Review of Artificial Intelligence Cyber Threat Assessment Techniques for Increased System Survivability-Reference-Cited by-同舟云学术

Review of Artificial Intelligence Cyber Threat Assessment Techniques for Increased System Survivability

Published:2020-12-21 Issue: Volume: Page:207-222
ISSN:
Container-title:Malware Analysis Using Artificial Intelligence and Deep Learning
language:
Short-container-title:

Author:

Doukas Nikolaos,Stavroulakis Peter,Bardis Nikolaos

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-62582-5_7

Reference18 articles.

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2. Alazab, M., S. Venkatraman, P. Watters, and M. Alazab. 2013. Information security governance: The art of detecting hidden malware. IT security governance innovations: theory and research, 293–315. Harrisburg: IGI Global.

3. Asăvoae, Irina Măriuca, Jorge Blasco, Thomas M. Chen, Harsha Kumara Kalutarage, Igor Muttik, Hoang Nga Nguyen, Markus Roggenbach, and Siraj Ahmed Shaikh. 2017. Detecting malicious collusion between mobile software applications: The AndroidTM case. https://www.jorgeblascoalis.com/assets/papers/Asavoae2017.pdf.

4. Atighetchi, Michael, and Joseph Loyall. 2010. Meaningful and flexible survivability assessments: Approach and practice, 12–18. In CrossTalk: The journal of defense software engineering.

5. Azab, A., M. Alazab, and M. Aiash. 2016. Machine learning based botnet identification traffic. IEEE Trustcom/BigDataSE/ISPA 1788–1794.

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