The aim is to develop a suitable method for quantifying security. We use stochastic modeling techniques for this purpose. An intrusion process is considered as a series of elementary attack phases and at each phase the interactions between the attacker and the system are analyzed rigorously. It is assumed that a typical attacker needs some time to perform an elementary attack phase. On the other hand, it is assumed that the attacker may be detected by the system and thus the overall intrusion process is interrupted. The attacker skill level and the system's abilities are characterized by the uniform distribution functions assigned to the transitions of the model. The underlying stochastic model is recognized as a semiMarkov chain. For security analysis, some valid assumptions about intrusion process are considered. Also, two quantitative security measures are defined and evaluated based on the model. The proposed method is demonstrated by modeling a complicated attack process and evaluating the desired security measures.

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Security quantification is a topic that has gained a lot of interest in the research community during the recent years. In this paper, a new method is proposed for modeling and quantifying attack effects on a computer system. In this work, intrusion process is considered as atomic sequential steps. Each atomic step changes the current system state. On the other hand, system tries to prevent and detect the attacker activity and therefore can transfer the current system state to a secure state. Intrusion process modeling is done by a semi-Markov chain (SMC). Distribution functions assigned to SMC transitions are uniform distributions. Uniform distributions represent the sojourn time of the attacker or the system in the transient states. Then the SMC is converted into a discrete-time Markov chain (DTMC). The DTMC is analyzed and then the probability of attacker success is computed based on mathematical theorems. The SMC has two absorbing for representing success and failure states of intrusion process.

Cyber-Physical Systems (CPSs) are increasingly used in various safety-critical domains; assuring the safety of these systems is of paramount importance. Fault Injection is known as an effective testing method for analyzing the safety of CPSs. However, the total number of faults to be injected in a CPS to explore the entire fault space is normally large and the limited budget for testing forces testers to limit the number of faults injected by e.g., random sampling of the space. In this paper, we propose DELFASE as an automated solution for fault space exploration that relies on Generative Adversarial Networks (GANs) for optimizing the identification of critical faults, and can run in two modes: active and passive. In the active mode, an active learning technique called ranked batch-mode sampling is used to select faults for training the GAN model with, while in the passive mode those faults are selected randomly. The results of our experiments on an adaptive cruise control system show that compared to random sampling, DELFASE is significantly more effective in revealing system weaknesses. In fact, we observed that compared to random sampling that resulted in a fault coverage of around 10%, when using the active and passive modes, the fault coverage of DELFASE could be as high as 89% and 81%, respectively.