Attacks on cloud computing (CC) services and infrastructure have raised concerns about the efficacy of data protection mechanisms in this environment. The framework developed in this study (CCAID: cloud computing, attack, and intrusion detection) aims to improve the performance of intrusion detection systems (IDS) operating in CC environments. It deploys a proposed new hybrid ensemble feature selection (FS) method. The ensemble includes FS algorithms of three different types (filter, wrapper, and embedded algorithms). The selected features used to train the ML (machine learning) model of the intrusion detection component comprised a binary detection engine for the identification of malicious/attack packets and a multiclassification detection engine for the identification of the type of attack. Both detection engines deploy ensemble classifiers. Experiments were carried out using the NSL KDD dataset. The binary model achieved a classification accuracy of 99.55% with a very low false alarm rate of 0.45%. The classification accuracy of the multiclassification model was also high (98.92%). These results compare very favourably with the results reported in the literature and indicate the feasibility of the framework implementation.
This paper reviews existing Intrusion Detection Systems (IDS) that target the Mobile Cloud Computing (MCC), Cloud Computing (CC), and Mobile Device (MD) environment. The review identifies the drawbacks in existing solutions and proposes a novel approach towards enhancing the security of the User Layer (UL) in the MCC environment. The approach named MINDPRES (Mobile-Cloud Intrusion Detection and Prevention System) combines a host-based IDS and network-based IDS using Machine Learning (ML) techniques. It applies dynamic analysis of both device resources and network traffic in order to detect malicious activities at the UL in the MCCenvironment. Preliminary investigations show that our approach will enhance the security of the UL in the MCC environment. Our future work will include the development and the evaluation of the proposed model across the various mobile platforms in the MCC environment.
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