Securing Against Zero-Day Attacks: A Machine Learning Approach for Classification and Organizations’ Perception of its Impact

Abstract

Zero-day malware is a type of malware that exploits system vulnerabilities before it is detected and sealed. This type of malware is a significant threat to enterprise cybersecurity and has tremendous impact on organizations’ performance, as it can spread widely before organizations can clamp down on the threat. Unfortunately, exploit developers can attack system’s vulnerabilities at a pace that is faster than defensive patches. In this research, classification of zero-day attack was carried out. Exploratory Data Analysis (EDA) on malware zero data was conducted. Then feature selection was carried out using Principal Component Analysis (PCA) for the selection of the most important features in the dataset after which a Random Forest (RF) Algorithm was adopted for the classification of zero-day attack. The impact of such attacks was also analyzed, and results were evaluated using confusion matrix and an accuracy of 95% in the classification of zero-day attack with a class error of 3.8% was obtained. A survey of the perception of the potential impacts of these attacks on organization was also carried out. These results indicate efficiency of machine learning algorithm in the classification of attacks as zero-day malware attacks or not. The research also offered pragmatic insights into the perception by organizations of its potential negative impacts and their eagerness to embrace and prioritize proffered cyber security solution(s) to avoid such attacks in order to avert undesirable consequences.

Keywords

• zero-day attack, machine learning, data security, cyber security, organizations performance