Salman Yussof

A routing protocol based on trusted and shortest path selection for mobile ad hoc network

A mobile ad-hoc network (MANET) is a peer-to-peer wireless network where nodes can communicate with each other without the use of infrastructure such as access points or base stations. Nodes can join and leave the network at anytime and are free to move randomly and organize themselves arbitrarily. Due to this nature of MANET, it is possible that there could be some malicious and selfish nodes that try compromise the routing protocol functionality and makes MANET vulnerable to security attacks. In this paper, we present a security-enhanced AODV (Ad hoc On-demand Distance Vector Routing) routing protocol called R-AODV (Reliant Ad hoc On-demand Distance Vector Routing). The implementation of this work is done by modified a trust mechanism known as direct and recommendations trust model and then incorporating it inside AODV which will allow AODV to not just find the shortest path, but instead to find a short path that can be trusted. This enhances security by ensuring that data does not go through malicious nodes that have been known to misbehave. The R-AODV protocol has been implemented and simulated on NS-2. Based on the simulation result, it can be shown that R-AODV does provide a more reliable data transfer compared to the normal AODV if there are malicious nodes in the MANET.

A Coarse-Grained Parallel Genetic Algorithm with Migration for Shortest Path Routing Problem

Shortest path routing is the type of routing widely used in computer networks nowadays. Even though shortest path routing algorithms are well established, other alternative methods may have their own advantages. One such alternative is to use a GA-based routing algorithm. Based on previous research, GA-based routing algorithm has been found to be more scalable and insensitive to variations in network topologies. However, it is also known that GA-based routing algorithm is not fast enough for real-time computation. In this paper, we proposed a coarse-grained parallel genetic algorithm for solving the shortest path routing problem with the aim to reduce its computation time. The migration scheme, which is commonly used in coarse-grained parallel genetic algorithm, is also employed in the proposed algorithm. This algorithm is developed and run on an MPI cluster. This paper studies the effect of migration on the proposed algorithm and the performance of the algorithm as compared to its serial counterpart.

A Parallel Genetic Algorithm for Shortest Path Routing Problem

Shortest path routing is the type of routing widely used in computer networks nowadays. Even though shortest path routing algorithms are well established, other alternative methods may have their own advantages. One such alternative is to use a GA-based routing algorithm. Based on previous research, GA-based routing algorithm has been found to be more scalable and insensitive to variations in network topologies. However, it is also known that GA-based routing algorithm is not fast enough for real-time computation. In this paper, we proposed a parallel genetic algorithm for solving the shortest path routing problem with the aim to reduce its computation time. This algorithm is developed and run on an MPI cluster. Based on experimental result, there is a tradeoff between computation time and the result accuracy. However, for the same level of accuracy, the proposed parallel algorithm can perform much faster compared to its non-parallel counterpart.

A Robust GA-based QoS Routing Algorithm for Solving Multi-constrained Path Problem

To support networked multimedia applications, it is important for a network to provide guaranteed quality-of-service (QoS). One way to provide such services is for the network to perform QoS routing, where the path taken must fulfill certain constraints. Multi-constrained path (MCP) problem refers to the problem of finding a path through a network subject to multiple additive constraints. It has been proven that this problem is NP-complete and finding an exact solution can be difficult. As such, various heuristics and approximation algorithms have been proposed to solve the MCP problem. However, the actual link metrics in a QoS-aware network is dynamic and may continuously change over time and since the path given by the routing algorithm is computed using the state information available to the router, which may or may not be up-to-date, it is possible that a feasible path returned by the algorithm may turn out to be no longer valid. This paper presents a GA-based QoS routing algorithm for solving the general k -constrained problem which has the capability to return multiple feasible paths in a single run. This makes the algorithm more robust in the case that the rate of change of state information in the network is higher than the rate of state information received by the router. Simulation results show that this algorithm consistently achieve higher feasibility ratio relative to existing well-known MCP routing algorithms when state information in the router lags behind the network.

Algorithm for Robot Writing Using Character Segmentation

Currently, there are many ongoing researches that are targeted at making robots more human-like. One of the tasks that can be done by humans easily but is difficult to be done by robots is writing. In this paper, we are presenting a flexible algorithm that can allow a robot to write. This algorithm is based on character segmentation, where the main idea is to store character information as segments and the segment information can then be used by the robot to write. We have also developed a sample application using the proposed algorithm to allow a Mitsubishi RV-2AJ robotic arm to write English characters and numbers. Through our experiment, it has been proven that the algorithm developed is able to allow the robotic arm to write

Integration of multiple soft biometrics for human identification

A new local feature descriptor is proposed for facial shape representation.The performance on still and surveillance face datasets is comparable to the state of the arts.We present experimental findings on integration of face and body based soft biometrics.Five score fusion techniques are examined to determine the most reliable method.Fuzzy logic is discovered as the most effective score fusion. We propose a computational approach to human identification based on the integration of face and body related soft biometric traits. In previous studies on soft biometrics, several methods for human identification using semantic descriptions have been introduced. Though the results attained exhibit the effectiveness of such techniques in image retrieval and short term tracking of subjects, semantics literally limits the ability of a biometric system to provide conclusive identification. This paper presents a new framework for biometric identification based solely on multiple measured soft biometric traits. The paper describes techniques for extracting/estimating face and body based soft biometric traits from frame set. Furthermore, we utilized a sequential attribute combination method to perform attribute selection prior to integration at match score level. Finally, an evaluation of five score fusion techniques is performed. The results show that the proposed framework can be utilized to model an adequate soft biometric system with rank-1 identification rate of 88%. Display Omitted

Online Handwritten Signature Verification Using Neural Network Classifier Based on Principal Component Analysis

One of the main difficulties in designing online signature verification (OSV) system is to find the most distinctive features with high discriminating capabilities for the verification, particularly, with regard to the high variability which is inherent in genuine handwritten signatures, coupled with the possibility of skilled forgeries having close resemblance to the original counterparts. In this paper, we proposed a systematic approach to online signature verification through the use of multilayer perceptron (MLP) on a subset of principal component analysis (PCA) features. The proposed approach illustrates a feature selection technique on the usually discarded information from PCA computation, which can be significant in attaining reduced error rates. The experiment is performed using 4000 signature samples from SIGMA database, which yielded a false acceptance rate (FAR) of 7.4% and a false rejection rate (FRR) of 6.4%.

Recent advances in facial soft biometrics

Face as a biometric attribute has been extensively studied over the past few decades. Even though, satisfactory results are already achieved in controlled environments, the practicality of face recognition in realistic scenarios is still limited by several challenges, such as, expression, pose, occlusion, etc. Recently, the research direction is concentrating on the prospects of complementing face recognition systems with facial soft biometric traits. The ease of extracting facial soft biometrics under several varying conditions has mainly resulted in the ability of using the traits to, either improve the performance of traditional face recognition systems, or performing recognition solely based on many facial soft biometrics. This paper presents state-of-the-art techniques in facial soft biometrics research by describing the type of traits, feature extraction methods, and the application domains. It indicates the most recent and valuable results attained, while also highlighting some possible future scientific research directions to be investigated.

A review of security attacks on IEC61850 substation automation system network

Although IEC61850 substations can provide various advantages over traditional substations, the power supplier companies are being cautious about its implementation due to security concerns. Indeed, researchers have identified a number of security vulnerabilities and weaknesses in the IEC61850 standard such as the lack of encryption used in the GOOSE messages, lack of intrusion detection system implementation in IEC61850 network, and no firewall implementation inside IEC61850 substation network. By exploiting these vulnerabilities, researchers have also discovered a number of security attacks that can be launched on IEC61850 substation network. These attacks can be categorized into two, which are common network security attacks on IEC61850 network and security attacks that exploit the IEC61850 multicast messages. This paper provides a review of these security attacks in terms of how the attacks are conducted and the subsequent damages that they may cause.

A novel noncooperative game competing model using generalized simple additive weighting method to perform network selection in heterogeneous wireless networks

Network selection mechanisms have a significant role in guaranteeing the QoS for users in a heterogeneous wireless networks environment. These mechanisms allow the selection of an optimal wireless network to satisfy the needs of users. Users are provided with the opportunity to select from multiple connectivity opportunities available all over various wireless networks. Furthermore, the network operators themselves can execute active selection strategies that facilitate proper decision making, in which user preferences are considered. This study proposes a new noncooperative competing game-theoretic model and strategy space based on user preference. This model can solve network selection problems and capture the inter-linkages of decisions taken by various networks. A generalized simple additive weighting method is incorporated into the framework of noncooperative game theory. In addition, the utility function is employed to assess the usefulness of the system. Simulation results and analysis illustrate the efficacy of the suggested model in attaining optimum network utility for heterogeneous wireless networks while optimizing user satisfaction. Copyright