Research on intrusion detection and response: a survey
Peyman Kabiri,Ali A. Ghorbani +1 more
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TLDR
A review on current trends in intrusion detection together with a study on technologies implemented by some researchers in this research area are provided.Abstract:
With recent advances in network based technology and increased dependability of our every day life on this technology, assuring reliable operation of network based systems is very important. During recent years, number of attacks on networks has dramatically increased and consequently interest in network intrusion detection has increased among the researchers. This paper provides a review on current trends in intrusion detection together with a study on technologies implemented by some researchers in this research area. Honey pots are effective detection tools to sense attacks such as port or email scanning activities in the network. Some features and applications of honey pots are explained in this paper.read more
Citations
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Journal ArticleDOI
Anomaly-based network intrusion detection: Techniques, systems and challenges
TL;DR: The main challenges to be dealt with for the wide scale deployment of anomaly-based intrusion detectors, with special emphasis on assessment issues are outlined.
Journal ArticleDOI
Review: Intrusion detection system: A comprehensive review
TL;DR: Through the extensive survey and sophisticated organization, this work proposes the taxonomy to outline modern IDSs and tries to give a more elaborate image for a comprehensive review.
Journal ArticleDOI
Network Intrusion Detection System: A systematic study of Machine Learning and Deep Learning approaches
Zeeshan Ahmad,Zeeshan Ahmad,Adnan Shahid Khan,Cheah Wai Shiang,Johari Abdullah,Farhan Ahmad,Farhan Ahmad +6 more
TL;DR: The concept of IDS is clarified and the taxonomy based on the notable ML and DL techniques adopted in designing network‐based IDS (NIDS) systems is provided, which highlights various research challenges and provided the future scope for the research in improving ML andDL‐based NIDS.
Journal ArticleDOI
A network intrusion detection system based on a Hidden Naïve Bayes multiclass classifier
TL;DR: The Hidden Naive Bayes (HNB) model can be applied to intrusion detection problems that suffer from dimensionality, highly correlated features and high network data stream volumes and significantly improves the accuracy of detecting denial-of-services (DoS) attacks.
Journal ArticleDOI
Taxonomy and Survey of Collaborative Intrusion Detection
TL;DR: The entire framework of requirements, building blocks, and attacks as introduced is used for a comprehensive analysis of the state of the art in collaborative intrusion detection, including a detailed survey and comparison of specific CIDS approaches.
References
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Journal ArticleDOI
A framework for constructing features and models for intrusion detection systems
Wenke Lee,Salvatore J. Stolfo +1 more
TL;DR: A novel framework, MADAM ID, for Mining Audit Data for Automated Models for Instrusion Detection, which uses data mining algorithms to compute activity patterns from system audit data and extracts predictive features from the patterns.
Proceedings ArticleDOI
Intrusion detection using neural networks and support vector machines
TL;DR: Using a set of benchmark data from a KDD (knowledge discovery and data mining) competition designed by DARPA, it is demonstrated that efficient and accurate classifiers can be built to detect intrusions.
Proceedings ArticleDOI
Specification-based anomaly detection: a new approach for detecting network intrusions
TL;DR: Whereas feature selection was a crucial step that required a great deal of expertise and insight in the case of previous anomaly detection approaches, it is shown that the use of protocol specifications in the approach simplifies this problem.
Book
Theory of multivariate statistics
Martin Bilodeau,David Brenner +1 more
TL;DR: Linear algebra as discussed by the authors, Gamma, Dirichlet, and F distributions, and Wishart distributions are used for linear algebra and linear algebra is used for robustness and robustness.
Journal ArticleDOI
Adaptive Intrusion Detection: A Data Mining Approach
TL;DR: A data mining framework for constructing intrusion detection models that uses meta-learning as a mechanism to makeintrusion detection models more effective and adaptive and uses an iterative level-wise approximation mining procedure to uncover the low frequency but important patterns.