A robust intelligent zero-day cyber-attack detection technique

With the introduction of the Internet to the mainstream like e-commerce, online banking, health system and other day-to-day essentials, risk of being exposed to various are increasing exponentially. Zero-day attack(s) targeting unknown vulnerabilities of a software or system opens up further research direction in the field of cyber-attacks. Existing approaches either uses ML/DNN or anomaly-based approach to protect against these attacks. Detecting zero-day attacks through these techniques miss several parameters like frequency of particular byte streams in network traffic and their correlation. Covering attacks that produce lower traffic is difficult through neural network models because it requires higher traffic for correct prediction. This paper proposes a novel robust and intelligent cyber-attack detection model to cover the issues mentioned above using the concept of heavy-hitter and graph technique to detect zero-day attacks. The proposed work consists of two phases (a) Signature generation and (b) Evaluation phase. This model evaluates the performance using generated signatures at the training phase. The result analysis of the proposed zero-day attack detection shows higher performance for accuracy of 91.33% for the binary classification and accuracy of 90.35% for multi-class classification on real-time attack data. The performance against benchmark data set CICIDS18 shows a promising result of 91.62% for binary-class classification on this model. Thus, the proposed approach shows an encouraging result to detect zero-day attacks.