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
A Discretized Extended Feature Space (DEFS) Model to Improve the Anomaly Detection Performance in Network Intrusion Detection Systems
TL;DR: A novel Discretized Extended Feature Space (DEFS) model is introduced that presents a twofold advantage: first, through a discretization process it reduces the event patterns by grouping those similar in terms of feature values, reducing the issues related to the classification of unknown events; second, it balances such a discRETization by extending the event pattern with a series of meta-information able to well characterize them.
Proceedings ArticleDOI
On the Analysis of Network Measurements Through Machine Learning: The Power of the Crowd
TL;DR: Results suggest that both neural networks and decision-tree-based models provide in general better results in terms of accuracy and prediction, with a much smaller computation overhead for decision trees as compared to models based on neural networks or support vector machines.
Book ChapterDOI
STAN: Synthetic Network Traffic Generation with Generative Neural Models
TL;DR: In this paper, the authors proposed Synthetic network Traffic Generation with Autoregressive Neural Models (STAN), a tool to generate realistic synthetic network traffic datasets for subsequent downstream applications.
Book ChapterDOI
Requirements for Training and Evaluation Dataset of Network and Host Intrusion Detection System
Petteri Nevavuori,Tero Kokkonen +1 more
TL;DR: The requirements for state-of-the-art NHIDS dataset are presented to be utilised for research and development of NHIDS applying machine learning and artificial intelligence.
Journal ArticleDOI
Investigating the Effect of Traffic Sampling on Machine Learning-Based Network Intrusion Detection Approaches
TL;DR: Jumabek et al. as discussed by the authors explored the impact of packet sampling on the performance and efficiency of ML-based NIDSs and found that malicious flows with shorter size are likely to go unnoticed even with mild sampling rates such as 1/10 and 1/100.
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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Outside the Closed World: On Using Machine Learning for Network Intrusion Detection
Robin Sommer,Vern Paxson +1 more