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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Proceedings ArticleDOI
Detection and Classification of Network Events in LAN Using CNN
TL;DR: A method using convolutional neural network with a refined normalization function and learning function to detect and also classify different events happened in the LAN, using Hilbert Curve, array exchange, and projection to generate feature maps to represent protocol information of events within a predetermined time span is proposed.
Journal ArticleDOI
Proactive trust classification for detection of replication attacks in 6LoWPAN-based IoT
TL;DR: The simulation results show that while maintaining detection runtime on average 60 s for up to 1000 nodes, the proposed trust-based strategy can significantly increase the detection probability to 90% on average against replication attacks and in turn significantly reduce the communication failure.
Proceedings ArticleDOI
Zero Residual Attacks on Industrial Control Systems and Stateful Countermeasures
TL;DR: Zero-Residual Attacks (ZeRA) are offered, which allow the attacker to launch stealthy attacks leveraging estimation of the stateful anomaly detector and matching of residuals as a fraction of actual estimation residual.
Posted Content
Representation Learning for Resource Usage Prediction.
TL;DR: This paper presents the ongoing work of integrating systems telemetry ranging from standard resource usage statistics to kernel and library calls of applications into a machine learning model, and trains recurrent neural networks to learn a model of the system under consideration.
Journal ArticleDOI
Application of meta-learning in cyberspace security: a survey
TL;DR: In this article , the authors divide the meta-learning model into five research directions based on different principles of use, namely model-based, metric based, optimization based, online-learning based, or stacked ensemble-based.
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