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
More filters
Journal ArticleDOI
User abnormal behavior recommendation via multilayer network
TL;DR: An unorthodox approach involving graph analysis is proposed to resolve this dilemma and build a novel private-preserving recommendation system under a multilayer network framework and shows that almost all feedbacks have achieved up to 85% satisfaction.
Proceedings ArticleDOI
A Machine learning based intrusion detection approach for industrial networks
TL;DR: An approach is presented that monitors the activities of factory network traffic based on two linear feature extraction algorithms, i.e. LDA and PCA, to detect and report anomalies such as malicious attacks.
Book ChapterDOI
Denoising Adversarial Autoencoder for Obfuscated Traffic Detection and Recovery
TL;DR: This paper proposes an unsupervised Deep Learning (DL)-based model, based on generative DL architectures, namely Autoencoders (AE) and Generative Adversarial Network (GAN), that consists of a denoising AE to de-anonymize the mutated traffic and a discriminator to detect it.
Proceedings ArticleDOI
Machine Learning Approach to Cyber Security in Aviation
TL;DR: A set of real-world potential cyber threats in the aviation industry is described to identify and immunize against such threats.
Proceedings ArticleDOI
On the Adversarial Robustness of Subspace Learning
Fuwei Li,Lifeng Lai,Shuguang Cui +2 more
TL;DR: The optimal rank-one attack strategy is characterized and it is shown that the optimal strategy depends on the smallest singular value of the original data matrix and the adversary’s energy budget.
References
More filters
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.
Related Papers (5)
Outside the Closed World: On Using Machine Learning for Network Intrusion Detection
Robin Sommer,Vern Paxson +1 more