Sardar Jaf

Security threats to critical infrastructure: the human factor

In the twenty-first century, globalisation made corporate boundaries invisible and difficult to manage. This new macroeconomic transformation caused by globalisation introduced new challenges for critical infrastructure management. By replacing manual tasks with automated decision making and sophisticated technology, no doubt we feel much more secure than half a century ago. As the technological advancement takes root, so does the maturity of security threats. It is common that today’s critical infrastructures are operated by non-computer experts, e.g. nurses in health care, soldiers in military or firefighters in emergency services. In such challenging applications, protecting against insider attacks is often neither feasible nor economically possible, but these threats can be managed using suitable risk management strategies. Security technologies, e.g. firewalls, help protect data assets and computer systems against unauthorised entry. However, one area which is often largely ignored is the human factor of system security. Through social engineering techniques, malicious attackers are able to breach organisational security via people interactions. This paper presents a security awareness training framework, which can be used to train operators of critical infrastructure, on various social engineering security threats such as spear phishing, baiting, pretexting, among others.

Optimize BpNN Using New Breeder Genetic Algorithm

In this paper, the ability of genetic algorithms in designing artificial neural network (ANN) is investigated. The multi-layer network (MLN) is taken into account as the ANN structure to be optimized. The idea presented here is to use the genetic algorithms to yield contemporaneously the optimization of: (1) the design of NN architecture in terms of number of hidden layers and of number of neurons in each layer; and (2) the choice of the best parameters (learning rate, momentum term, activation functions, and order of training patterns) for the effective solution of the actual problem to be faced. The back-propagation (BP) algorithm, which is one of the best-known training methods for ANNs, is used. To verify the efficiency of the current scheme, a new version of the breeder genetic algorithm (NBGA) is proposed and used for the automatic synthesis of NN. Finally, several problems of the experiment were taken and the results show that the back-propagation neural network (BpNN) classifier improved the current scheme has higher accuracy of classification and greater gradient of convergence than other classifiers, which have been proposed in the literature.

A Hybrid Approach to Parsing Natural Languages

Ambiguities in natural languages make processing (parsing) them a difficult task. Parsing is even more difficult when dealing with a structurally complex natural language such as Arabic. In this paper, we briefly highlight some of the complex structure of Arabic, and we identify different parsing approaches and briefly discuss their limitations. Our goal is to produce a hybrid parser, by combining different parsing approaches, which retains the advantages of data-driven approaches but is guided by a set of grammatical rules to produce more accurate results. We describe a novel technique for directly combining different parsing approaches. Results for our initial experiments that we have conducted in this work, and our plans for future work are also presented.