A Comparison of Neural Projection Techniques Applied to Intrusion Detection Systems

Herrero, Álvaro; Corchado, Emilio; Gastaldo, Paolo; Zunino, Rodolfo

doi:10.1007/978-3-540-73007-1_138

Cited by 6 publications

(4 citation statements)

References 19 publications

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“…The effectiveness of MOVICAB-IDS in facing some anomalous situations has been widely demonstrated in previous works [10], [15], [16]. The projections obtained [by CMLHL overcome that obtained by Principal Component Analysis (PCA) [10], Maximum Likelihood Hebbian Learning [28] and Auto Associative Back Propagation Networks [28]. Figures 4 and 5 show a comparison of projections obtained by CMLHL ( Fig.…”

Section: Results Conclusion and Future Workmentioning

confidence: 72%

Hybrid Multi Agent-Neural Network Intrusion Detection with Mobile Visualization

Herrero

Corchado

Pellicer

et al. 2007

Advances in Soft Computing

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A multiagent system that incorporates an Artificial Neural Networks based Intrusion Detection System (IDS) has been defined to guaranty an efficient computer network security architecture. The proposed system facilitates the intrusion detection in dynamic networks. This paper presents the structure of the Mobile Visualization Connectionist Agent-Based IDS, more flexible and adaptable. The proposed improvement of the system in this paper includes deliberative agents that use the artificial neural network to identify intrusions in computer networks. The agent based system has been probed through anomalous situations related to the Simple Network Management Protocol.

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Section: Results Conclusion and Future Workmentioning

confidence: 72%

Hybrid Multi Agent-Neural Network Intrusion Detection with Mobile Visualization

Herrero

Corchado

Pellicer

et al. 2007

Advances in Soft Computing

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“…The reason for these difficulties is the inconsistency in the verification studies and in the presentation of obtained results. In practice, various reference data sets are used, each differing in size and type of data (Greenberg 1988;Kdd cup 1999;Lane 1999), user's command line calls, system calls (Borah et al 2011;Estrada et al 2009) or network traffic data (Corchado et al 2005;Herrero et al 2007). Finally, authors' own data bases, which are not publicly available, are frequently used (Sodiya et al 2011;Wespi et al 1999).…”

Section: Critical Remarksmentioning

confidence: 99%

Fuzzy approach for intrusion detection based on user’s commands

2015

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The article concerns the problem of detecting masqueraders in computer systems. A masquerader in a computer system is an intruder who pretends to be a legitimate user in order to gain access to protected resources. The article presents an intrusion detection method based on a fuzzy approach. Two types of user's activity profiles are proposed along with the corresponding data structures. The solution analyzes the activity of the computer user in a relatively short period of time, building a user's profile. The profile is based on the most recent activity of the user, therefore, it is named the local profile. Further analysis involves creating a more general structure based on a defined number of local profiles of one user, called the fuzzy profile. It represents a generalized behavior of the computer system user. The fuzzy profiles are used directly to detect abnormalities in users' behavior, and thus possible intrusions. The proposed solution is prepared to be able to create user's profiles based on any countable features derived from user's actions in computer system (i.e., used commands, mouse and keyboard data, requested network resources). The presented method was tested using one of the commonly available standard intrusion data sets containing command names executed by users of a Unix system. Therefore, the obtained results can be compared with other approaches. The results of the experiments Communicated by V. Loia. have shown that the method presented in this article is comparable with the best intrusion detection methods, tested with the same data set, in the matter of the obtained results. The proposed solution is characterized by a very low computational complexity, which has been confirmed by experimental results.

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“…To probe the effectiveness of the chosen projection model (See Section 4.3), it has been compared with other projection methods for data visualization, such as Principal Component Analysis (PCA) [17] and MLHL [35] as detailed in Section 6.1.…”

Section: <Figure 4 Goes Here>mentioning

confidence: 99%

MOVIH-IDS: A mobile-visualization hybrid intrusion detection system

et al. 2009

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Abstract.A novel hybrid artificial intelligent system for Intrusion Detection, called MOVIH-IDS, is presented in this study. A hybrid model built by means of a multiagent system that incorporates an unsupervised connectionist Intrusion Detection System (IDS) has been defined to guaranty an efficient computer network security architecture. This hybrid IDS facilitates the intrusion detection in dynamic networks, in a more flexible and adaptable manner. The proposed improvement of the system in this paper includes deliberative agents characterized by the use of an unsupervised connectionist model to identify intrusions in computer networks. This hybrid IDS has been probed through several real anomalous situations related to the Simple Network Management Protocol as it is potentially dangerous. Experimental results probed the successful detection of such attacks through MOVIH-IDS.

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A Comparison of Neural Projection Techniques Applied to Intrusion Detection Systems

Cited by 6 publications

References 19 publications

Hybrid Multi Agent-Neural Network Intrusion Detection with Mobile Visualization

Hybrid Multi Agent-Neural Network Intrusion Detection with Mobile Visualization

Fuzzy approach for intrusion detection based on user’s commands

MOVIH-IDS: A mobile-visualization hybrid intrusion detection system

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