Effective approach toward Intrusion Detection System using data mining techniques

Nadiammai, G. V.; Hemalatha, M.

doi:10.1016/j.eij.2013.10.003

Cited by 149 publications

(67 citation statements)

References 12 publications

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“…It was useful to detect intruder in High accuracy rate, Drawback of this approach is cannot processing large dataset. Efficient accession toward Intrusion Detection System using the technique of data mining [2] used Hybrid PSO with C4.5, SNORT with ALADLERAD, SVM and HOPERAA approaches to detect Intruders. Advantage of this approach is the detection of the intruder in high accuracy rate.…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid classification model to detect advanced intrusions using data mining techniques

Mala¹,

Meena²

2018

IJET

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Traditional signature based approach fails in detecting advanced malwares like stuxnet, flame, duqu etc. Signature based comparison and correlation are not up to the mark in detecting such attacks. Hence, there is crucial to detect these kinds of attacks as early as possible. In this research, a novel data mining based approach were applied to detect such attacks. The main innovation lies on Misuse signature detection systems based on supervised learning algorithm. In learning phase, labeled examples of network packets systems calls are (gave) provided, on or after which algorithm can learn about the attack which is fast and reliable to known. In order to detect advanced attacks, unsupervised learning methodologies were employed to detect the presence of zero day/ new attacks. The main objective is to review, different intruder detection methods. To study the role of Data Mining techniques used in intruder detection system. Hybridclassification model is utilized to detect advanced attacks.

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Section: Literature Reviewmentioning

confidence: 99%

Hybrid classification model to detect advanced intrusions using data mining techniques

Mala¹,

Meena²

2018

IJET

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“…Therefore, instead of training the model with only labeled data, we incorporate the unlabelled data before active learning starts. G.V.Nadiammai, S.Krishnaveni, M. Hemalatha [6] have been referred to in order to understand the use different data mining techniques in order to implement an intrusion detection system. In this paper, they are provided with a summary of the current research directions in detecting such attacks using collaborative intrusion detection systems (CIDSs).…”

Section: Related Workmentioning

confidence: 99%

Effective Framework of J48 Algorithm using Semi-Supervised Approach for Intrusion Detection

Wagh¹,

Khati²,

Irani³

et al. 2014

IJCA

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Network security is a very important aspect for internet enabled systems. As the internet keeps developing the number of security attacks as well as their severity has shown a significant increase. The Intrusion Detection System (IDS) plays a very important role in discovering anomalies and attacks in the network. The aim of an intrusion detection system is to identify those entities that attempt to destabilize security controls that have been put in place. The field of machine learning is rapidly gaining more attention in the development of these intrusion detection systems. Machine learning techniques can be broadly classified into three broad categories: Supervised, Un-supervised and semi-supervised. The supervised learning method displays good classification accuracy for those attacks that are aready known to us. But this method requires a large amount of training data.The availability of labelled data is not only time consuming but also very expensive. The evolving field of semi-supervised learning offers a promising direction for supplementary research. Hence, in this paper we propose a semi-supervised approach for a pattern based IDS to improve performance and to reduce the false alarm rate. The experimentation is performed on KDD CUP99 dataset and we use the J48 Algorithm in order to implement the semi-supervised learning.

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“…The classification techniques proposed by Nadiammai et al [15] were used to predict the severity of attacks over the network. A comparison is done with zero R classifier, Decision …”

Section: Related Workmentioning

confidence: 99%

A Fast Construction of Intrusion Relieved Communication Path based on Trust level and Heuristic Search

Reshma¹,

Srivatsa²

2013

IJCA

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An Adhoc network is subjected to many malicious activities and security threatens because of its wide distribution and heterogeneous nature. Intrusion is one among such activities that comprise confidentiality, integrity or availability of resources. Numerous efforts have been made in the literature to detect intrusion in an Adhoc network, however less number of efforts have been put to construct an intrusion relieved network. In the previous work, we proposed a methodology to construct an intrusion relieved network based on trust level of every node. The methodology used Rotboost algorithm to estimate the trust level of every node in the upcoming instants. As the learning of Rotboost requires more time, we planned to incorporate a fast learning algorithm to improve the efficiency of the methodology. Moreover, this paper introduces an efficient heuristic search algorithm to find the shortest path instead Dijkstra algorithm. As Dijkstra is time consuming in determining shortest possible network paths, it ultimately affects the efficiency of constructing intrusion free path. Replacing Dijkstra by heuristic search algorithm can lead to better performance in terms of computational complexity and the intrusion free path can be constructed in an efficient way. Hence a modified architecture for intrusion detection and intrusion free path detection is constructed and simulated. The simulation results show that the modified architecture outperforms the conventional architecture in terms of intrusion detection rate, path costs and computational times.

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Effective approach toward Intrusion Detection System using data mining techniques

Cited by 149 publications

References 12 publications

Hybrid classification model to detect advanced intrusions using data mining techniques

Hybrid classification model to detect advanced intrusions using data mining techniques

Effective Framework of J48 Algorithm using Semi-Supervised Approach for Intrusion Detection

A Fast Construction of Intrusion Relieved Communication Path based on Trust level and Heuristic Search

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