Debar H., Becker M., and Siboni D. (1992) A Neural Network Component for an Intrusion Detection System, in Proceedings, IEEE Symposium on Research in Computer Security and Privacy.  Home/Search   Document Not in Database   Summary   Related Articles   Check

.... between the successive keystrokes 16 while typing a known sequence of characters is suggested using multilayer neural network system is suggested in [72] and that using fuzzy algorithms are used in [73] Neural network based intrusion detection schemes have been developed by few researchers [69] that predicts the next command of a user and if the intrusion is likely to take place, that is reported to the mangers and measures are taken to stop the same. But carryout this task for each of the commands of each of the user is definitely a laborious job and kills major portion of the system ....

H. Debar, M. Becker and D. Siboni, A Neural Network Component for an Intrusion Detection System, in : Proc. IEEE Symp. on Research in Computer Security and Privacy, pp.240-250, 1992.

....a great deal of interest in the application of machine learning techniques to automate the process of learning the patterns. Examples include the Time based Inductive Machine (TIM) for intrusion detection [3] that learns sequential patterns and neural network based intrusion detection systems [4]. More recently, techniques from the data mining area (mining of association rules and frequency episodes) have been used to mine normal patterns from audit data [5, 10, 15] Problems are encountered, however, if one derives rules that are directly dependent on audit data [6] An intrusion that ....

Debar, H., M. Becker, and D. Siboni. 1992. A neural network component for an intrusion detection system. In Proceedings of 1992 IEEE computer society symposium on research in security and privacy held in Oakland, California, May 4-6, 1992, by IEEE Computer Society, 240-50. Los Alamitos, CA: IEEE Computer Society Press.

....for intrusion detection in order for the IDS to achieve maximal performance. Since most of the intrusions can be uncovered by examining patterns of user activities, many intrusion detection systems have been built by utilizing the recognized attack and misuse patterns to develop learning machines [1,2,3,4,5,6,7,8,9]. In our earlier work, support vector machines (SVMs) are found to be superior to neural networks in many important respects of intrusion detection [10,11,12] so we will illustrate feature ranking use SVMs. The data we used in our experiments originated from MIT s Lincoln Lab. It was developed ....

Debar H, Becke M, Siboni D (1992) A Neural Network Component for an Intrusion Detection System. Proceedings of the IEEE Computer Society Symposium on Research in Security and Privacy.

....tool that is capable of (mostly) real time intrusion detection is the goal of the researchers in IDSs. Various artificial intelligence techniques have been utilized to automate the intrusion detection process to reduce human intervention; several such techniques include neural networks [3,4,5,6,7], and machine learning [8] Several data mining techniques have been introduced to identify key features or parameters that define intrusions [9,10,11,12] In this paper, we explore Multivariate Adaptive Regression Splines (MARS) SVMs and neural networks, to perform intrusion detection based on ....

Debar H., Becke M., Siboni D. (1992) "A Neural Network Component for an Intrusion Detection System," Proceedings of the IEEE Computer Society Symposium on Research in Security and Privacy.

....The strength of these methods lies in their ability to differentiate normal user behaviour, anomalous acceptable behaviour, and intrusive behaviour. Techniques used for constructing models include statistical measures (static or adaptive) Anderson95] expert systems [Frank92] neural networks [Debar92], and user behaviour patterning [Lane97] Any observed behaviour is compared to known patterns or expected behaviour large deviations are noted as suspicious. Few commercial systems currently use this approach systems using these methods generally stem from academic projects (e.g. IDES, ....

H. Debar, M. Becker, D.Siboni "A Neural Network Component for an Intrusion Detection System", Proc. IEEE Symposium on Research in Computer Security and Privacy, 1992.

....which is itself a problem of great interest in building models based on experimental data. Since most of the intrusions can be uncovered by examining patterns of user activities, many IDSs have been built by utilizing the recognized attack and misuse patterns to develop learning machines [3,4,5,6,7,8,9,10,11]. In our recent work, SVMs are found to be superior to ANNs in many important respects of intrusion detection [12,13,14] we will concentrate on SVMs and briefly summarize the results of ANNs. The data we used in our experiments originated from MIT s Lincoln Lab. It was developed for intrusion ....

Debar H., Becke M., Siboni D. (1992) "A Neural Network Component for an Intrusion Detection System," Proceedings of the IEEE Computer Society Symposium on Research in Security and Privacy.

.... At present research on advanced IDSs based on learning by example paradigms is at an early stage, so that a number of issues should be solved in order to be used in operational environments [3] However the few works in the literature showed promising results, thus calling for further research [5 11]. In this paper, an approach to intrusion detection in computer networks based on the ensemble learning paradigm is proposed [12] Each member of the ensemble is trained on a distinct feature representation of patterns, then the results of the ensemble members are combined. This approach is ....

....early years of IDS development. In particular the application of neural networks for IDSs has been investigated by a number of researchers. Neural networks provide a solution to the problem of modelling the users behavior in anomaly detection because they do not require any explicit user model [5 7]. Neural networks for intrusion detection have first been introduced as an alternative to statistical techniques in the IDES intrusion detection expert system to model users behavior [5] In particular the typical sequence of commands executed by each users is learned. Similar approaches have ....

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H. Debar, M. Becker, D. Siboni, "A Neural Network Component for an Intrusion Detection System", Proc. of the IEEE Symp. on Research in Security and Privacy, Oakland, CA, USA, 1992, pp. 240-250.

.... of IDES, the reader is referred to [12] Two example intrusion detection implementations that employ rule based anomaly detection are Wisdom and Sense (W S) 34] and the Time based Inductive Machine (TIM) approach [3] Neural network based anomaly detection has also been proposed in recent work [4], 20] Anomaly detection is not without limitations. In many environments, it may be difficult to establish behavior patterns for users. For example, in sporadic user environments establishing profiles of normal user behavior would be difficult. This leads to a potentially large number of false ....

H. Debar, M. Becker and D. Siboni, "A Neural Network Compo- nent for an Intrusion Detection System," Proceedings of the IEEE Symposium on Research in Security and Privacy, Oakland, CA, pp. 240-258, May 1992.

....behaviour is not new and several Artificial 1 The term is used in its broad commercial context, rather than the logical unit of work with ACID properties defined in Transaction Processing. Intelligence techniques have been employed to address it. The term Classification refers to techniques [3, 9, 12, 13, 15, 20, 21] which derive some patterns of normal activity within a specific domain and then distinguish data into normal or exceptional based on the set of known patterns. Usually, those data have been subjected to a Data Reduction preprocessing [3, 6, 15, 17, 20] Data Reduction techniques aim to analyse a ....

....Classification refers to techniques [3, 9, 12, 13, 15, 20, 21] which derive some patterns of normal activity within a specific domain and then distinguish data into normal or exceptional based on the set of known patterns. Usually, those data have been subjected to a Data Reduction preprocessing [3, 6, 15, 17, 20]. Data Reduction techniques aim to analyse a collection of data, identify and extract only those data elements that are considered significant. As noted in [18] financial institutions rely on customized fraud detection systems. These systems have been developed by employing machine learning and ....

Debar, H. and Becker, M. and Siboni, D., "A Neural Network Component for an Intrusion Detection System", Proceedings IEEE Symposium on Research in Computer Security and Privacy, 1992.

....the above reasons, none of the approaches can be considered better than the others. Rather, they complement each other since each of them can be applied to determine specific kinds of violations. Other approaches to intrusion detection and audit controls are possible. For instance, neural network [14, 23], state based [27] or model based [24] approaches have been proposed as a way to describe violations in terms of events or observables in the system. Other approaches have proposed the use of specific techniques as a protection against s specific attacks. For instance, the keystroke latency ....

H. Debar, M. Becker, and D. Siboni. A neural network component for an intrusion detection system. In Proc. IEEE Symposium on Security and Privacy, pages 240--250, Oakland, CA, 1992.

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Debar H., Becker M., and Siboni D. (1992) A Neural Network Component for an Intrusion Detection System, in Proceedings, IEEE Symposium on Research in Computer Security and Privacy.

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Debar, H., Becker, M., and Siboni, D. A Neural Network Component for an Intrusion Detection System. IEEE Computer Society Symposium on Research in Security and Privacy, Los Alamitos, CA, pp. 240--250, Oakland, CA, May 1992.

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H. Debar, M. Becke, D. Siboni, A neural network component for an intrusion detection system, in: Proceedings of the IEEE Computer Society Symposium on Research in Security and Privacy, 1992, pp. 240--250.

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M. Debar, D. Becke, and A. Siboni. "A Neural Network Component for an Intrusion Detection System". Proceedings of the IEEE Computer Society Symposium on Research in Security and Privacy, 1992.

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H. Debar, M. Becke, D. Siboni. A Neural Network Component for an Intrusion Detection System. Proceedings of the IEEE Computer Society Symposium on Research in Security and Privacy, 1992.

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Debar H., Becke M., Siboni D., 1992a. A Neural Network Component for an Intrusion Detection System. Proceedings of the IEEE Computer Society Symposium on Research in Security and Privacy.

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M Debar, DBecke, and A Siboni. "A Neural Network Component for an Intrusion Detection System". Proceedings of the IEEE Computer Society Symposium on Research in Security and Privacy, 1992.

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Debar H., Becke B., Siboni D. (1992) "A Neural Network Component for an Intrusion Detection System," Proceedings of the IEEE Computer Society Symposium on Research in Security and Privacy, pp. 240-250.

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Debar H., Becker M., Siboni D., "A Neural Network Component for an Intrusion Detection System". Proceedings of the 1992.

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Herve Debar, Monique Becker, and Didier Siboni. A Neural Network Component for an Intrusion Detection System. In Proceedings of the 1992 IEEE Symposium on Research in Security and Privacy, Oakland, CA, May 1992.

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H. Debar, M. Becker, and D. Siboni, "A Neural Network Component for an Intrusion Detection System," in Proc. of IEEE Computer Society Symposium on Research in Security and Privacy, 1992.

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H. Debar, M. Becker, and D. Siboni. A neural network component for an intrusion detection system. In IEEE Computer Society Symposium on Research on Security and Privacy, pages 240--250, May 1992.

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H. Debar, M. Becke, and D. Siboni. A neural network component for an intrusion detection system. In Proeedings of the IEEE Computer Society Symposium on Research in Security and Privacy, 1992.

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H. Debar, M. Becker and D. Siboni, A Neural Network Component for an Intrusion Detection System, in : Proceedings of IEEE Symposium on Research in Computer Security and Privacy, pp.240-250, 1992.

No context found.

Debar, H., Becker, M., and Siboni, D. (1992). A Neural Network Component for an Intrusion Detection System. In Proceedings of the IEEE Symposium of Research in Computer Security and Privacy, pages 240250.

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