Journal ArticleDOI
Anomaly-based intrusion detection system through feature selection analysis and building hybrid efficient model
TLDR
A new hybrid model can be used to estimate the intrusion scope threshold degree based on the network transaction data’s optimal features that were made available for training and revealed that the hybrid approach had a significant effect on the minimisation of the computational and time complexity involved when determining the feature association impact scale.About:
This article is published in Journal of Computational Science.The article was published on 2017-03-22. It has received 484 citations till now. The article focuses on the topics: Anomaly-based intrusion detection system & Intrusion detection system.read more
Citations
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Proceedings ArticleDOI
Exploration of Machine Learning Algorithms for Development of Intelligent Intrusion Detection Systems
TL;DR: In this paper , the authors explore the use of machine learning algorithms in developing intelligent intrusion detection systems for preventing unauthorized access to computer networks and provide an architectural evaluation of the existing deep/machine learning-based solutions of string-matching algorithms.
Proceedings ArticleDOI
Hierarchical Association Features Learning for Network Traffic Recognition
TL;DR: Wang et al. as mentioned in this paper proposed a novel method to learn correlation features of network traffic based on the hierarchical structure, which learns the spatial-temporal features using convolutional neural networks (CNNs) and the bidirectional long short-term memory networks (Bi-LSTMs).
Book ChapterDOI
Correlation Filter Detection and Tracking Model Based on Dynamic Spatial Feature Selection
TL;DR: Wang et al. as discussed by the authors studied the tracking model of correlation filter detection based on dynamic spatial feature selection, and the analysis and research of accelerated correlation filtering detection and tracking model optimized by the improved alternating direction multiplier method.
References
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Proceedings ArticleDOI
Multi-column deep neural networks for image classification
TL;DR: In this paper, a biologically plausible, wide and deep artificial neural network architectures was proposed to match human performance on tasks such as the recognition of handwritten digits or traffic signs, achieving near-human performance.
Proceedings Article
Bro: a system for detecting network intruders in real-time
TL;DR: Bro as mentioned in this paper is a stand-alone system for detecting network intruders in real-time by passively monitoring a network link over which the intruder's traffic transits, which emphasizes high-speed (FDDI-rate) monitoring, realtime notification, clear separation between mechanism and policy and extensibility.
Journal ArticleDOI
Bro: a system for detecting network intruders in real-time
Vern Paxson,Vern Paxson +1 more
TL;DR: An overview of the Bro system's design, which emphasizes high-speed (FDDI-rate) monitoring, real-time notification, clear separation between mechanism and policy, and extensibility, is given.
Journal ArticleDOI
Testing Intrusion detection systems: a critique of the 1998 and 1999 DARPA intrusion detection system evaluations as performed by Lincoln Laboratory
TL;DR: The purpose of this article is to attempt to identify the shortcomings of the Lincoln Lab effort in the hope that future efforts of this kind will be placed on a sounder footing.
Proceedings ArticleDOI
An Intrusion-Detection Model
TL;DR: A model of a real-time intrusion-detection expert system capable of detecting break-ins, penetrations, and other forms of computer abuse is described, based on the hypothesis that security violations can be detected by monitoring a system's audit records for abnormal patterns of system usage.
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