Probabilistic Black-Box Checking via Active MDP Learning

We introduce a novel methodology for testing stochastic black-box systems, frequently encountered in embedded systems. Our approach enhances the established black-box checking (BBC) technique to address stochastic behavior. Traditional BBC primarily involves iteratively identifying an input that breaches the system's specifications by executing the following three phases: the learning phase to construct an automaton approximating the black box's behavior, the synthesis phase to identify a candidate counterexample from the learned automaton, and the validation phase to validate the obtained candidate counterexample and the learned automaton against the original black-box system. Our method, ProbBBC, refines the conventional BBC approach by (1) employing an active Markov Decision Process (MDP) learning method during the learning phase, (2) incorporating probabilistic model checking in the synthesis phase, and (3) applying statistical hypothesis testing in the validation phase. ProbBBC uniquely integrates these techniques rather than merely substituting each method in the traditional BBC; for instance, the statistical hypothesis testing and the MDP learning procedure exchange information regarding the black-box system's observation with one another. The experiment results suggest that ProbBBC outperforms an existing method, especially for systems with limited observation.Comment: Accepted to EMSOFT 202

A Novel Feature-Selection Algorithm in IoT Networks for Intrusion Detection

The Internet of Things (IoT) and network-enabled smart devices are crucial to the digitally interconnected society of the present day. However, the increased reliance on IoT devices increases their susceptibility to malicious activities within network traffic, posing significant challenges to cybersecurity. As a result, both system administrators and end users are negatively affected by these malevolent behaviours. Intrusion-detection systems (IDSs) are commonly deployed as a cyber attack defence mechanism to mitigate such risks. IDS plays a crucial role in identifying and preventing cyber hazards within IoT networks. However, the development of an efficient and rapid IDS system for the detection of cyber attacks remains a challenging area of research. Moreover, IDS datasets contain multiple features, so the implementation of feature selection (FS) is required to design an effective and timely IDS. The FS procedure seeks to eliminate irrelevant and redundant features from large IDS datasets, thereby improving the intrusion-detection system’s overall performance. In this paper, we propose a hybrid wrapper-based feature-selection algorithm that is based on the concepts of the Cellular Automata (CA) engine and Tabu Search (TS)-based aspiration criteria. We used a Random Forest (RF) ensemble learning classifier to evaluate the fitness of the selected features. The proposed algorithm, CAT-S, was tested on the TON_IoT dataset. The simulation results demonstrate that the proposed algorithm, CAT-S, enhances classification accuracy while simultaneously reducing the number of features and the false positive rate.publishedVersio

Special Session on Industry 4.0

No abstract available

Complex Patterns of Failure:Fault Tolerance via Complex Event Processing for IoT Systems

Fault-tolerance (FT) support is a key challenge for ensuring dependable Internet of Things (IoT) systems. Many existing FT-support mechanisms for IoT are static, tightly coupled, and inflexible, and so they struggle to provide effective support for dynamic IoT environments. This paper proposes Complex Patterns of Failure (CPoF), an approach to providing FT support for IoT systems using Complex Event Processing (CEP) that promotes modularity and reusability in FT-support design. System defects are defined using our Vulnerabilities, Faults, and Failures (VFF) framework, and error-detection strategies are defined as nondeterministic finite automata (NFA) implemented via CEP systems. We evaluated CPoF on an automated agriculture system and demonstrated its effectiveness against three types of error-detection checks: reasonableness, timing, and reversal. Using CPoF, we identified unreasonable environmental conditions and performance degradation via sensor data analysis