Arif Husen

Work place: Department of Computer Science, Virtual University of Pakistan



Research Interests: Applied computer science, Computer systems and computational processes, Computer Architecture and Organization, Computer Networks, Theoretical Computer Science


Arif H. Rashid completed MS degree in computer sciences from Virtual University of Pakistan. He has vast experience in telecommunication industry in Middle East and led numerous multivendor projects related to transformation of networks to next generation technologies. He is currently working as instructor Computer Science at Virtual University of Pakistan.

Author Articles
A Classification Framework to Detect DoS Attacks

By Ahmed Iqbal Shabib Aftab Israr Ullah Muhammad Anwaar Saeed Arif Husen

DOI:, Pub. Date: 8 Sep. 2019

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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