IIARD International Institute of Academic Research and Development

INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND MATHEMATICAL THEORY (IJCSMT )

E-ISSN 2545-5699
P-ISSN 2695-1924
VOL. 9 NO. 4 2023

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Real-Time Detection for Network Systems Malware Attacks and Prevention Attack Using Artificial Immmune System Algorithm

Ubochi C. I., Amanze B.C., Igbe C.M., Agbakwuru A.O., Agbasonu V.C

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Abstract

Malware threats detection and prevention has improved with age, but this improvement seems to be a continuous process as advancement in the technology opens the door with a loop-hole for intruders every time. To develop a system that will intelligently provide real-time detection of network systems malware attacks and prevent the attack using Artificial Immune System (AIS) algorithm and machine learning techniques. During the literature review, it was observed that recently, many researchers were and is still performing their experiment to increase the effectiveness of intrusion prevention in standard datasets. When the amount of data in the network started to grow, this led to a significant challenge in malware threats detection. Therefore, there was need of dealing with these huge datasets. Many IDS still lack the ability to detect all kinds of new attacks in the network, so researchers are inclined towards modeling the normal instances to increase their system effectiveness. Anomaly detection based on outlier has always been a challenging task for real-time detection. In this research work, artificial immune system and machine learning was used to detect the malware threats on the data network. In summarizing the whole research, the work mainly focused on malware threat detection, packets analysis and modeling the normal instances in presence of malicious attack information. Our approach overcomes the drawbacks of one associated with the rule-based approaches and is efficient. One has discussed about the effectiveness of this work on basis on performance metrics, and accuracy. Thus, this work provides a practical solution for construction of better malware threats detection and prevention system based on artificial immune system and machine learning

keywords:

IDS, Firewall, Data Network, AIS

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