Detection of Unauthorized Actions in Networks Using Wavelet Analysis

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dc.contributor.authorHrynchenko, Pavlo
dc.date.accessioned2023-11-22T16:46:22Z
dc.date.available2023-11-22T16:46:22Z
dc.date.issued2023
dc.description.abstractSignal processing techniques are used to analyze and detect network anomalies because of their ability to detect new and unknown intrusions. The paper proposes a method of modeling network signals for the detection of network anomalies, which combines wavelet approximation and the theory of system identification. To characterize the behavior of network traffic, fifteen functions are provided, which are used as input signals within the system. At the same time, it is assumed that security violations within the network can be detected by checking abnormal patterns of system functioning according to audit data. Despite the fact that machine learning methods have achieved significant results in detecting network anomalies, they still face the difficulty of using the implemented algorithms, in the presence of differences in the behavior of the training data and test data, which in turn leads to inefficient performance of the algorithms. This effect is exacerbated by the limitation of algorithms to detect previously unknown types of attacks due to the large number of false positives. The paper develops a new method of modeling network signals for detecting anomalies in networks using wavelet analysis. In particular, the general architecture of the approach consists of three components: feature analysis, modeling of normal network traffic based on wavelet approximation and prediction using ARX model, intrusion or non-intrusion decision making The result is evaluated using the DARPA intrusion detection dataset, which performs a comprehensive analysis of the intrusions in the dataset. Evaluation results show that this approach provides a high level of detection of both instances and types of attacks.uk
dc.format.pagerangePp. 40-46uk
dc.identifier.citationHrynchenko, P. Detection of Unauthorized Actions in Networks Using Wavelet Analysis / Pavlo Hrynchenko // Theoretical and Applied Cybersecurity : scientific journal. – 2023. – Vol. 5, Iss. 2. – Pp. 40–46. – Bibliogr. 9 ref.uk
dc.identifier.doihttps://doi.org/10.20535/tacs.2664-29132023.2.277621
dc.identifier.urihttps://ela.kpi.ua/handle/123456789/62405
dc.language.isoenuk
dc.publisherIgor Sikorsky Kyiv Polytechnic Instituteuk
dc.publisher.placeKyivuk
dc.relation.ispartofTheoretical and Applied Cybersecurity: scientific journal, Vol. 5, No. 2uk
dc.subjectWavelet analysisuk
dc.subjectnetwork intrusion detection systemuk
dc.subjectnetwork securityuk
dc.subject.udc004.732uk
dc.titleDetection of Unauthorized Actions in Networks Using Wavelet Analysisuk
dc.title.alternativeDetection of Unauthorized Actions in Networks Using Wavelet Analysisuk
dc.typeArticleuk

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Theoretical and Applied Cybersecurity: scientific journal, Vol. 5, No. 2