A novel hybrid anomaly based intrusion detection method

Qazanfari, Kazem; Mirpouryan, Minoo Sadat; Gharaee, Hossein

doi:10.1109/istel.2012.6483122

Cited by 10 publications

(5 citation statements)

References 7 publications

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“…Therefore, selecting useful features by features relevance analysis [9] is a critical step for the whole system. We use IG [12] to decide the most relevant feature in discrimination. For a specified attack type, the feature with the highest IG is regarded as the most relevant feature which has a key role in determining the attack type.…”

Section: Feature Selection Using Information Gain (Ig) Methodsmentioning

confidence: 99%

“…In 2009, Jingwen Tian, Meijuan Gao have proposed this technique which contains the ability of strong function approach and fast convergence of radial basic function neural network [12], the network intrusion detection method based on radial basic function neural network can detect various intrusion behaviors rapidly and effectively by learning the typical intrusion characteristic information.…”

Section: Related Workmentioning

confidence: 99%

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Attribute Reduction based Hybrid Anomaly Intrusion Detection using K-Means and SVM Classifier

Ravale¹,

Marathe²,

Padiya³

2013

IJCA

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In Information Security, intrusion detection is the act of detecting actions that attempt to compromise the confidentiality, integrity or availability of a resource. One of the primary challenges to intrusion detection is the problem of misjudgment, misdetection and lack of real time response to the attack. Various data mining techniques such as clustering, classification and association rule discovery are being used for intrusion detection. The proposed hybrid technique combines data mining approaches like K Means clustering algorithm and Support Vector Machine classification module. The main purpose of proposed technique is to decrease the number of attributes associated with each data point. So that, the proposed technique can perform better in terms of Detection Rate and Accuracy when applied to KDD'99 Data Set.

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Section: Feature Selection Using Information Gain (Ig) Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Attribute Reduction based Hybrid Anomaly Intrusion Detection using K-Means and SVM Classifier

Ravale¹,

Marathe²,

Padiya³

2013

IJCA

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“…C3 [36] KDD dataset e proposed algorithms detect intrusion simultaneously and their output is combined using the rule-based method. e model is tested using the KDD dataset and records an outstanding performance.…”

Section: Datasetmentioning

confidence: 99%

A Systematic Review on Hybrid Intrusion Detection System

Maseno

Wang

Xing

2022

Security and Communication Networks

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As computer networks keep growing at a high rate, achieving confidentiality, integrity, and availability of the information system is essential. Intrusion detection systems (IDSs) have been widely used to monitor and secure networks. The two major limitations facing existing intrusion detection systems are high rates of false-positive alerts and low detection rates on zero-day attacks. To overcome these problems, we need intrusion detection techniques that can learn and effectively detect intrusions. Hybrid methods based on machine learning techniques have been proposed by different researchers. These methods take advantage of the single detection methods and leverage their weakness. Therefore, this paper reviews 111 related studies in the period between 2012 and 2022 focusing on hybrid detection systems. The review points out the existing gaps in the development of hybrid intrusion detection systems and the need for further research in this area.

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“…Qazanfari et al [19] presented a hybrid anomaly-based intrusion detection method that combines two different methods. These methods are trained in a supervised way.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Hybrid Anomaly Detection Scheme Using K-Means Clustering for Wireless Sensor Networks

Wazid

Das

2016

Wireless Pers Commun

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Sensor nodes in a wireless sensor network (WSN) may be lost due to enervation or malicious attacks by an adversary. WSNs deployed for several applications including military applications are prone to various attacks, which degrade the network performance very rapidly. Hybrid anomaly is a type of anomaly that contains the different types of attacker nodes such as blackhole, misdirection, wormhole etc. These multiple attacks can be launched in the network using the hybrid anomaly. In this situation, it is very difficult to find out which kind of attacker nodes are activated in the network. This motivates us to design a robust and efficient secure intrusion detection approach in order to extend the lifetime of a WSN. In this paper, we aim to propose a new intrusion detection technique for hybrid anomaly, which uses the existing data mining algorithm, called K-means clustering. For the detection purpose, patterns of intrusions are built automatically by the K-means clustering algorithm over training data. After that intrusions are detected by matching network activities against these detection patterns. We evaluate our approach over a WSN dataset that is created using Opnet modeler, which contains various attributes, such as endto-end delay, traffic sent and traffic received. The training dataset contains the normal values of the network parameters. The testing dataset is created in actual working mode consists of normal and abnormal values of the network parameters. The proposed technique has the ability to detect two types of malicious nodes: blackhole and misdirection nodes. Our scheme achieves 98.6 % detection rate and 1.2 % false positive rate, which are significantly better than the existing related schemes.

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A novel hybrid anomaly based intrusion detection method

Cited by 10 publications

References 7 publications

Attribute Reduction based Hybrid Anomaly Intrusion Detection using K-Means and SVM Classifier

Attribute Reduction based Hybrid Anomaly Intrusion Detection using K-Means and SVM Classifier

A Systematic Review on Hybrid Intrusion Detection System

An Efficient Hybrid Anomaly Detection Scheme Using K-Means Clustering for Wireless Sensor Networks

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