A Pragmatic Optimal Approach for Detection of Cyber Attacks using Genetic Programming

摘要

Cyber-attacks are becoming an increasing threat to people and daily businesses regularly. Attackers have also been evolving their strategies and methods with time. Every attack carried out has the potential to exploit the system on a large scale. Various Artificial Intelligence (AI) algorithms are used to defend such vulnerabilities. This paper analyzes a novel attack and extracts attackers' intrusion scenarios. Evolutionary Computation Techniques have been remarkably used in the field of cybersecurity. This paper particularly discusses the Distributed Denial Of Service (DDoS) attack. The effect of this attack ranges from a disturbance of an elementary service to causing major threats to critical services. In recent times these attacks have become more intricate and carry a significant threat. Therefore, there is a necessity for an intelligent Intrusion Detection System (IDS) to recognize attacks. In this study, work is carried on the latest dataset called Modern DDoS. This paper comprises of comparing the results of six established classification techniques: Random Forest, Naive Bayes, Stochastic Gradient Descent, Decision Trees, Logistic Regression, and K-Nearest Neighbour (KNN) with the proposed Genetic Programming model. The results show that the proposed Genetic Programming model has better accuracy when compared to various existing methods.