An Intelligent Intrusion Detection System Using Average Manhattan Distance-based Decision Tree

Selvi, R. Thanga; Kumar, S. Saravan; Suresh, A.

doi:10.1007/978-81-322-2126-5_23

Cited by 4 publications

(2 citation statements)

References 6 publications

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“…According to authors, instead of single classifier multi classifier system because the multi classifier system provide the superior classification accuracy. In [15] Selvi et. al.…”

Section: Related Workmentioning

confidence: 99%

Genetic Algorithm to Solve the Problem of Small Disjunct In the Decision Tree Based Intrusion Detection System

Azad

Jha²

2015

IJCNIS

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Abstract-Intrusion detection system is the most important part of the network security system because the volume of unauthorized access to the network resources and services increase day by day. In this paper a genetic algorithm based intrusion detection system is proposed to solve the problem of the small disjunct in the decision tree. In this paper genetic algorithm is used to improve the coverage of those rules which are cope with the problem of the small disjunct. The proposed system consists of two modules rule generation phase, and the second module is rule optimization module. We tested the effectiveness of the system with the help of the KDD CUP dataset and the result is compared with the REP Tree, Random Tree, Random Forest, Naï ve Bayes, and the DTLW IDS (decision tree based light weight intrusion detection system). The result shows that the proposed system provide the best result in comparison to the above mentioned classifiers.

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“…According to authors, instead of single classifier multi classifier system because the multi classifier system provide the superior classification accuracy. In [15] Selvi et. al.…”

Section: Related Workmentioning

confidence: 99%

Genetic Algorithm to Solve the Problem of Small Disjunct In the Decision Tree Based Intrusion Detection System

Azad

Jha²

2015

IJCNIS

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“…Although it has been 16 years since the release of the KDD'99 dataset, it is still in use as the primary source for network intrusion detection studies. For example, [6][7][8][9][10] are some of the papers that were released in 2014 and 2015 in which the KDD dataset was used.…”

Section: Introductionmentioning

confidence: 99%

Modified stacking ensemble approach to detect network intrusion

Demir¹,

Dalkılıç²

2018

Turk J Elec Eng & Comp Sci

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Detecting intrusions in a network traffic has remained an issue for researchers over the years. Advances in the area of machine learning provide opportunities to researchers to detect network intrusion without using a signature database. We studied and analyzed the performance of a stacking technique, which is an ensemble method that is used to combine different classification models to create a better classifier, on the KDD'99 dataset. In this study, the stacking method is improved by modifying the model generation and selection techniques and by using different classifications algorithms as a combiner method. Model generation is performed using subsets of the dataset with randomly selected features and not all of these models are used as input for the combiner. Various metrics are used in model selection and only selected models are used as input for the combiner method. In our experiments, the stacking technique provided higher accuracy results all the time compared to pure machine learning techniques. The second important result in our experiments was obtaining the highest detection rate for user-to-root attacks compared to other studies.

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Decision Tree and Genetic Algorithm Based Intrusion Detection System

Azad

Jha

2018

Lecture Notes in Electrical Engineering

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An Intelligent Intrusion Detection System Using Average Manhattan Distance-based Decision Tree

Cited by 4 publications

References 6 publications

Genetic Algorithm to Solve the Problem of Small Disjunct In the Decision Tree Based Intrusion Detection System

Genetic Algorithm to Solve the Problem of Small Disjunct In the Decision Tree Based Intrusion Detection System

Modified stacking ensemble approach to detect network intrusion

Decision Tree and Genetic Algorithm Based Intrusion Detection System

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