A convolutional neural network-based malware analysis, intrusion detection, and prevention schema

Roheen Qamar; Baqar Ali Zardari; Aijaz Ahmed Arain; Asadullah Burdi; Dr. Kelash Kanwar; Engr. Fayyaz Ahmed Memon

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A convolutional neural network-based malware analysis, intrusion detection, and prevention schema

Author(s)

Roheen Qamar Department of Computer Science, Quaid-e-Awam University of Engineering, Science and Technology, Nawabshah, Pakistan

Baqar Ali Zardari Department of Information Technology, Quaid-e-Awam University of Engineering, Science and Technology, Nawabshah, Pakistan

Aijaz Ahmed Arain Department of Computer Science, Quaid-e-Awam University of Engineering, Science and Technology, Nawabshah, Pakistan

Asadullah Burdi Department of Electronic Engineering, Quaid-e-Awam University of Engineering, Science and Technology, Nawabshah, Pakistan

Dr. Kelash Kanwar Department of Electronic Engineering, Quaid-e-Awam University of Engineering, Science and Technology, Nawabshah, Pakistan

Engr. Fayyaz Ahmed Memon Department of computer systems engineering Quaid-e-Awam University of Engineering, Science and Technology, Nawabshah, Pakistan

Abstract

This paper explores distributed denial of service (DDoS) attacks, their current threat level, and intrusion detection systems (IDS), which are one of key techniques for mitigating them. It focuses on the problems and issues that IDS systems encounter while detecting DDoS attacks, as well as the difficulties and obstacles that they face nowadays when integrating with artificial intelligence systems. These ID systems enable the automatic and realtime identification of harmful threats. However, the network requires a highly sophisticated security solution due to the frequency with which malicious threats emerge and change. A significant amount of research is required to create an intelligent and trustworthy identification system for research purposes; numerous ID datasets are freely accessible. Due to the rapid evolution of attack detection mechanisms and the complexity of malicious attacks, publicly available Identification databases must be completely changed. on a regular basis. Due to the ever-evolving attack detection mechanism and the complexity of malicious attacks, publicly available ID datasets must frequently be modified. A Convolutional Neural Network (CNN) network was trained using four distinct training algorithms. The CICDDoS2019 dataset, which contains the most recent DDoS attack types created in CICDDoS2019, was tested, According to the analysis; the "Gradient Descent with Momentum Backpropagation" algorithm could be trained quickly. Network data attacks were correctly detected 93.1 percent of the time. The results indicate that The Convolutional Neural Network is able to successfully defend against DDoS attacks detection by using intrusion detection systems IDS, as evidenced by the high accuracy values obtained.

Publication Details

Page(s) 8-18

DOI DOI not available

Published Journal: University of Sindh Journal of Information and Communication Technology, Volume: 6, Issue: 4, Year: 2022

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Keywords

Convolutional Neural Network, Intrusion Detection System, Artificial Neural Network (ANN), Trainrp, Traingdm, Traincgf, CICDDoS2019 dataset, Distributed DDoS (Denial of Service) Attacks, Traingda

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