IEEE Communications Surveys & Tutorials· 2015Q1· Review
A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection
- 3,128citations
- Q1SCImago
- 2015year
Short summary
This survey paper provides a focused review of machine learning (ML) and data mining (DM) techniques applied to cyber security intrusion detection.
AI-generated from the title and abstract; the full text is not read.
Key points
- Reviews machine learning and data mining methods for intrusion detection.
- Includes tutorial descriptions of ML/DM techniques.
- Identifies relevant cyber data sets.
- Discusses challenges and provides recommendations for using ML/DM in cyber security.
AI-generated from the title and abstract; the full text is not read.
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.
The authors' abstract, as published at the source. IEEE Communications Surveys & Tutorials, 2015 · DOI ↗
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Field: Computer Networks and Communications
Computer Networks and CommunicationsComputer Science