A Hybrid Machine Learning Approach for Malicious Behaviour Detection and Recognition in Cloud ComputingShow others and affiliations
2020 (English)In: Journal of Network and Computer Applications, ISSN 1084-8045, E-ISSN 1095-8592, Vol. 151, article id 102507Article in journal (Refereed) Published
Abstract [en]
The rapid growth of new emerging computing technologies has encouraged many organizations to outsource their data and computational requirements. Such services are expected to always provide security principles such as confidentiality, availability and integrity; therefore, a highly secure platform is one of the most important aspects of cloud-based computing environments. A considerable improvement over traditional security strategies is achieved by understanding how malware behaves over the entire behavioural space. In this paper, we propose a new approach to improve the capability of cloud service providers to model users’ behaviours. We applied a particle swarm optimization-based probabilistic neural network (PSO-PNN) for the detection and recognition process, in the first module of the recognition process, we meaningfully converted the users’ behaviours to an understandable format and then classified and recognized the malicious behaviours by using a multi-layer neural network. We took advantage of the UNSW-NB15 dataset to validate the proposed solution by characterizing different types of malicious behaviours exhibited by users. Evaluation of the experimental results shows that the proposed method is promising for use in security monitoring and recognition of malicious behaviours. © 2019 Elsevier Ltd
Place, publisher, year, edition, pages
London: Academia Press, 2020. Vol. 151, article id 102507
Keywords [en]
Malicious behaviour recognition, Intrusion detection, Particle Swarm Optimization, Probabilistic Neural Network
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:hh:diva-41270DOI: 10.1016/j.jnca.2019.102507ISI: 000514024000004Scopus ID: 2-s2.0-85076846833OAI: oai:DiVA.org:hh-41270DiVA, id: diva2:1379407
Note
Funding: National Natural Science Foundation of China (61170035, 61272420, 81674099, 61502233), the Fundamental Research Fund for the Central Universities (30916011328, 30918015103), the Nanjing Science and Technology Development Plan Project (201805036), and the “13th Five-Year” Equipment Field Fund (61403120501).
2019-12-172019-12-172022-10-31Bibliographically approved