Behavior-Based Outlier Detection for Network Access Control Systems

Author:

Muhammad Musa Abubakar1,Ayesh Aladdin1,Wagner Isabel2

Affiliation:

1. Mobile Cognitive Systems (MCS) Research Group, Cyber Technology Institute, De Montfort University, The Gateway, Leicester, United Kingdom

2. Cyber Security Center, Cyber Technology Institute, De Montfort University, The Gateway, Leicester, United Kingdom

Publisher

ACM

Reference24 articles.

1. Enhancing one-class support vector machines for unsupervised anomaly detection

2. IoT Device Fingerprint using Deep Learning

3. Anomaly detection

4. Vulnerabilities & Attacks in Mobile Adhoc Networks (MANET);Dhillon Gurjeet Singh;International Journal of Advanced Research in Computer Science,2017

5. An experimental evaluation of novelty detection methods

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2. Machine learning approach for detecting and combating bring your own device (BYOD) security threats and attacks: a systematic mapping review;Artificial Intelligence Review;2023-01-17

3. Research on Network Access Control System Model Based on Virtualization Technology;2022 Fourth International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT);2022-12-26

4. Machine Learning Approach for Detecting and Combating Bring Your Own Device (BYOD) Security Threats and Attacks: A systematic Mapping Review;2022-10-06

5. Device-Type Profiling for Network Access Control Systems using Clustering-Based Multivariate Gaussian Outlier Score;The 5th International Conference on Future Networks & Distributed Systems;2021-12-15

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