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
Multi-Parallel Adaptive Grasshopper Optimization Technique for Detecting Anonymous Attacks in Wireless Networks
TL;DR: A multi-parallel adaptive evolutionary technique to utilize adaptation mechanism in the group of swarms for network intrusion detection and demonstrates that the applicability of proposed technique concerning its merits outperforms the others algorithms.
Posted Content
TANTRA: Timing-Based Adversarial Network Traffic Reshaping Attack.
TL;DR: In this article, the authors presented a novel end-to-end Timing-based Adversarial Network Traffic Reshaping Attack (TANTRA) that can bypass a variety of NIDSs.
Journal ArticleDOI
Imbalanced data classification algorithm with support vector machine kernel extensions
TL;DR: A imbalanced data classification algorithm of support vector machines (KE-SVM) is proposed in this article, this algorithm achieve the initial classification of data samples by training the maximum margin classification SVM model, and then obtaining a new kernel extension function.
Journal ArticleDOI
How to Effectively Collect and Process Network Data for Intrusion Detection
TL;DR: In this article, several feature selection techniques have been applied on five flow-based network intrusion detection datasets, establishing an informative flowbased feature set, and the results show that a set of 10 features and a small amount of data is enough for the final model to perform very well.
Proceedings Article
Supervised Machine Learning based Routing Detection for Smart Meter Network
TL;DR: This paper introduces supervised machine learning to detect unknown routing attacks under AODV and shows that the decision trees algorithm assures 100% accuracy with minimum time overhead to detect routing attacks in A ODV.
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