Azizul Rahman Mohd Shariff

A Delay Constrained Minimum Hop Distributed Routing Algorithm using Adaptive Path Prediction

We propose a new delay-constrained minimumhop distributed routing algorithm (DCMH) for unicastrouting. DCMH uses a novel path feasibility prediction (PFP) model that is able to predict the feasibility of a tentative routing path, and employs a crankback facility. It is a hybrid algorithm where it uniquely integrates the use of local-state and the global binary connectivity information maintained at each node to compute routing paths. Simulation results show that average message overhead is modest with good connection success and low nodal storage.

A distributed algorithm for unicast QoS-routing using path feasibility prediction

We propose a new delay-constrained distributed algorithm (DCDRA) for unicast-routing. DCDRA uses a novel path feasibility predictor (PFP) inequality model that is able to predict the feasibility of a tentative routing path, and employs a crankback facility. It is a hybrid distributed algorithm where it uniquely integrates the use of local-link state and the global binary connectivity information maintained at each node to compute routing paths. Simulation results show that average message overhead is modest with good connection success rates

User Behavior Based Call Admission Control for Traffic Steering in Wi-Fi/Cellular Networks

The expanded growth in smartphones and applications, hence data traffic resulted in cellular networks being conspicuously overwhelmed with demand for resources. Service providers constantly looking for suitable technologies and mechanisms to reduce the network congestion by offloading the data traffic, where Wi-Fi is an excellent option. In the traffic steering process, network selection is the most vital process, as it affects the user’s network experience. This paper focuses on enhancing the procedure of Call Admission Control (CAC). Two key functions were introduced into the call admission procedure, namely Channel Reserve Engine (CRE) and Traffic Steering Policy (TSP). Based on the simulation results, it is found the proposed CAC reduces the call blocking probability as compared with conventional CAC. Thus, it is suitable to be implemented in WLAN/Cellular integration.

A Time Gap Interval for Safe Following Distance (TGFD) in Avoiding Car Collision in Wireless Vehicular Networks (VANET) Environment

Driving is an intense and dynamic cooperation between human and vehicles. Drivers control the vehicle directly or supervise the situation according to information displayed on the instrument panel. Different from the time-headway and traditional braking models, this paper aims to propose a new safety indicator called time gap interval for safe following distance (TGFD) which incorporates vehicle dynamics and driver behavior factors that includes the time component to broadcast and propagate suitable safety messages in vehicular ad hoc network (VANET) environment. This simulation-based safety indicator could analyze the capability of a vehicle in a car-following situation to safely stop without hitting the vehicle in front when an emergency brake is applied by considering the proper reaction time of the driver of the following. Results from this simulation study indicate that the TGFD is more comprehensive safety indicator for safety analysis.

APTGuard : Advanced Persistent Threat (APT) Detections and Predictions using Android Smartphone

Advanced Persistent Threat (APT) is an attack aim to damage the system’s data from the aspect of confidentiality and integrity. APT attack has several variants of attacks such social engineering techniques via spear phishing, watering hole and whaling. APTGuard exhibits the ability to predict spear phishing URLs accurately using ensemble learning that combines decision tree and neural network. The URL is obtained from the SMS content received on the smart phones and sent to the server for filtering, classifying, logging and finally informing the administrator of the classification outcome. APTGuard can predict and detect APT from spear phishing but it does not have the ability of automated intervention on the user receiving the spear phishing URL. As a result, APTGuard is capable to extract the features of the URL and then classify it accordingly using ensemble learner which combines decision tree and neural network accurately.

An Authentication Technique: Behavioral Data Profiling on Smart Phones

Mobile devices have become an indispensable component in modern society. Many of these devices rely on personal identification numbers (PIN) as a form of user authentication. One of the main concerns in the use of mobile devices is the possibility of a breach in security and privacy if the device is seized by an outside party. Threats can possibly come from friends as well as strangers. Smart devices can be easily lost due to their small size, thereby exposing details of users’ private lives. User behavior authentication is designed to overcome this problem by utilizing user behavioral techniques to continuously assess user identity. This study proposed a behavioral data profiling technique that utilizes data collected from the user behavior application to verify the identity of the user in a continuous manner. By utilizing a combination of analytical hierarchy process and correlation coefficient method, the best experimental results were obtained by verifying the identity of six types of user behaviors to determine the different behaviors. Based on the results, this study proposes a new authentication technique that enables verification of a user’s identity through their application usage in a transparent manner. Behavioral data profiling is designed in a modular manner that will not reject user access based on a single application activity but on several consecutive abnormal application usages to balance the trade-off between security and usability. The proposed framework is evaluated using a PIN-based technique and achieved an overall 95% confidence level. Behavioral data profiling provides a significant improvement in the security afforded to the device and user convenience.

A combination of mobility, load and services for network selection in heterogenous wireless network

WLAN has been considered as a compliment technology for cellular network in the heterogeneous wireless network. Thus, seamless connection between these technologies play important role as the performance indicator. In this paper, we consider the users velocity and channel holding time to predict the users dwelling time in the WLAN area to assist in the initial network selection. Based on the mobility prediction, network load and type of services, the proposed algorithm will select the most suitable radio access network for the user. In addition, we also incorporate the traffic offloading method in order to accept as many users to the system while maintaining the quality of service of the users. Through extensive discrete-event simulation, the results show that the proposed solution has better performance as compared to the location-based (WLAN First) and mobility threshold proposals.