An Empirical Evaluation On Comparative Machine Learning Techniques For Detection of The Distributed Denial of Service (DDoS) Attacks
Open Access
- 17 May 2020
- journal article
- Published by Yayasan Ahmar Cendekia Indonesia in Journal of Applied Science, Engineering, Technology, and Education
- Vol. 2 (1), 18-27
- https://doi.org/10.35877/454ri.asci2192
Abstract
The advent of the Internet that aided the efficient sharing of resources. Also, it has introduced adversaries whom are today restlessly in their continued efforts at an effective, non-detectable means to invade secure systems, either for fun or personal gains. They achieve these feats via the use of malware, which is both on the rise, wreaks havoc alongside causing loads of financial losses to users. With the upsurge to counter these escapades, users and businesses today seek means to detect these evolving behavior and pattern by these adversaries. It is also to worthy of note that adversaries have also evolved, changing their own structure to make signature detection somewhat unreliable and anomaly detection tedious to network administrators. Our study investigates the detection of the distributed denial of service (DDoS) attacks using machine learning techniques. Results shows that though evolutionary models have been successfully implemented in the detection DDoS, the search for optima is an inconclusive and continuous task. That no one method yields a better optima than hybrids. That with hybrids, users must adequately resolve the issues of data conflicts arising from the dataset to be used, conflict from the adapted statistical methods arising from data encoding, and conflicts in parameter selection to avoid model overtraining, over-fitting and over-parameterization.Keywords
This publication has 10 references indexed in Scilit:
- Memetic algorithm for short messaging service spam filter using text normalization and semantic approachInternational Journal of Informatics and Communication Technology (IJ-ICT), 2020
- Signature-Based Malware Detection Using Approximate Boyer Moore String Matching AlgorithmInternational Journal of Mathematical Sciences and Computing, 2019
- Evolutionary Memetic Models for Malware Intrusion Detection: A Comparative Quest for Computational Solution and ConvergenceInternational Journal of Computer Applications, 2018
- Mitigating Social Engineering Menace in Nigerian UniversitiesJournal of Computer Sciences and Applications, 2018
- Malware Propagation on Social Time Varying Networks: A Comparative Study of Machine Learning FrameworksInternational Journal of Modern Education and Computer Science (ijmecs), 2014
- Microarray data feature selection using hybrid genetic algorithm simulated annealingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2012
- Autonomous rule creation for intrusion detectionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2011
- Hybrid Intrusion Detection Systems (HIDS) using Fuzzy LogicPublished by IntechOpen ,2011
- ID-SOMGA: A Self Organising Migrating Genetic Algorithm-Based Solution for Intrusion DetectionComputer and Information Science, 2010
- Anomaly-based network intrusion detection: Techniques, systems and challengesComputers & Security, 2009