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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Sequence Aggregation Rules for Anomaly Detection in Computer Network Traffic.
TL;DR: It is concluded that the frequency-based model tends to perform as well as or better than the LSTM models for the tasks at hand, with a few notable exceptions.
Journal ArticleDOI
Robust deep auto-encoding Gaussian process regression for unsupervised anomaly detection
TL;DR: A novel hybrid unsupervised AD method is proposed that first integrates convolutional auto-encoder and Gaussian process regression to extract features and to remove anomalies from noisy data as well, which behaves more effectively at modeling high-dimension data and more robust to variation of the anomaly rate in dataset.
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An Adversarial Approach for Explainable AI in Intrusion Detection Systems
TL;DR: An approach to generate explanations for incorrect classifications made by data-driven Intrusion Detection Systems (IDSs) by finding the minimum modifications required to correctly classify a given set of misclassified samples.
Journal ArticleDOI
Distance Measurement Methods for Improved Insider Threat Detection
TL;DR: This work builds on a published method of detecting insider threats and applies Hidden Markov method on a CERT data set and analyses a number of distance vector methods in order to detect changes of behaviour, which are shown to have success in determining different insider threats.
Journal ArticleDOI
Automatic Multi-task Learning System for Abnormal Network Traffic Detection
TL;DR: This study proposes a novel multi-task learning system based on convolutional neural network, which can simultaneously solve the tasks of malware detection, VPN-capsulation recognition and Trojan classification, and finds a synergy among all these tasks and achieves the state-of-the-art output.
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