A Hybrid Approach based on Classification and Clustering for Intrusion Detection System

Chahal, Jasmeen Kaur; Kaur, Amanjot

doi:10.5815/ijmsc.2016.04.04

Cited by 10 publications

(13 citation statements)

References 6 publications

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“…It is very important for network protection and control to detect intrusion attacks effectively [17]. In the network intrusion detection, clustering algorithm [18], Apriori algorithm, decision tree [19], Q-learning, neural network [20] and hidden Markov [21] have a wide range of applications. This study mainly analyzed SVM.…”

Section: Discussionmentioning

confidence: 99%

Research on the Detection of Network Intrusion Prevention With Svm Based Optimization Algorithm

Wang¹,

Xu²

2020

IJCAI

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Support vector machine (SVM) has a good application in intrusion detection, but its performance needs to be further improved. This study mainly analyzed the SVM optimization algorithm. The principle of SVM was introduced firstly, then SVM was improved using the improved whale optimization algorithm (WOA), the improved WOA (IWOA)-SVM based intrusion detection method was analyzed, and finally experiments were carried out on KDD CUP99 to verify the effectiveness of the algorithm. The results showed that the IWAO-SVM algorithm was more accurate in attack detection; compared with SVM, PSO-SVM and ant colony optimization (ACO)-SVM algorithms, the performance of the IWAO-SVM algorithm was better, the detection rate was 99.89%, the precision ratio was 99.92%, the accuracy rate was 99.86%, and the detection time was 192 s, showing that it had high precision in intrusion detection. The experimental results verify the reliability of the IWAO-SVM algorithm, and it can be promoted and applied in the detection of network intrusion prevention.Povzetek: Algoritem SVM je bil prilagojen za iskanje napadov v omrežjih.

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Section: Discussionmentioning

confidence: 99%

Research on the Detection of Network Intrusion Prevention With Svm Based Optimization Algorithm

Wang¹,

Xu²

2020

IJCAI

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“…In literature review, we found that there are many ensemble classifiers which have been used for intrusion detection system. Jasmeen K. Chahal and Amanjot Kaur [8] have proposed hybrid intrusion detection to improve false positive and false negative rate. Authors have used K-mean algorithm for clustering data and adaptive Support Vector Machine for classification purpose.…”

Section: Introductionmentioning

confidence: 99%

Intrusion Detection System Using Ensemble of Rule Learners and First Search Algorithm as Feature Selectors

Gaikwad¹

2021

IJCNIS

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Recently, the use of Internet is increased for digital communication to share a lot of sensitive information between computers and mobile devices. For secure communication, data or information must be protected from adversaries. There are many methods of safeties like encryption, firewalls and access control. Intrusion detection system is mainly used to detect internal attacks in organization. Machine leaning techniques are mostly used to implement intrusion detection system. Ensemble method of machine learning gives high accuracy in which moderately accurate classifiers are combined. Ensemble classifier also provides less false positive rates. In this paper, a novel ensemble classifier using rule combination method has proposed for intrusion detection system. Ensemble classifier is designed using three rule learners as base classifiers. The benefits and feasibility of the proposed ensemble classifier have demonstrated by means of KDD’98 datasets. The main novelty of the proposed approach is based on three rule learner combination using rule of combination method of ensemble and feature selector. These three base classifiers are separately trained and combined using average probabilities rule combination. Base classifier’s accuracies have compared with the proposed ensemble classifier. Best First search algorithm has used to select relevant features from training dataset. This algorithm also helped to reduce dimension of training and testing dataset which benefits in reduction of training time. Several comparative experiments are conducted for evaluating performances of classifiers in term of accuracy and false positive rates. Experimental results show that the proposed ensemble classifier provide significant improvement of accuracy compared to individual classifiers with less positive rates.

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“…As a result of that, the cyber space is becoming an unsafe place day by day due to malicious activities. These malicious activities are also called as intrusions that effect integrity, confidentiality and availability of resources [1]. Intrusion detection system (IDS) is a system which protects resources against intrusions.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Approach for Intrusion Detection Using K-Nearest Neighbor and Artificial Neural Network

Dissanayake¹,

N²

2020

ijarcs

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Network intrusion detection is an important process in this era due to the increase of cyber violations. In this article, a hybrid approach which utilizes K-Nearest Neighbor algorithm and Artificial Neural Network to detect intrusions, is proposed. NSL-KDD dataset was used for the study. Initially, data preprocessing was carried out. Encoding was done as the first step of the pre-process which was accomplished using one hot encoding. Then, features were inserted into feature scaling which was done using Min-max normalization. Feature reduction is the final step of the pre-process which was achieved using Principal Component Analysis. Subsequently, K-Nearest Neighbor algorithm was used as binary classifier that classify data into normal and abnormal classes. Then, the abnormal class was further classified into four major attack types using Artificial Neural Network. Finally, the model was evaluated and results show that the model has high accuracy and very low overfitting and underfitting.

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A Hybrid Approach based on Classification and Clustering for Intrusion Detection System

Cited by 10 publications

References 6 publications

Research on the Detection of Network Intrusion Prevention With Svm Based Optimization Algorithm

Research on the Detection of Network Intrusion Prevention With Svm Based Optimization Algorithm

Intrusion Detection System Using Ensemble of Rule Learners and First Search Algorithm as Feature Selectors

A Hybrid Approach for Intrusion Detection Using K-Nearest Neighbor and Artificial Neural Network

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