An Enhanced Classification Model for Likelihood of Zero-Day Attack Detection and Estimation

Abstract

The growing threat to sensitive information stored in computer systems and devices is becoming alarming. This is as a result of the proliferation of different malware created on a daily basis to cause zero-day attacks. Most of the malware whose signatures are known can easily be detected and blocked, however, the unknown malwares are the most dangerous. In this paper a zero-day vulnerability model based on deep-reinforcement learning is presented. The technique employs a Monte Carlo Based Pareto Rule (Deep-RL-MCB-PR) approach that exploits a reward learning and training feature with sparse feature generation and adaptive multi-layered recurrent prediction for the detection and subsequent mitigation of zero-day threats. The new model has been applied to the Kyoto benchmark datasets for intrusion detection systems, and compared to an existing system, that uses a multi-layer protection and a rule-based ranking (RBK) approach to detect a zero-day attack likelihood. Experiments were performed using the dataset, and simulation results show that the Deep-RL-MCB-PR technique when measured with the classification accuracy metrics, produced about 67.77%. The dataset was further magnified, and the result of classification accuracy showed about 75.84%. These results account for a better error response when compared to the RBK technique.

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

“…This attack cannot be discovered until it happens, that is the most dreaded part of it. One best way to prevent this kind of cyber-attack is to purchase software products that have passed through series of tests and confirmations from trusted software and programmers should ensure every loophole is sealed in order to avoid attackers from exploiting any vulnerable loophole in an application [9]. People who have cloud storage and use applications or API to read and process data should ensure there is maximum testing for those cloud applications.…”