Journal ArticleDOI
A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection
Anna L. Buczak,Erhan Guven +1 more
TLDR
The complexity of ML/DM algorithms is addressed, discussion of challenges for using ML/ DM for cyber security is presented, and some recommendations on when to use a given method are provided.Abstract:
This survey paper describes a focused literature survey of machine learning (ML) and data mining (DM) methods for cyber analytics in support of intrusion detection. Short tutorial descriptions of each ML/DM method are provided. Based on the number of citations or the relevance of an emerging method, papers representing each method were identified, read, and summarized. Because data are so important in ML/DM approaches, some well-known cyber data sets used in ML/DM are described. The complexity of ML/DM algorithms is addressed, discussion of challenges for using ML/DM for cyber security is presented, and some recommendations on when to use a given method are provided.read more
Citations
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Security and Privacy Threats for Bluetooth Low Energy in IoT and Wearable Devices: A Comprehensive Survey
TL;DR: This survey paper presents a comprehensive taxonomy for the security and privacy issues of BLE, and presents possible attack scenarios for different types of vulnerabilities, classify them according to their severity, and list possible mitigation techniques.
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TL;DR: It is demonstrated that the proposed data-driven network intelligence system achieves high detection accuracy and provides efficient computational performance and is incorporated into the design of the system.
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Hybrid Feature Selection Models for Machine Learning Based Botnet Detection in IoT Networks
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Blockchain Security: A Survey of Techniques and Research Directions
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