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Article

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

This version is not peer-reviewed.

Submitted:

18 December 2021

Posted:

21 December 2021

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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: 
Computer Science and Mathematics  -   Probability and Statistics
Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.

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