Ayaz Isazadeh

Programs are under continuous attack for disclosing secret information, and defending against these attacks is becoming increasingly vital. An attractive approach for protection is to measure the amount of secret information that might leak to attackers. A fundamental issue in computing information leakage is that given a program and attackers with various knowledge of the secret information, what is the maximum amount of leakage of the program? This is called channel capacity. In this paper, two notions of capacity are defined for concurrent probabilistic programs using information theory. These definitions consider intermediate leakage and the scheduler effect. These capacities are computed by a constrained nonlinear optimization problem. Therefore, an evolutionary algorithm is proposed to compute the capacities. Single preference voting and dining cryptographers protocols are analyzed as case studies to show how the proposed approach can automatically compute the capacities. The results demonstrate that there are attackers who can learn the whole secret of both the single preference protocol and dining cryptographers protocol. The proposed evolutionary algorithm is a general approach for computing any type of capacity in any kind of program.

Preserving the confidentiality of information is a growing concern in software development. Secure information flow is intended to maintain the confidentiality of sensitive information by preventing them from flowing to attackers. This paper discusses how to ensure confidentiality for multi-threaded programs through a property called observational determinism. Operational semantics of multi-threaded programs are modeled using Kripke structures. Observational determinism is formalized in terms of divergence weak low-bisimulation. Bisimulation is an equivalence relation associating executions that simulate each other. The new property is called bisimulation-based observational determinism. Furthermore, a model checking method is proposed to verify the new property and ensure that secure information flow holds in a multi-threaded program. The model checking method successively refines the Kripke model of the program until the quotient of the model with respect to divergence weak low-bisimulation is reached. Then, bisimulation-based observational determinism is checked on the quotient, which is a minimized model of the concrete Kripke model. The time complexity of the proposed method is polynomial in the size of the Kripke model. The proposed approach has been implemented on top of PRISM, a probabilistic model checking tool. Finally, a case study is discussed to show the applicability of the proposed approach.

Data clustering is one of the data mining techniques which is widely used in some applications, such as pattern recognition, machine learning, image processing, etc. Swarm intelligence-based algorithms are extensively used in data clustering in recent years. Cuckoo Search (CS) is one of the recently proposed algorithms in the category of swarm intelligence-based techniques. In this paper, a new hybrid algorithm which utilises CS, Particle Swarm Optimisation (PSO) and k-means has been proposed (HCSPSO). The proposed algorithm employed PSO and k-means to produce new nests in standard CS to obtain better results. It also benefits from Mantegna levy distribution to obtain higher convergence speed as well as local search. To eliminate the problem of the high number of functional evaluations in standard CS, a fraction of nests has assigned to every section of the algorithm. The proposed algorithm's performance was evaluated by ten standard benchmark datasets. Evaluation results show that the proposed algorithm is an efficient method for data clustering and produces more optimised results in comparison with standard CS, PSO, Elephant Search Algorithm (ESA), Enhanced Bat Algorithm (EBA), Bird Flock Gravitational Search Algorithm (BFGSA), Improved Cuckoo Search (ICS) and k-means.

One way to speed up the execution of sequential programs is to divide them into concurrent segments and execute such segments in a parallel manner over a distributed computing environment. We argue that the execution speedup primarily depends on the concurrency degree between the identified segments as well as communication overhead between the segments. To guarantee the best speedup, we have to obtain the maximum possible concurrency degree between the identified segments, taking communication overhead into consideration. Existing code distributor and multi-threading approaches do not fulfill such requirements; hence, they cannot provide expected distributability gains in advance. To overcome such limitations, we propose a novel approach for verifying the distributability of sequential object-oriented programs. The proposed approach enables users to see the maximum speedup gains before the actual distributability implementations, as it computes an objective function which is used to measure different distribution values from the same program, taking into consideration both remote and sequential calls. Experimental results showed that the proposed approach successfully determines the distributability of different real-life software applications compared with their real-life sequential and distributed implementations.