Israr Ullah

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



Research Interests: Data Structures and Algorithms, Analysis of Algorithms, Combinatorial Optimization, Models of Computation


Israr Ullah has completed PhD in Computer Engineering from Jeju National University, South Korea in February 2019. He completed his M.S. in Computer Science from National University of Computer and Emerging Sciences (NUCES), Islamabad, Pakistan in 2009. He is serving as Assistant Professor of Computer Science at Virtual University of Pakistan. His research is mainly focused on development of AI based IoT solutions for smart cities. He has experience in the field of Network Simulation and Modeling. His research interests also include Designing and Analysis of Optimization Algorithms using AI techniques.

Author Articles
A Feature Selection based Ensemble Classification Framework for Software Defect Prediction

By Ahmed Iqbal Shabib Aftab Israr Ullah Muhammad Salman Bashir Muhammad Anwaar Saeed

DOI:, Pub. Date: 8 Sep. 2019

Software defect prediction is one of the emerging research areas of software engineering. The prediction of defects at early stage of development process can produce high quality software at lower cost. This research contributes by presenting a feature selection based ensemble classification framework which consists of four stages: 1) Dataset selection, 2) Feature Selection, 3) Classification, and 4) Results. The proposed framework is implemented from two dimensions, one with feature selection and second without feature selection. The performance is evaluated through various measures including: Precision, Recall, F-measure, Accuracy, MCC and ROC. 12 Cleaned publically available NASA datasets are used for experiments. The results of both the dimensions of proposed framework are compared with the other widely used classification techniques such as: “Naïve Bayes (NB), Multi-Layer Perceptron (MLP). Radial Basis Function (RBF), Support Vector Machine (SVM), K Nearest Neighbor (KNN), kStar (K*), One Rule (OneR), PART, Decision Tree (DT), and Random Forest (RF)”. Results reflect that the proposed framework outperformed other classification techniques in some of the used datasets however class imbalance issue could not be fully resolved.

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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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