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User Behavior Traffic Analysis Using a Simplified Memory-Prediction Framework

Computers, Materials & Continua 2022年第2期70卷 2679-2698页

作者： Rahmat Budiarto Ahmad A.Alqarni Mohammed YAlzahrani Muhammad Fermi Pasha Mohamed FazilMohamed Firdhous Deris Stiawan College of Computer Science&IT Albaha UniversityAlaqiq65779-7738Saudi Arabia Malaysia School of Information Technology Monash UniversityBandar Sunway47500Malaysia Department of Information Technology University of MoratuwaMoratuwa10400Sri Lanka Faculty of Computer Science Universitas SriwijayaIndralaya30662Indonesia

As nearly half of the incidents in enterprise security have been triggered by insiders,it is important to deploy a more intelligent defense system to assist enterprises in pinpointing and resolving the incidents caused by insiders or malicious software(malware)in *** to do so may cause a serious loss of reputation as well as *** the same time,modern network traffic has dynamic patterns,high complexity,and large volumes that make it more difficult to detect malware *** ability to learn tasks sequentially is crucial to the development of artificial *** neurogenetic computation models with deep-learning techniques are able to detect complex patterns;however,the models have limitations,including catastrophic forgetfulness,and require intensive computational *** defense systems using deep-learning models require more time to learn new traffic patterns,they cannot perform fully online(on-the-fly)***,an intelligent attack/malware detection system with on-the-fly learning capability is *** this paper,a memory-prediction framework was adopted,and a simplified single cell assembled sequential hierarchical memory(***)model instead of the hierarchical temporal memory(HTM)model is proposed to speed up learning convergence to achieve onthe-fly *** *** consists of a Single Neuronal Cell(SNC)model and a simplified Sequential Hierarchical Superset(SHS)*** *** implemented as the prediction engine of a user behavior analysis tool to detect insider attacks/*** experimental results show that the proposed memory model can predict users’traffic behavior with accuracy level ranging from 72%to 83%while performing on-the-fly learning.

关键词： Machine learning memory prediction framework insider attacks user behavior analytics

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