Altyeb Altaher

Malaysian Internet Surfing Addiction (MISA): Factors affecting the Internet use and its consequences

Internet addiction provoked as a serious mental health issue in the recent decade. In the recent times, Internet addiction had become a global concern to the human society. As the electronic world has become more and more common to the young, younger generations are alleged to be susceptible to the risk of Internet addiction. This research outlines a method to examine the addictive level to the Malaysians Internet surfing. The purpose of our study is to identify the Internet use among Malaysians and the relation of excessive use to the addictive level. We propose and devise an online survey to study the Internet use among the younger generations. The questionnaire consists of five subsections; such as the relevant demographic questions, the diagnosis of the Internet addiction level, related questions on the Internet surfing behavior, the reasons of excessive Internet usage, and the consequences of the addictive behavior. Participants of the online survey were selected randomly from Malaysia, ranging from the age of 7-year old to the age of 30-year old. We found that the Internet use of the younger generations was susceptible to the Internet addiction. The findings reflect that younger generations are vulnerable to the fantasized world.

Malware detection based on evolving clustering method for classification

Malware is a computer program that can replicate itself and cause potential damage in data files. The high speed of the computers and networks increased the virus spread. To avoid the virus infection and the data loss, it is important to use an efficient and effective method for virus detection. This paper proposes an approach for malware detection based on the evolving clustering method. The proposed approach effectively combined the information gain method as a feature selector with the evolving clustering method as evolving learning classifier. Based on the experimental results, the proposed malware detection approach proved its capability to detect the malware by decreasing the false positive rate to 1% while increasing the level of accuracy to 99%.

Real time network anomaly detection using relative entropy

As the computer networks continue to increase in size, complexity and importance, the network security issue becomes more and more important. In this paper, we propose a real time anomaly detection system based on relative entropy. The proposed system captures the network traffic packets and then uses relative entropy and adaptive filter to dynamically determine the traffic changes and to examine whether the traffic change is normal or contains anomaly. Our experimental results show that the proposed system is efficient for on-line anomaly detection, using traffic trace collected in high-speed links.

On-line anomaly detection based on relative entropy

Because the internet and computer networks are exposed to rapidly increasing number of serious security threats, efficient and effective anomaly detection techniques have become a necessity to secure the internet and computer networks. Traditional signature based anomaly detection techniques failed to detect polymorphic and new security threats. In this paper, we propose an online worm detection system based on relative entropy. The system effectively profiles network traffic features and then uses relative entropy to dynamically determine the traffic changes. It then applies adaptive filter to differentiate the traffic changes and determines whether the traffic is normal or contains worms. Our experimental results show that the proposed system is efficient for on-line anomaly detection, using traffic trace collected in high-speed links.

A dual stack IPv4/IPv6 testbed for malware detection in IPv6 networks

The exhaustion of IPv4 addresses on November 2011 has made the future of the internet in the IPv6 and raised new challenges in the network security research. This paper proposed a dual stack ipv4/ipv6 network testbed for dealing with the designation and implementation of an intelligent approach for malware detection in IPv6 networks. All the equipments, tools and network are configured based on real implementation of a dual stack ip4/ipv6 network. With fully functional operation for handling basic transition between IPv6 clients over IPv4 networks, the dual stack IPV4/IPv6 testbed is suitable for investigating the malware detection in real ipv6 networks. The experimental results from the testing phase show the efficiency and the functionality of the dual stack IPv4/IPv6 testbed.