A Hybrid Intrusion Detection System Based on Decision Tree and Support Vector Machine

Kumari, Anku; Mehta, Ashok Kumar

doi:10.1109/iccca49541.2020.9250753

Cited by 24 publications

(11 citation statements)

References 19 publications

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“…Kumari et al [15] developed an ensemble-based model for intrusion detection by combining these two machine learning techniques, J48 DT and SVM. The KDD99 intrusion detection dataset was optimized using particle swarm optimization to identify the nine most relevant and critical attributes, WEKA is utilized to implement classification.…”

Section: Related Workmentioning

confidence: 99%

An Online Ensemble Learning Model for Detecting Attacks in Wireless Sensor Networks

Tabbaa¹,

Ifzarne²,

Hafidi³

2022

Preprint

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In today's modern world, the usage of technology is unavoidable and the rapid advances in the Internet and communication fields have resulted to expand the Wireless Sensor Network (WSN) technology. A huge number of sensing devices collect and/or generate numerous sensory data throughout time for a wide range of fields and applications. However, WSN has been proven to be vulnerable to security breaches, the harsh and unattended deployment of these networks, combined with their constrained resources and the volume of data generated introduce a major security concern. WSN applications are extremely critical, it is essential to build reliable solutions that involve fast and continuous mechanisms for online data stream analysis enabling the detection of attacks and intrusions. In this context, our aim is to develop an intelligent, efficient, and updatable intrusion detection system by applying an important machine learning concept known as ensemble learning in order to improve detection performance. Although ensemble models have been proven to be useful in offline learning, they have received less attention in streaming applications. In this paper, we examine the application of different homogeneous and heterogeneous online ensembles in sensory data analysis, on a specialized wireless sensor network-detection system (WSN-DS) dataset in order to classify four types of attacks: Blackhole attack, Grayhole, Flooding, and Scheduling among normal network traffic. Among the proposed novel online ensembles, both the heterogeneous ensemble consisting of an Adaptive Random Forest (ARF) combined with the Hoeffding Adaptive Tree (HAT) algorithm and the homogeneous ensemble HAT made up of 10 models achieved higher detection rates of 96.84% and 97.2 %, respectively. The above models are efficient and effective in dealing with concept drift, while taking into account the resource constraints of WSNs.

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Section: Related Workmentioning

confidence: 99%

An Online Ensemble Learning Model for Detecting Attacks in Wireless Sensor Networks

Tabbaa¹,

Ifzarne²,

Hafidi³

2022

Preprint

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“…In 2020, Kumari and Mehta [65], suggested a hybrid classification method for IDS. The hybrid is a combination between Decision Tree J48 and Support Vector Machine (SVM).…”

Section: Review Of Classification Algorithms For Idsmentioning

confidence: 99%

Evaluation of Classification Algorithms for Intrusion Detection System: A Review

Salih

Abdulazeez

2021

JSCDM

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Intrusion detection is one of the most critical network security problems in the technology world. Machine learning techniques are being implemented to improve the Intrusion Detection System (IDS). In order to enhance the performance of IDS, different classification algorithms are applied to detect various types of attacks. Choosing a suitable classification algorithm for building IDS is not an easy task. The best method is to test the performance of the different classification algorithms. This paper aims to present the result of evaluating different classification algorithms to build an IDS model in terms of confusion matrix, accuracy, recall, precision, f-score, specificity and sensitivity. Nevertheless, most researchers have focused on the confusion matrix and accuracy metric as measurements of classification performance. It also provides a detailed comparison with the dataset, data preprocessing, number of features selected, feature selection technique, classification algorithms, and evaluation performance of algorithms described in the intrusion detection system.

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“…An ensemble-based model for intrusion detection was established in [28] using multiple ML techniques of classification such as DT, J48 and SVM. Particle swarm optimization was used for selecting nine most relevant and important features in KDD99 dataset of intrusion detection.…”

Section: Literature Reviewmentioning

confidence: 99%

A New Ensemble-Based Intrusion Detection System for Internet of Things

et al. 2021

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The domain of Internet of Things (IoT) has witnessed immense adaptability over the last few years by drastically transforming human lives to automate their ordinary daily tasks. This is achieved by interconnecting heterogeneous physical devices with different functionalities. Consequently, the rate of cyber threats has also been raised with the expansion of IoT networks which puts data integrity and stability on stake. In order to secure data from misuse and unusual attempts, several intrusion detection systems (IDSs) have been proposed to detect the malicious activities on the basis of predefined attack patterns. The rapid increase in such kind of attacks requires improvements in the existing IDS. Machine learning has become the key solution to improve intrusion detection systems. In this study, an ensemble-based intrusion detection model has been proposed. In the proposed model, logistic regression, naive Bayes, and decision tree have been deployed with voting classifier after analyzing model’s performance with some prominent existing state-of-the-art techniques. Moreover, the effectiveness of the proposed model has been analyzed using CICIDS2017 dataset. The results illustrate significant improvement in terms of accuracy as compared to existing models in terms of both binary and multi-class classification scenarios.

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A Hybrid Intrusion Detection System Based on Decision Tree and Support Vector Machine

Cited by 24 publications

References 19 publications

An Online Ensemble Learning Model for Detecting Attacks in Wireless Sensor Networks

An Online Ensemble Learning Model for Detecting Attacks in Wireless Sensor Networks

Evaluation of Classification Algorithms for Intrusion Detection System: A Review

A New Ensemble-Based Intrusion Detection System for Internet of Things

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