A Meta-Classification Model for Optimized ZBot Malware Prediction Using Learning Algorithms

Shanmugam Jagan, Ashish Ashish, Miroslav Mahdal (), Kenneth Ruth Isabels, Jyoti Dhanke, Parita Jain and Muniyandy Elangovan
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Shanmugam Jagan: Department of CSE, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai 600062, India
Ashish Ashish: Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram 522302, India
Miroslav Mahdal: Department of Control Systems and Instrumentation, Faculty of Mechanical Engineering, VSB-Technical University of Ostrava, 17. Listopadu 2172/15, 70800 Ostrava, Czech Republic
Kenneth Ruth Isabels: Department of Mathematics, Saveetha Engineering College (Autonomous), Chennai 600062, India
Jyoti Dhanke: Department of Engineering Science (Mathematics), Bharati Vidyapeeth’s College of Engineering, Pune 412115, India
Parita Jain: Department of CSE, KIET Group of Institutions, Ghaziabad 201206, India
Muniyandy Elangovan: Department of R&D, Bond Marine Consultancy, London EC1V2NX, UK

Mathematics, 2023, vol. 11, issue 13, 1-21

Abstract: Botnets pose a real threat to cybersecurity by facilitating criminal activities like malware distribution, attacks involving distributed denial of service, fraud, click fraud, phishing, and theft identification. The methods currently used for botnet detection are only appropriate for specific botnet commands and control protocols; they do not endorse botnet identification in early phases. Security guards have used honeypots successfully in several computer security defence systems. Honeypots are frequently utilised in botnet defence because they can draw botnet compromises, reveal spies in botnet membership, and deter attacker behaviour. Attackers who build and maintain botnets must devise ways to avoid honeypot traps. Machine learning methods support identification and inhibit bot threats to address the problems associated with botnet attacks. To choose the best features to feed as input to the machine learning classifiers to estimate the performance of botnet detection, a Kernel-based Ensemble Meta Classifier (KEMC) Strategy is suggested in this work. And particle swarm optimization (PSO) and genetic algorithm (GA) intelligent optimization algorithms are used to establish the ideal order. The model covered in this paper is employed to forecast Internet cyber security circumstances. The Binary Cross-Entropy (loss), the GA-PSO optimizer, the Softsign activation functions and ensembles were used in the experiment to produce the best results. The model succeeded because Forfileless malware, gathered from well-known datasets, achieved a total accuracy of 93.3% with a True Positive (TP) Range of 87.45% at zero False Positive (FP).

Keywords: honeypots; botnet; malware; soft sign; genetic algorithm; kernels and cyber threats (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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