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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Journal ArticleDOI
Applications in Security and Evasions in Machine Learning: A Survey
TL;DR: This paper examines different security applications’ perspectives where ML models play an essential role and compare, with different possible dimensions, their accuracy results and represents the threat model and defense strategies against adversarial attack methods.
Book ChapterDOI
A Brief Survey on Random Forest Ensembles in Classification Model
TL;DR: The concept of Random forest ensembles in classification is outlined, which can predict the future instances with multiple classifiers rather than single classifier to reach accuracy and correctness of the prediction.
Journal ArticleDOI
A systematic literature review of methods and datasets for anomaly-based network intrusion detection
TL;DR: A systematic literature review of anomaly-based network intrusion detection techniques and datasets is presented in this article , where the authors focus on the technical landscape of the field in order to facilitate subsequent research within this field.
Journal ArticleDOI
Supervised feature selection techniques in network intrusion detection: A critical review
TL;DR: Feature Selection (FS) as discussed by the authors is a crucial pre-processing step in network management and specifically for the purposes of network intrusion detection, where trade-offs between performance and resource consumption are crucial.
Journal ArticleDOI
Inter-dataset generalization strength of supervised machine learning methods for intrusion detection
TL;DR: An experimental investigation into the inter-dataset generalization of supervised machine learning methods, trained to distinguish between benign and several classes of malicious network flows, raises questions about the implied link that great intra- datasetgeneralization leads to great inter- or extra-datasegment generalization.
References
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