Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Optimization of Intrusion Detection Systems Determined by Ameliorated HNADAM-SGD Algorithm

Version 1 : Received: 18 December 2021 / Approved: 21 December 2021 / Online: 21 December 2021 (11:45:39 CET)

How to cite: Shyla, S.; Bhatnagar, V.; Bali, V.; Bali, S. Optimization of Intrusion Detection Systems Determined by Ameliorated HNADAM-SGD Algorithm. Preprints 2021, 2021120323 (doi: 10.20944/preprints202112.0323.v1). Shyla, S.; Bhatnagar, V.; Bali, V.; Bali, S. Optimization of Intrusion Detection Systems Determined by Ameliorated HNADAM-SGD Algorithm. Preprints 2021, 2021120323 (doi: 10.20944/preprints202112.0323.v1).

Abstract

A single Information security is of pivotal concern for consistently streaming information over the widespread internetwork. The bottleneck flow of incoming and outgoing data traffic introduces the issue of malicious activities taken place by intruders, hackers and attackers in the form of authenticity desecration, gridlocking data traffic, vandalizing data and crashing the established network. The issue of emerging suspicious activities is managed by the domain of Intrusion Detection Systems (IDS). The IDS consistently monitors the network for identifica-tion of suspicious activities and generates alarm and indication in presence of malicious threats and worms. The performance of IDS is improved by using different signature based machine learning algorithms. In this paper, the performance of IDS model is determined using hybridization of nestrov-accelerated adaptive moment estimation –stochastic gradient descent (HNADAM-SDG) algorithm. The performance of the algorithm is compared with other classi-fication algorithms as logistic regression, ridge classifier and ensemble algorithm by adapting feature selection and optimization techniques

Keywords

Intrusion Detection System (IDS); HNADAM-SDG(Hybrid Nestrov-Accelerated Adaptive Moment Estimation –Stochastic Gradient Descent); Network-based Intrusion Detection System (NIDS); Host-based Intrusion Detection System (HIDS); Signature-based Intrusion Detection System (SIDS); Anomaly-based Intrusion Detection System (AIDS); Algorithms; Machine Learning.

Subject

MATHEMATICS & COMPUTER SCIENCE, Probability and Statistics

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