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
Machine learning-based IDS for software-defined 5G network
TL;DR: An intelligent intrusion system taking the advances of software defined technology and artificial intelligence based on Software Defined 5G architecture flexibly combines security function mod- ules which are adaptively invoked under centralized management and control with a globle view is proposed.
Journal ArticleDOI
Detecting Behavioral Change of IoT Devices Using Clustering-Based Network Traffic Modeling
TL;DR: A modular device classification architecture is developed that allows operators to automatically detect IoT devices by their network activity and dynamically accommodate legitimate changes in assets (either addition of new device profile or upgrade of existing profiles).
Journal ArticleDOI
An End-to-End Framework for Machine Learning-Based Network Intrusion Detection System
Gustavo de Carvalho Bertoli,Lourenço Alves Pereira Júnior,Osamu Saotome,Aldri Santos,Filipe Alves Neto Verri,Cesar Augusto Cavalheiro Marcondes,Sidnei Barbieri,Moisés da Silva Rodrigues,Jose M. Parente De Oliveira +8 more
TL;DR: AB-TRAP as mentioned in this paper is a five-step framework consisting of the attack dataset, the bonafide dataset, training of machine learning models, realization (implementation) of the models, and performance evaluation of the realized model after deployment.
Journal ArticleDOI
Foundations and applications of artificial Intelligence for zero-day and multi-step attack detection
TL;DR: This review proposes a comprehensive framework for addressing the challenge of characterising novel complex threats and relevant counter-measures in the field of intrusion detection, which is typically performed online, and security investigation, performed offline.
Proceedings ArticleDOI
Machine Learning Techniques for Network Anomaly Detection: A Survey
TL;DR: This paper surveys recent research advances linked to machine learning techniques in intrusion detection systems with different algorithms and compares them in terms of intrusion accuracy and detection rate using different data sets.
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