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
A Novel Framework Design of Network Intrusion Detection Based on Machine Learning Techniques
TL;DR: Wang et al. as discussed by the authors proposed a general intrusion detection framework, which consists of five parts: preprocessing module, autoencoder module, database module, classification module, and feedback module.
Journal ArticleDOI
A Survey on Representation Learning Efforts in Cybersecurity Domain
TL;DR: This survey highlights various cyber-threats, real-life examples, and initiatives taken by various international organizations, and discusses various computing platforms based on representation learning algorithms to process and analyze the generated data.
Proceedings ArticleDOI
A novel approach for internet traffic classification based on multi-objective evolutionary fuzzy classifiers
TL;DR: This paper adopts two Internet traffic datasets extracted from two real-world networks and shows that, in both cases, MOEFCs can achieve satisfactory accuracy in the face of low complexity and, therefore, high interpretability.
Proceedings ArticleDOI
Generative Deep Learning for Internet of Things Network Traffic Generation
TL;DR: In this paper, an autoencoder with a GAN is used to generate sequences of packet sizes that correspond to bidirectional flows, which can fool anomaly detectors into labeling them as legitimate.
Journal ArticleDOI
Exploring user behavioral data for adaptive cybersecurity
TL;DR: Results from the empirical study show that predictive analytics is feasible in the context of behavioral cybersecurity, and can aid in the generation of useful heuristics for the design and development of adaptive cybersecurity mechanisms.
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