Intrusion detection in cloud computing based attack patterns and risk assessment

Abstract

This paper is an extension of work originally presented in SYSCO CONF.We extend our previous work by presenting the initial results of the implementation of intrusion detection based on risk assessment on cloud computing. The idea focuses on a novel approach for detecting cyber-attacks on the cloud environment by analyzing attacks pattern using risk assessment methodologies. The aim of our solution is to combine evidences obtained from Intrusion Detection Systems (IDS) deployed in a cloud with risk assessment related to each attack pattern. Our approach presents a new qualitative solution for analyzing each symptom, indicator and vulnerability analyzing impact and likelihood of distributed and multi-steps attacks directed to cloud environments. The implementation of this approach will reduce the number of false alerts and will improve the performance of the IDS.

Cyberattack detection in mobile cloud computing: A deep learning approach

“…For example, the authors of [4], [5], and [6] introduced solutions to detect DoS attacks. Alternatively, one can also rely on attacks' patterns and risk assessment [9], game theory [10], and supervised learning [11] to detect and counter cyber threats. The common limitation of these methods is the relatively low accuracy in detecting cyberattacks, and they are unable to work effectively in real-time cloud systems with different types of attacks.…”