Incremental SVM based on reserved set for network intrusion detection

Yi, Yang; Wu, Jiansheng; Xu, Wei

doi:10.1016/j.eswa.2010.12.141

Cited by 102 publications

(47 citation statements)

References 8 publications

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“…In [7], Yi et al proposed an improved incremental SVM algorithm, called RS-ISVM, to deal with network intrusion detection. The authors select about 10% of the KDD99 dataset as the source dataset.…”

Section: 24mentioning

confidence: 99%

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An incremental intrusion detection system using a new semi‐supervised stream classification method

Noorbehbahani

Fanian

Mousavi

et al. 2015

Int J Communication

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SUMMARYIn recent years, the utilization of machine learning and data mining techniques for intrusion detection has received great attention by both security research communities and intrusion detection system (IDS) developers. In intrusion detection, the most important constraints are the imbalanced class distribution, the scarcity of the labeled data, and the massive amounts of network flows. Moreover, because of the dynamic nature of the network flows, applying static learned models degrades the detection performance significantly over time. In this article, we propose a new semi-supervised stream classification method for intrusion detection, which is capable of incremental updating using limited labeled data. The proposed method, called the incremental semi-supervised flow network-based IDS (ISF-NIDS), relies on an incremental mixed-data clustering, a new supervised cluster adjustment method, and an instance-based learning. The ISF-NIDS operates in real time and learns new intrusions quickly using limited storage and processing power. The experimental results on the KDD99, Moore, and Sperotto benchmark datasets indicate the superiority of the proposed method compared with the existing state-of-the-art incremental IDSs.

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Section: 24mentioning

confidence: 99%

“…(1) Incremental learning: an IDS should be capable of incremental learning to adapt its model continually [7,8]. (2) Semi-supervised learning: an IDS should learn and update its model with a limited number of labeled data [9].…”

Section: Introductionmentioning

confidence: 99%

An incremental intrusion detection system using a new semi‐supervised stream classification method

Noorbehbahani

Fanian

Mousavi

et al. 2015

Int J Communication

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show abstract

“…Design metrics have proved their utility for fault prediction [3].Static code metrics also proven their effectiveness in many studies [4]. Weyuker et.al, [5] found addition of developer information improves the accuracy of fault prediction models.…”

Section: Related Workmentioning

confidence: 99%

Feature Selection based Neural Networks for Software Defect Prediction

Srinivas¹,

Pradeepini²

2014

IJSTSD

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“…The system can extend by applying of online training with unsupervised and incremental learning methods. Yang Yi, Jiansheng Wu and Wei Xu [4] have proposed RS-ISVM which is combined with modified U-RBF kernel function. They have embedded the mean and mean square difference values of feature attributes in RBF kernel function.…”

Section: Literature Surveymentioning

confidence: 99%

A Framework for Simulation of Intrusion Detection System using Support Vector Machine

Gaikwad¹,

Thool²

2013

IJCA

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An intrusion compromises the security and the value of a computer system in network. Legitimate users find it difficult to access network services due to the network attacks as they intentionally occupy or sabotage network resources and services. The intrusion detection system defends the critical computer system and networks from cyber-attacks. Various techniques of machine learning are applied to intrusion detection system. In this paper, a framework for simulation of intrusion detection system is described. The radial basis kernel based support vector machine is used to simulate the intrusion detection system. The major research goal regarding the SVM is to improve the speed in training and testing by determining the best kernel for a given data. Out of the various parameters of the packet only few important normalized parameters are used which will result in improving speed of training the SVM and high detection rate. The KDDCUP'99 dataset is used to train and test the system. The experimental results show that the detection rate of the system is 88.27% with good speed. Furthermore, two applications of framework are described to show how the system can be used to generate pattern of attack for testing the system and how the system prevent downloading of large PDF files from server by unauthorized user.

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Incremental SVM based on reserved set for network intrusion detection

Cited by 102 publications

References 8 publications

An incremental intrusion detection system using a new semi‐supervised stream classification method

An incremental intrusion detection system using a new semi‐supervised stream classification method

Feature Selection based Neural Networks for Software Defect Prediction

A Framework for Simulation of Intrusion Detection System using Support Vector Machine

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