Analysis Of Finite State Machines Research Articles

Generalized Affine Equivalence Checking of Boolean Functions via Reachability Analysis

We consider the problem of checking the generalized affine equivalence of two given Boolean functions. This problem arises in various computer-aided design (CAD) and cryptographic applications, such as circuit synthesis and Reed-Muller codes. Based on the theory of affine group acting on the Boolean functions, we define the coefficient spaces and transition relations, and transform the checking problem into reachability analysis of finite state machines. Two methods are proposed to check the affine equivalence of Boolean functions using Binary Decision Diagrams (BDDs) and Property Directed Reachability (PDR), respectively. Both methods can check affine equivalence of bent and semi-bent functions, which state-of-the-art methods can hardly handle. Furthermore, existing methods only consider the case of affine equivalence, while our methods can handle the generalized affine equivalence of subspaces of Boolean functions. In the application of circuit synthesis, our methods can significantly reduce the size of the library compared to Boolean matching. To classify Boolean functions up to the generalized affine equivalence, we propose a method to obtain a complete classification based on BDDs. In the experiments, we have successfully applied our methods to some examples that can hardly be solved by using the previous methods, thus validating the effectiveness of our methods.

A matrix-based static approach to analysis of finite state machines

A matrix-based static approach to analysis of finite state machines

Learning Planning Domain Descriptions in RDDL

Recently, there is increasing interest in action model learning. However, most previous studies focused on learning effect-based action models. On the other hand, a rule-based planning domain description language was proposed in the latest planning competition. That is the Relational Dynamic Influence Diagram Language (RDDL). It uses rules to describe transitions instead of action models. In this paper, we build a system to learn planning domain descriptions in the RDDL. There are three major parts of an RDDL domain description: constraints, transitions and rewards. We first take advantage of the finite state machine analysis to identify constraints. Then, we employ the inductive learning technique to learn transitions. At last, we use regression to fix rewards. The evaluation was performed on benchmarks from planning competitions. It showed that our system can learn domain descriptions in the RDDL with low error rates. Moreover, our system is developed based on classical approaches. It implicates that the RDDL roots in previous planning languages. Therefore, more classical approaches could be useful in the RDDL domains.

Security Vulnerabilities: From Analysis to Detection and Masking Techniques

This paper presents a study that uses extensive analysis of real security vulnerabilities to drive the development of: 1) runtime techniques for detection/masking of security attacks and 2) formal source code analysis methods to enable identification and removal of potential security vulnerabilities. A finite-state machine (FSM) approach is employed to decompose programs into multiple elementary activities, making it possible to extract simple predicates to be ensured for security. The FSM analysis pinpoints common characteristics among a broad range of security vulnerabilities: predictable memory layout, unprotected control data, and pointer taintedness. We propose memory layout randomization and control data randomization to mask the vulnerabilities at runtime. We also propose a static analysis approach to detect potential security vulnerabilities using the notion of pointer taintedness.