. An assessment agent weights the forecasts of such predictoragents, giving a final binary conclusion using a probabilistic model. Theseweights are continuously adapted according to the previous performanceof each predictor agent. Other agent establishes if the prediction fromthe assessor agent was right or not, sending him back the results. Thisprocess is continually repeated and runs without human interaction. Theeffectiveness of our proposal is measured with the usual method appliedin Intrusion Detection domain: Receiver Operating Characteristic curves(detection rate versus false alarm rate). Results of the adaptive agentsapplied to intrusion detection improve ROC curves as it is shown in thispaper.1 IntroductionResearch on intrusion detection topic is very active. Several models have beenproposed and they are still evolving. There are also problems with the automa-tion of such intrusion detection models. In fact, nowadays these models are usedin close combination with a human supervisor (so called Site Security Officer)[14].One of the main questions about intrusion detection models or systems (inadvance, IDS) is to know how effective they are at detecting intrusions whileraising as fewer false alarms as possible. In order to evaluate the effectiveness[15] of IDS the use of Receiver Operating Characteristic (ROC) has been largelyproposed [16],[17],[15],[18]. ROC curves plot detection rate versus false alarmrate. A ROC curve shows the false alarm rate incurred by choosing a particulardetection rate. Some IDS can be tuned to accept a higher false alarm rate in orderto detect more attacks. IDS based on signature detection, however, produce abinary output of 1 for a declared attack and 0 for a normal session. That is whythey are represented by a single point on the ROC curve [17] (sometimes a linejoining this point to (0,0) and another line joining it to (1,1) is plotted). ROCtechnique has also been used in other fields where detection rates are objectof study such as signal detection, speaker identification, medical risk prediction2 Javier Carbo´ et al.and meteorology [19][20][21][22][23]. The bigger the area under the ROC curveis (AROC) the bigger the IDS effectiveness is [17].In this scope agents may play an important role: agents intend a completeautomation of complex processes acting in behalf of human users [11]. Severaldefinitions and approaches to agent term have led to some confusion. From theArtificial Intelligence point of view, agents are classified as reactive or delibera-tive according to the external or internal nature of the intelligent behavior. Inthis way, deliberative agents often accomplish the so called BDI paradigm [12]where knowledge is structured in three different levels of abstraction: beliefs,intentions and desires.Intelligent agents presumably can adapt decision making through the cooper-ation with other agents [10]. Communication between agents is usually modeledthrough human-like typed messages including performatives inspired in SpeechAct Theory (for instance, KQML [13]).Particularly, agents have been applied to this dominion in the past [2]. Forinstance, a methodology [3], an architecture [1] were proposed, and also some IDSwere implemented as agent systems using genetic algorithms [5], and neurofuzzycontrollers [4].2 The Analysis of Relative Operating CharacteristicIn order to asses the skill of a probabilistic prediction (based on several IDSagents) we use the Receiver Operating Characteristic (ROC) in a slightly dif-ferent way [23]. In our case study, ROC measures the success and false alarmrates of an ensemble; made by assuming an event E will occur if it is predictedwith a probability exceeding some specified probability threshold pt. The differ-ence with usual ROC approximation in intrusion detection is that the thresholdwe vary is not the false alarm threshold, but the probability of occurrence the”event”. Indirectly, this is like tuning the false alarm threshold of the probabilis-tic system.This definition is based on the notion that a prediction of an event E isassumed if E is predicted by at least a fraction p = pt of ensemble members,where the threshold pt is defined a priori.Let us consider first a deterministic (single model) prediction of E (eitherthat it will occur or that it will not occur). Over a sufficiently large sample of in-dependent predictions, we can form the prediction contingency matrix (Table 1)giving the frequency that E occurred or not, and whether it was predicted ornot.Based on these values, the hit rate(H) and false alarm rate (F ) for a deter-ministic prediction are given byH = δ/(β + δ).F = γ/(α+ γ).(1)Hit and false alarm rates for a probabilistic prediction can be redefined asfollows [24]. Suppose it is assumed that E will happen if the probability of theAdaptive Agents applied to Intrusion Detection 3Table 1. Prediction contingency matrixOcurrsNo YesPrediction No α βYes γ δprediction p is greater than pt (and will not if p < pt ). By varying pt between0 and 1 we can define H = H(pt), F = F (pt).The ROC curve is a plot of H(pt) against F (pt). A measure of skill is givenby the area under the ROC curve (AROC). A perfect deterministic forecast willhave AROC = 1, while a no skill-forecast for which the hit and false alarm ratesare equal, will have AROC = 0.5.In our case study the event E is ”an intrusion”. We will have different models(different IDS) that try to detect intrusions. These models must deal with thesame traffic and must not take any action on it.3 The role of AgentsThe proposed system has three different types of agents: predictor, assessor andmanager. In the scenario considered, there are several predictor agents and onlyone assessor agent and one manager agent. But it would make sense to use severalassessor agents rather than just one, since not every predictor would be askedby each assessor agent, and they would also adopt different weighting criteria.The main role of a predictor agent consists of suggesting if there is an in-trusion or not when an assessor agent asks him for a prediction. On the otherhand, major goal of assessor agents is giving proper weights to predictor agentsaccording to the previous level of success, and afterwards, making a binary de-cision based on such weighted references. Finally the manager agent calculatesH and F and, at last, it communicates the results to the assessor agent. Themanager agent knows if there was an intrusion or not because the experiment isdone under a training environment. The interactions between agents are typedas KQML messages. You can see an illustrative example of such interactions infigure Fig. 1.All of this agents adopt a BDI-like architecture, where abstract desires be-came in concrete goals when external perceptions would be sensed. Each of thesegoals has an associated generic plan composed of a sequence of atomic intentions.The intelligence of agents relies on how predictions are weighted according tothe success of previous predictions in order to make a suggestion. Neverthelessplans of predictor and manager agents show a straightforward behavior ratherthan the adaptive reasoning of assessor agents.Our proposal in this publication involves the use of the ratio H-F as a weightin the aggregated sum of predictor agents. Therefore, the reputation of certainpredictor agent would increase if the number of hit rates became higher, and4 Javier Carbo´ et al.ManagerEvaluatorEvaluatorPredictorPredictorPredictorPredictorPredictorPredictorPredictorPredictorPredictorPredictorPredictorPredictor   	      	       Predictions       	                	                     Fig. 1. Interactions between agentsif the number of false alarm rates remains in a low level. After updating theseweights with the last results, all of them are normalized, and the correspondingequations result:Weighti =(Hi − Fi + 1)÷ 2∑j(Hj − Fj + 1)÷ 2(2)Suggestion =∑iWeighti × Predictioni (3)4 Simulated ResultsThe main goal of this section is to explain how our model of agents works andto show the benefits of dynamic adaptation of agents to improve the decisionmaking. The level of success of this adaptive behavior is compared with agentsevaluating all the predictions with the same behavior (weight= 1 / number ofpredictors). No real data of traffic was able to be used in the experiments, buta possible simulation of such data were tested.Let us analyze the experimental setup: Many predictor agents (IDS models),eight in our example, are considered. The events to predict are the same eventE for all of them, and it consists of an intrusion. A possible scenario is the onein Table 2.An assessor agent suggests if there has been an intrusion or not dependingon how many models has predicted the event (Prob. of table 2) vs. a weightedsum. This suggestion is made through the comparison with a certain thresholdpt.Five different thresholds uniformly distributed were considered in both ex-periments (0.2, 0.4, 0.6, 0.8, 1.0). In this way, comparing the average sum andAdaptive Agents applied to Intrusion Detection 5Table 2. Possible measures on the event E (intrusion). 1 represents the predictoragent (IDS model) has launched an alert on the event E (predicts that E happens).0 represents it does not. If the event E is finally verified by the manager agent, it isrepresented by 1. If it does not it is represented by a 0Day 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15IDS1 0 0 0 0 0 0 1 1 1 1 0 0 1 1 0IDS2 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0IDS3 1 0 1 1 0 1 1 1 1 1 0 1 1 1 0IDS4 0 0 0 1 0 1 0 1 0 0 0 1 0 0 0IDS5 0 0 0 1 1 0 0 1 0 1 1 1 0 1 0IDS6 0 0 1 1 0 1 0 0 0 0 0 0 1 0 0IDS7 0 1 0 0 0 1 0 0 0 0 0 1 1 0 1IDS8 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0Prob 0.125 0.25 0.25 0.625 0.125 0.5 0.25 0.5 0.375 0.375 0.125 0.625 0.625 0.375 0.125Occurs 1 0 0 0 0 0 0 0 0 1 0 1 1 1 0Day 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30IDS1 1 0 0 0 0 0 1 1 1 1 0 0 1 1 0IDS2 1 0 0 0 0 1 0 1 1 0 0 0 1 0 0IDS3 1 0 1 1 0 1 1 1 1 1 0 1 1 1 0IDS4 1 0 0 1 0 1 0 1 0 0 0 1 0 0 0IDS5 1 0 0 1 1 1 0 1 0 1 1 1 1 1 0IDS6 1 0 1 1 0 1 0 1 0 0 0 0 1 0 0IDS7 1 1 0 0 0 1 0 1 0 0 0 1 1 0 1IDS8 0 1 0 1 0 1 0 0 0 0 0 1 1 0 0Prob 0.875 0.25 0.25 0.625 0.125 0.875 0.25 0.875 0.375 0.375 0.125 0.625 0.875 0.375 0.125Occurs 1 0 0 0 0 0 0 1 0 1 0 1 1 1 0threshold pt = 0.2 for instance, we can observe the suggestions made in table 3.Table 3. Ten days measures on the event E. Prediction (based on a threshold pt = 0.2)and what really happened is binary representedDay 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15pt = 0.2 0 1 1 1 0 1 1 1 1 1 0 1 1 1 0Occurs 1 0 0 0 0 0 0 0 0 1 0 1 1 1 0Day 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30pt = 0.2 1 1 1 1 0 1 1 1 1 1 0 1 1 1 0Occurs 1 0 0 0 0 0 0 1 0 1 0 1 1 1 06 Javier Carbo´ et al.With the lowest and the highest thresholds the hit rate (H), and the falsealarm rate (F) values are the same in both experiments:– For pt = 0.0 : H = 1.000 F = 1.000– For pt = 0.2 : H = 0.909 F = 0.684– For pt = 0.8 : H = 0.273 F = 0.053– For pt = 1.0 : H = 0.000 F = 0.000The suggestions from the assessor agent for pt = 0.6 are:– With a constant evaluation of predictor agents (average sum):H = 0.545 F = 0.158– With an adaptive evaluation of predictor agents (dynamic weights):H = 0.545 F = 0.107From these data we observe a similar number of hit rates and a slightly lowernumber of false alarms with an adaptive evaluation.At last, the suggestions from the assessor agent for pt = 0.4 are:– With a constant evaluation of predictor agents (average sum):H = 0.545 F = 0.263– With an adaptive evaluation of predictor agents (dynamic weights):H = 0.909 F = 0.421From these data, we can observe more hit rates in the adaptive evaluation thanin the constant evaluation, but it also appears to be more false alarms. So at firstglance it does not show clearly which alternative is better. But Fig. 2 representingthe AROC curves of both possible evaluations shows that an adaptive evaluationof agents include a greater area than the constant evaluation.Adaptive Agents applied to Intrusion Detection 7Aroc00,20,40,60,810 0,2 0,4 0,6 0,8 1F(Pt)H(Pt)Aroc without adaptive agents     	                 Fig. 2. AROC of adaptive agent approach vs. average one. Area corresponding to adap-tive agent model is bigger, showing a better effectiveness of the modelMathematically, the area of both alternatives is:– With a constant evaluation of predictor agents (average sum): 0.75– With an adaptive evaluation of predictor agents (dynamic weights): 0.81So an improvement of 7.25% is achieved with the adaptive behaviour of agentsproposed in this paper.5 Conclusions and Future WorkIn this paper, we have proposed a system of different types of agents cooper-ating in order to detect intrusions. One of these types implements IDS models,other evaluates the predictions from them, and finally a third kind of agent con-siders the suggestions and emulates the results with which future evaluationswill be more accurate. The dynamic weights involved in the adaptive evaluationperformed to generate a final suggestion from several different Intrusion Detec-tion models showed a better performance than classical approach based on theaverage sum of the predictions received.Furthermore, the multiagent system applied to this dominion, allow us dif-ferent future directions of research. For instance, the use of a fuzzy thresholdapplied over the average sum of predictions causes a relevant improvement in theoverall performance of Intrusion Detection task [6]. Such fuzzy threshold maybe used also as adapted dynamically in order to obtain even better results.In the future, we intend to use the architecture of agents proposed to computeand represent the reputation of predictor agents as a fuzzy set in the same waythat AFRAS [9]. In order to do such fuzzy evaluation we will use an analysis of8 Javier Carbo´ et al.the economical costs involved in the precautionary action taken by the manageragent. This reputation mechanism showed a fast convergence, while sensitivity tosudden changes avoids a high level of deception [8]. Finally, it could be interestingto try with real data, and also to test several assessor agents analyzing differenttraffic data.References1. Balasubramaniyan, J.S., Garcia J.O., Isacoff D., Spafford E., Zamboni D.,: An Ar-chitecture for Intrusion Detection using Autonomous Agents. Procs. of the 14thAnnual Computer Security Applications Conf., pp. 13-24. IEEE Computer Society,December 1998.2. Vigna G., Cassell B., Fayram D.,: An Intrusion Detection System for Aglets. 6thInt. Conf. on Mobile Agents, Barcelona, Spain, October 2002.3. Carver C., Hill J., Surdu J., Pooch U.,: A methodology for using Intelligent Agentsto provide Automated Intrusion Response. Procs. of the IEEE Systems, Man, andCybernetics Information Assurance and Security Workshop, West Point, NY, June6-7, 2000.4. Dasgupta, D., Brian H.,: Mobile Security Agents for Network Traffic Analysis. Procs.DARPA Information Survivability Conf. adn Exposition II, IEEE Society Press,Anaheim, California, June 2001.5. Crosbie, M., Spafford G.,: Active Defense of a Computer System using AutonomousAgents. Technical Report No. 95-008, Purdue University, U.S., June 1995.6. Orfila A., Carbo J., Ribagorda A.: Fuzzy logic on Decision Model for IDS. Procs.IEEE Int. Conf. on Fuzzy Systems, St. Louis, May 2003.7. Baldwin, J.F.,: A calculus for mass assignment in evidential reasoning. Advances inDempster-Shafer Theory of Evidence, M. Fedrizzi, J. Kacprzyk, R.R. Yager, eds.,John Wiley, 1992.8. Carbo, J., Molina J.M., Davila, J.,: Trust management through fuzzy reputation.Accepted for Int. Journal of Cooperative Information Systems, to appear.9. Carbo, J., Molina J.M., Davila J.,: A fuzzy model of reputation in multiagent system.Procs. 5th Int. Conf. on Autonomous Agents, Montreal, June 2001.10. Smith R.G., David R.,: Frameworks for cooperation in distributed problem solving.IEEE Trans. On Systems, Man and Cybernetics, vol. 11, number 1, pp.61-70, June1995.11. Maes, P.,: Agents that reduce work and information overload. Communications ofthe ACM, vol. 37, number 7, pp. 31-40, 1994.12. Rao, A.S., Georgeff, M.P.,: BDI-agents from theory to practice. Procs. 1st Int.Conf. on Multiagent Systems (ICMAS’95), San Francisco, June 1995.13. Finin, T., McKay R., Fritzson, R., McEntire R.,: KQML: an information andknowledge exchange protocol. Procs. Int. Conf. on Building and Sharing of VeryLarge-Scale Knowledge Bases, December 1993.14. Axelsson, S.,: Intrusion-detection systems: A taxonomy and survey. TechnicalReport 99-15, Department of Computer Engineering, Chalmers University ofTechnology,SE-41296, Goteborg, Sweden, March 2000.15. Axelsson, S.,: The base rate fallacy and its implications for the difficulty of intrusiondetection. In 6th ACM conference on computer and communications security.KentRidge Digital Labs, Singapore, 1-4 November 1999, pp. 1-7Adaptive Agents applied to Intrusion Detection 916. Lippman, R.P., Fried, D.J., Graf, I., Haines, J.W., Kendall, K.R., McClung, D.,Weber, D., Webster, S.E., Wyshhogrod, D., Cunningham, R.K, Zissman, M.A.:Evaluating Intrusion detection systems: the 1998 DARPA Off-line Intrusion Detec-tion Evaluation. Proceedings of the 2000 DARPA information survivality Conferenceand Exposition (DISCEX), Vol.2, IEEE Press, January 200017. Durst, R., Champion, T., Witten, B., Miller, E., Spagnolo, L. : Testing and eval-uating computer intrusion detection systems. Communications of the ACM, 42(7),1999, pp.53-6118. Gomez, J., Dasgupta, D.: Evolving Fuzzy Classifiers for Intrusion Detection. Pro-ceedings of the 2002 IEEE. Workshop on Information Assurance. United StatesMilitary Academy, West Point, NY June 200119. Swets, J.A: The Relative Operating Characteristic in Psychology. Sci-ence,182,1973,pp. 990-100020. Egan, J.P: Signal detection theory and ROC-analysis. Academic Press, 197521. Martin,A., Doddington,G., Kamm,T., Ordowski,M.,Przybocki,M.: The DETCurve in Assessment of Detection Task Performance. Proceedings EuroSpeech 4.1998, pp. 1895-1898.22. Lippmann, R.P., Shahian,D.M.:Coronary Artery Bypass Risk Prediction UsingNeural Networks. Annals of Thoracic Surgery, 63. 1997. pp. 1635-1643.23. Stanski, H.R., Wilson, L.J., Burrows, W.R. Survey of common verification methodsin meteorology. World Weather Report No. 8. World Meteorological Organization.Geneva.24. Palmer, T.N., Brankovic, C., and Richardson, D.S. A Probability and Decision-Model Analysis of PROVOST Seasonal Ensemble Integrations. Research Depart-ment. Technical Memorandum No.265. Nov 1998.25. Murphy,A.H. A new vector partition of the probability score. J. Appl. Meteor.1973.26. Katz, R.W., Murphy, A.H. Forecast value: prototype decision-making models. InEconomic value of weather and climate forecasts. Eds. Cambridge University Press.1997.27. Wenke, L., Wei, F., Miller, M., Stolfo. S., Zadok, E. Toward Cost-Sensitive Mod-eling for Intrusion Detection and Response. North Carolina State University. Com-puter Science

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