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
Recomposition vs. Prediction: A Novel Anomaly Detection for Discrete Events Based On Autoencoder.
Lun-Pin Yuan,Peng Liu,Sencun Zhu +2 more
TL;DR: DabLog as mentioned in this paper is a deep autoencoder-based anomaly detection method for discrete event logs, which determines whether a sequence is normal or abnormal by analyzing (encoding) and reconstructing (decoding) the given sequence.
Journal ArticleDOI
A Novel Self-Adaptive Affinity Propagation Clustering Algorithm Based on Density Peak Theory and Weighted Similarity
TL;DR: The proposed DPWSAP had better clustering accuracy and convergence performance than original AP algorithm and several other clustering algorithms, and improved the overall performance for the algorithm, and reduced the possibility of human factors affecting the algorithm effect.
Posted Content
Deep Structured Cross-Modal Anomaly Detection
TL;DR: A novel deep structured anomaly detection framework to identify the cross-modal anomalies embedded in the data is proposed and experiments demonstrate the effectiveness of the proposed framework comparing with the state-of-the-art.
Journal ArticleDOI
A Hybrid Intrusion Detection Model Combining SAE with Kernel Approximation in Internet of Things.
TL;DR: A joint training model that combines a stacked autoencoder with an SVM and the kernel approximation technique that outperforms the previously proposed methods in terms of classification performance and also reduces the training time is proposed.
Proceedings ArticleDOI
SoK: a taxonomy for anomaly detection in wireless sensor networks focused on node-level techniques
TL;DR: A novel taxonomy of anomaly detection approaches focused on wireless sensor networks and a meta-survey of related classification schemes is contributed with a comprehensive super-set of all previously published taxonomies in this field.
References
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Journal ArticleDOI
Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
Book
Fuzzy sets
TL;DR: A separation theorem for convex fuzzy sets is proved without requiring that the fuzzy sets be disjoint.
Journal ArticleDOI
Maximum likelihood from incomplete data via the EM algorithm
Book
The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Journal ArticleDOI
Collective dynamics of small-world networks
TL;DR: Simple models of networks that can be tuned through this middle ground: regular networks ‘rewired’ to introduce increasing amounts of disorder are explored, finding that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs.
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