Biblio

Two known shortcomings of standard techniques in formal verification are the limited capability to provide system-level assertions, and the scalability to large, complex models, such as those needed in Cyber-Physical Systems (CPS) applications. Leveraging data, which nowadays is becoming ever more accessible, has the potential to mitigate such limitations. However, this leads to a lack of formal proofs that are needed for modern safety-critical systems. This contribution presents a research initiative that addresses these shortcomings by bringing model-based techniques and data-driven methods together, which can help pushing the envelope of existing algorithms and tools in formal verification and thus expanding their applicability to complex engineering systems, such as CPS. In the first part of the contribution, we discuss a new, formal, measurement-driven and model-based automated technique, for the quantitative verification of physical systems with partly unknown dynamics. We formulate this setup as a data-driven Bayesian inference problem, formally embedded within a quantitative, model-based verification procedure. We argue that the approach can be applied to complex physical systems that are key for CPS applications, dealing with spatially continuous variables, evolving under complex dynamics, driven by external inputs, and accessed under noisy measurements. In the second part of the contribution, we concentrate on systems represented by models that evolve under probabilistic and heterogeneous (continuous/discrete - that is "hybrid" - as well as nonlinear) dynamics. Such stochastic hybrid models (also known as SHS) are a natural mathematical framework for CPS. With focus on model-based verification procedures, we provide algorithms for quantitative model checking of temporal specifications on SHS with formal guarantees. This is attained via the development of formal abstraction techniques that are based on quantitative approximations. Theory is complemented by algorithms, all packaged in software tools that are available to users, and which are applied here in the domain of Smart Energy.

We are witnessing a huge growth of cyber-physical systems, which are autonomous, mobile, endowed with sensing, controlled by software, and often wirelessly connected and Internet-enabled. They include factory automation systems, robotic assistants, self-driving cars, and wearable and implantable devices. Since they are increasingly often used in safety- or business-critical contexts, to mention invasive treatment or biometric authentication, there is an urgent need for modelling and verification technologies to support the design process, and hence improve the reliability and reduce production costs. This paper gives an overview of quantitative verification and synthesis techniques developed for cyber-physical systems, summarising recent achievements and future challenges in this important field.

Moving Target Defense (MTD) can enhance the resilience of cyber systems against attacks. Although there have been many MTD techniques, there is no systematic understanding and quantitative characterization of the power of MTD. In this paper, we propose to use a cyber epidemic dynamics approach to characterize the power of MTD. We define and investigate two complementary measures that are applicable when the defender aims to deploy MTD to achieve a certain security goal. One measure emphasizes the maximum portion of time during which the system can afford to stay in an undesired configuration (or posture), without considering the cost of deploying MTD. The other measure emphasizes the minimum cost of deploying MTD, while accommodating that the system has to stay in an undesired configuration (or posture) for a given portion of time. Our analytic studies lead to algorithms for optimally deploying MTD.

While automated methods are the first line of defense for detecting attacks on webservers, a human agent is required to understand the attacker's intent and the attack process. The goal of this research is to understand the value of various log fields and the cognitive processes by which log information is grouped, searched, and correlated. Such knowledge will enable the development of human-focused log file investigation technologies. We performed controlled experiments with 65 subjects (IT professionals and novices) who investigated excerpts from six webserver log files. Quantitative and qualitative data were gathered to: 1) analyze subject accuracy in identifying malicious activity; 2) identify the most useful pieces of log file information; and 3) understand the techniques and strategies used by subjects to process the information. Statistically significant effects were observed in the accuracy of identifying attacks and time taken depending on the type of attack. Systematic differences were also observed in the log fields used by high-performing and low-performing groups. The findings include: 1) new insights into how specific log data fields are used to effectively assess potentially malicious activity; 2) obfuscating factors in log data from a human cognitive perspective; and 3) practical implications for tools to support log file investigations.