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
Unsupervised Outlier Detection via Transformation Invariant Autoencoder
TL;DR: In this article, a transformation invariant autoencoder (TIAE) is proposed for unsupervised outlier detection in complex image datasets, where the most confident inliers likely examples in each epoch are used as the training set.
Journal ArticleDOI
Preventing crimes against public health with artificial intelligence and machine learning capabilities
Hongning Wang,Sanjun Ma +1 more
TL;DR: The prediction model established by artificial intelligence algorithm can effectively predict criminal behaviors that endanger public health and provide reliable data for prevention.
Journal ArticleDOI
Threat Detection and Investigation with System-level Provenance Graphs: A Survey
TL;DR: A comprehensive provenance graph-based threat detection system can be divided into three modules, namely, "data collection module", "data management module", and "threat detection modules", and the strategy of technology selection is given.
Proceedings ArticleDOI
On the Evaluation of Sequential Machine Learning for Network Intrusion Detection
TL;DR: In this article, the authors proposed a detailed methodology to extract temporal sequences of NetFlow that denote patterns of malicious activities, and applied this methodology to compare the efficacy of sequential learning models against traditional static learning models.
Journal ArticleDOI
On the performance of intelligent techniques for intensive and stealthy DDos detection
Xiaoyu Liang,Taieb Znati +1 more
TL;DR: A taxonomy of the ML-based DDoS detection schemes, focusing on the important features and mechanisms that each scheme uses to detect and mitigate the impact of these attacks, and shows that the class imbalance problem significantly impact performance.
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