Open AccessAn Enhanced Classification Model for Likelihood of Zero-Day Attack Detection and Estimation
Author(s) -
Victor T. Emmah,
Chidiebere Ugwu,
L. N. Onyejegbu
Publication year - 2021
Publication title -
european journal of electrical engineering and computer science
Language(s) - English
Resource type - Journals
ISSN - 2736-5751
DOI - 10.24018/ejece.2021.5.4.350
Subject(s) - computer science , malware , intrusion detection system , benchmark (surveying) , artificial intelligence , feature (linguistics) , deep learning , machine learning , zero (linguistics) , data mining , vulnerability (computing) , ranking (information retrieval) , pareto principle , pattern recognition (psychology) , statistics , computer security , mathematics , linguistics , philosophy , geodesy , geography
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.