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- Applied Science & Technology
Keyword Searches
Keyword Database Results
“Machine learning false positives” Computer Science Collection 23
“Machine learning false positives” Applied Science & Technology 2
“Machine learning improvements” Computer Science Collection 26
“Machine learning improvements” Applied Science & Technology 30
“Machine learning cybersecurity” Computer Science Collection 13
“Machine learning cybersecurity” Applied Science & Technology 9
Useful Sources
Name: Distance Measurement Methods for Improved Insider Threat Detection
Database: Computer Science Collection
Citation: Lo, O., Buchanan, W. J., Griffiths, P., & Macfarlane, R. (2018). Distance Measurement Methods for Improved Insider Threat Detection. Security and Communication Networks, 2018. https://link.gale.com/apps/doc/A596644750/CDB?u=klnb_fhsuniv&sid=bookmark-CDB&xid=418c79dd
Name: Difficulties Faced and Applications of Machine Learning in Cyber-Security
Database: Applied Science & Technology
Citation: Batyha, R. M., Aburashed, T. K., & Alshammari, B. R. (2021). Difficulties Faced and Applications of Machine Learning in Cyber-Security. International Journal of Advances in Soft Computing & Its Applications, 13(2), 162–172.
Name: A Behaviour Profiling Based Technique for Network Access Control Systems
Database: Computer Science Collection
Citation: Muhammad, Musa Abubakar, and Aladdin Ayesh. “A Behaviour Profiling Based Technique for Network Access Control Systems.” International Journal of Cyber-Security and Digital Forensics, vol. 8, no. 1, Jan. 2019, pp. 23+. Gale OneFile: Computer Science, link.gale.com/apps/doc/A607390802/CDB?u=klnb_fhsuniv&sid=bookmark-CDB&xid=93415a45. Accessed 29 Oct. 2021.
Name: DeepBalance: Deep-Learning and Fuzzy Oversampling for Vulnerability Detection
Database: Applied Science & Technology
Citation: Liu, S., Lin, G., Han, Q.-L., Wen, S., Zhang, J., & Xiang, Y. (2020). DeepBalance: Deep-Learning and Fuzzy Oversampling for Vulnerability Detection. IEEE Transactions on Fuzzy Systems, 28(7), 1329–1343. https://doi-org.ezproxy.fhsu.edu/10.1109/TFUZZ.2019.2958558
(Annotation) A Behaviour Profiling Based Technique for Network Access Control Systems
This article discusses the use of new techniques in machine learning to ensure better network and endpoint security. Unfortunately with BYOD and increased mobile phone use, firewalls and antivirus solutions will not always be able to protect against these new threats. By using large datasets available in the cloud, network security components can better understand behavior and identify what is normal behavior and what is not. The article discusses the need for improved machine learning to better protect endpoints and networks and as a method to do so, a need for larger and more specific unusual behavior datasets can help protect these devices. By profiling, each device on a network machine learning tools can better understand the normal from the abnormal.