A classification framework to detect DoS attacks

A classification framework to detect DoS attacks

Автор: Ahmed Iqbal, Shabib Aftab, Israr Ullah, Muhammad Anwaar Saeed, Arif Husen

Журнал: International Journal of Computer Network and Information Security @ijcnis

Статья в выпуске: 9 vol.11, 2019 года.

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The exponent increase in the use of online information systems triggered the demand of secure networks so that any intrusion can be detected and aborted. Intrusion detection is considered as one of the emerging research areas now days. This paper presents a machine learning based classification framework to detect the Denial of Service (DoS) attacks. The framework consists of five stages, including: 1) selection of the relevant Dataset, 2) Data pre-processing, 3) Feature Selection, 4) Detection, and 5) reflection of Results. The feature selection stage incudes the Decision Tree (DT) classifier as subset evaluator with four well known selection techniques including: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Best First (BF), and Rank Search (RS). Moreover, for detection, Decision Tree (DT) is used with bagging technique. Proposed framework is compared with 10 widely used classification techniques including Naïve Bayes (NB), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), K-Nearest Neighbor (kNN), Decision Tree (DT), Radial Basis Function (RBF), One Rule (OneR), PART, Bayesian Network (BN) and Random Tree (RT). A part of NSL-KDD dataset related to Denial of Service attack is used for experiments and performance is evaluated by using various accuracy measures including: Precision, Recall, F measure, FP rate, Accuracy, MCC, and ROC. The results reflected that the proposed framework outperformed all other classifiers.

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Intrusion Detection, DoS Attacks, Denial of Service Attacks, Network Intrusion Detection, Machine learning, Classification, Feature Selection

Короткий адрес: https://sciup.org/15015713

IDR: 15015713   |   DOI: 10.5815/ijcnis.2019.09.05

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