Biblio

The Compressive Sensing (CS) has wide range of applications in various domains. The sampling of sparse signal, which is periodic or aperiodic in nature, is still an out of focus topic. This paper proposes novel Sparse Spasmodic Sampling (SSS) techniques for different sparse signal in original domain. The SSS techniques are proposed to overcome the drawback of the existing CS sampling techniques, which can sample any sparse signal efficiently and also find location of non-zero components in signals. First, Sparse Spasmodic Sampling model-1 (SSS-1) which samples random points and also include non-zero components is proposed. Another sampling technique, Sparse Spasmodic Sampling model-2 (SSS-2) has the same working principle as model-1 with some advancements in design. It samples equi-distance points unlike SSS-1. It is demonstrated that, using any sampling technique, the signal is able to reconstruct with a reconstruction algorithm with a smaller number of measurements. Simulation results are provided to demonstrate the effectiveness of the proposed sampling techniques.

Traditional risk assessment process based on knowledge of threat occurrence probability against every system’s asset. One should consider asset placement, applied security measures on asset and network levels, adversary capabilities and so on: all of that has significant influence on probability value. We can measure threat probability by modelling complex attack process. Such process requires creating an attack tree, which consist of elementary attacks against different assets and relations between elementary attacks and impact on influenced assets. However, different attack path may lead to targeted impact – so task of finding optimal attack chain on a given system topology emerges. In this paper method for complex attack graph creation presented, allowing automatic building various attack scenarios for a given system. Assuming that exploits of particular vulnerabilities represent by independent events, we can compute the overall success probability of a complex attack as the product of the success probabilities of exploiting individual vulnerabilities. This assumption makes it possible to use algorithms for finding the shortest paths on a directed graph to find the optimal chain of attacks for a given adversary’s target.