Malaysian Journal of Computer Science 

About: Malaysian Journal of Computer Science is an academic journal published by University of Malaya. The journal publishes majorly in the area(s): Computer science & Artificial neural network. It has an ISSN identifier of 0127-9084. Over the lifetime, 499 publications have been published receiving 3300 citations.

Intelligent traffic lights control by fuzzy logic

Abstract: Describes the design and implementation of an intelligent traffic lights controller based on fuzzy logic technology. A software has been developed to simulate the situation of an isolated traffic junction based on this technology. It is highly graphical in nature, uses the Windows system and allows simulation of different traffic conditions at the logic controller and a conventional fixed-time controller. Simulation results show that the fuzzy logic controller has better performance and is more cost effective.

Webuse: website usability evaluation tool

Abstract: Usability is one of the major factors that determines the successfulness of a website. It is important therefore to have certain measurement methods to assess the usability of websites. The methods could be used to help website designers make their websites more usable. This research focuses on website usability issues and implements a tool for evaluating the usability of websites, called WEBUSE (WEBsite USability Evaluation Tool). Based on literature research, a 24-question evaluation questionnaire has been formulated. The questionnaire is implemented as a Web-based tool. Visitors’ of a website can use it to evaluate the usability of the website. The visitors’ responses to the questionnaire are analysed. The results of the analysis show the good and bad usability aspects of the website. Website designers and developers can improve their websites based on these results. WEBUSE is suitable for the evaluation of all types of websites. Evaluation provided by WEBUSE is reliable and has received favourable user satisfaction and acceptance.

A Study Of Machine Learning Classifiers for Anomaly-Based Mobile Botnet Detection

Abstract: In recent years, mobile devices are ubiquitous. They are employed for purposes beyond merely making phone calls. Among the mobile operating systems, Android is the most popular due to its availability as an open source operating system. Due to the proliferation of Android malwares, it is crucial to study the best classifiers that can detect these malwares effectively and accurately through selecting the most suitable network traffic features as well as comprehensive comparison with related works. This study evaluates five machine learning classifiers, namely Naive Bayes, k-nearest neighbour, decision tree, multi-layer perceptron, and support vector machine. The evaluation was validated using malware data samples from the Android Malware Genome Project. The data sample is a collection of malwares gathered between August 2010 and October 2011 by the University of North Carolina. Among various network traffic characteristics, three network features were selected: connection duration, TCP size and number of GET/POST parameters. From the experiment, it is found that knearest neighbour provides the optimum results in terms of performance among the classifiers. The experimental results also indicate a true positive rate as high as 99.94% and false positive of 0.06% for the knearest neighbour classifier.

Identifying False Alarm for Network Intrusion Detection System Using Hybrid Data Mining and Decision Tree

Abstract: Although intelligent intrusion and detection strategies are used to detect any false alarms within the network critical segments of network infrastructures, reducing false positives is still a major challenge. Up to this moment, these strategies focus on either detection or response features, but often lack of having both features together. Without considering those features together, intrusion detection systems probably will not be able to highly detect on low false alarm rates. To offset the abovementioned constraints, this paper proposes a strategy to focus on detection involving statistical analysis of both attack and normal traffics based on the training data of KDD Cup 99. This strategy also includes a hybrid statistical approach which uses Data Mining and Decision Tree Classification. As a result, the statistical analysis can be manipulated to reduce misclassification of false positives and distinguish between attacks and false positives for the data of KDD Cup 99. Therefore, this strategy can be used to evaluate and enhance the capability of the IDS to detect and at the same time to respond to the threats and benign traffic in critical segments of network, application and database infrastructures.

Scheduling framework for bandwidth-aware job grouping-based scheduling in grid computing

Abstract: In recent years, Grid computing has emerged as an evolution from the existing distributed computing systems for delivering information, resources and services to users. This new computational infrastructure offers a remarkable increase in the number of available computation capabilities that can be delivered to applications. Grid computing is increasingly being used for aggregating resources across different geographical places. Therefore, job scheduling in the Grid computing environment has posed new challenges which demand for better computing performance and well-improved utilization of resources. This paper investigates the use of bandwidth-awareness in a scheduling framework to enhance the performance of job scheduling.