Mamoun Alazab

Assoc Prof

Accepting PhD Students

20122019
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Personal profile

Research interests

Mamoun Alazab is an Associate Professor in the College of Engineering, IT and Environment, IT Discipline. He is acyber security researcher and practitioner with industry and academic experience. His research is multidisciplinary that focuses oncyber security and digital forensics of computer systems including current and emerging issues in the cyber environment like cyber-physical systems and internet of things, by taking into consideration the unique challenges present in these environments, with a focus on cybercrime detection and prevention. Assoc. Prof. Alazab received hisPhD degree in Computer Science and has more than 100 research papers. He presented at many invited keynotes talks and panels, at conferences and venues nationally and internationally (22 events in 2018 alone). He is a Senior Member of the IEEE. He is an editor on multiple editorial boards including Associate Editor of IEEE Access (2017 Impact Factor 3.557), Editor of the Security and Communication Networks Journal (2017 Impact Factor: 0.904) and Book Review Section Editor: Journal of Digital Forensics, Security and Law (JDFSL).

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Learning systems Engineering & Materials Science
Intrusion detection Engineering & Materials Science
Internet Engineering & Materials Science
vulnerability Social Sciences
Classifiers Engineering & Materials Science
Electronic mail Engineering & Materials Science
Malware Engineering & Materials Science
Authentication Engineering & Materials Science

Network Recent external collaboration on country level. Dive into details by clicking on the dots.

Research Output 2012 2019

A hybrid deep learning image-based analysis for effective malware detection

Venkatraman, S., Alazab, M. & Vinayakumar, R., 1 Aug 2019, In : Journal of Information Security and Applications. 47, p. 377-389 13 p.

Research output: Contribution to journalArticleResearchpeer-review

Internet
Malware
Deep learning
Scalability
Classifiers

A hybrid technique to detect botnets, based on P2P traffic similarity

Khan, R. U., Kumar, R., Alazab, M. & Zhang, X., 1 May 2019, Proceedings - 2019 Cybersecurity and Cyberforensics Conference, CCC 2019. IEEE, Institute of Electrical and Electronics Engineers, p. 136-142 7 p. 8854561. (Proceedings - 2019 Cybersecurity and Cyberforensics Conference, CCC 2019).

Research output: Chapter in Book/Report/Conference proceedingConference Paper published in ProceedingsResearchpeer-review

traffic
conversation
hypertext
chat
centralization
6 Downloads (Pure)

An adaptive multi-layer botnet detection technique using machine learning classifiers

Khan, R. U., Zhang, X., Kumar, R., Sharif, A., Golilarz, N. A. & Alazab, M., 11 Jun 2019, In : Applied Sciences (Switzerland). 9, 11, p. 1-22 22 p., 2375.

Research output: Contribution to journalArticleResearchpeer-review

Open Access
File
machine learning
classifiers
traffic
Learning systems
Classifiers

An improved convolutional neural network model for intrusion detection in networks

Khan, R. U., Zhang, X., Alazab, M. & Kumar, R., 3 Oct 2019, Proceedings - 2019 Cybersecurity and Cyberforensics Conference, CCC 2019. IEEE, Institute of Electrical and Electronics Engineers, p. 74-77 4 p. 8854549. (Proceedings - 2019 Cybersecurity and Cyberforensics Conference, CCC 2019).

Research output: Chapter in Book/Report/Conference proceedingConference Paper published in ProceedingsResearchpeer-review

Intrusion detection
neural network
Neural networks
Learning systems
learning

A reinforcement learning based algorithm towards energy efficient 5G multi-tier network

Islam, N., Alazab, A. & Alazab, M., 1 May 2019, Proceedings - 2019 Cybersecurity and Cyberforensics Conference, CCC 2019. IEEE, Institute of Electrical and Electronics Engineers, p. 96-101 6 p. 8854559. (Proceedings - 2019 Cybersecurity and Cyberforensics Conference, CCC 2019).

Research output: Chapter in Book/Report/Conference proceedingConference Paper published in ProceedingsResearchpeer-review

Reinforcement learning
reinforcement
Base stations
Energy efficiency
energy