Finite State Research Articles

Security Evaluation of State Space Obfuscation of Hardware IP through a Red Team-Blue Team Practice

Due to the inclination towards a fab-less model of integrated circuit (IC) manufacturing, several untrusted entities get white-box access to the proprietary intellectual property (IP) blocks from diverse vendors. To this end, the untrusted entities pose security-breach threats in the form of piracy, cloning, and reverse-engineering, sometimes threatening national security. Hardware obfuscation is a prominent countermeasure against such issues. Obfuscation allows for preventing the usage of the IP blocks without authorization from the IP owners. Due to finite state machine (FSM) transformation-based hardware obfuscation, the design’s FSM gets transformed to make it difficult for an attacker to reverse-engineer the design. A secret key needs to be applied to make the FSM functional, thus preventing the usage of the IP for unintended purposes. Although several hardware obfuscation techniques have been proposed, due to the inability to analyze the techniques from the attackers’ standpoint, numerous vulnerabilities inherent to the obfuscation methods go undetected unless a true adversary discovers them. In this article, we present a collaborative approach between two entities—one acting as an attacker or red team and another as a defender or blue team , the first systematic approach to replicate the real attacker-defender scenario in the hardware security domain, which in return strengthens the FSM transformation-based obfuscation technique. The blue team transforms the underlying FSM of a gate-level netlist using state space obfuscation. The red team plays the role of an adversary or evaluator and tries to unlock the design by extracting the unlocking key or recovering the obfuscation circuitries. As the key outcome of this red team–blue team effort, a robust state space obfuscation methodology is evolved showing security promises.

Feedback stabilization of probabilistic finite state machines based on deep Q-network.

As an important mathematical model, the finite state machine (FSM) has been used in many fields, such as manufacturing system, health care, and so on. This paper analyzes the current development status of FSMs. It is pointed out that the traditional methods are often inconvenient for analysis and design, or encounter high computational complexity problems when studying FSMs. The deep Q-network (DQN) technique, which is a model-free optimization method, is introduced to solve the stabilization problem of probabilistic finite state machines (PFSMs). In order to better understand the technique, some preliminaries, including Markov decision process, ϵ-greedy strategy, DQN, and so on, are recalled. First, a necessary and sufficient stabilizability condition for PFSMs is derived. Next, the feedback stabilization problem of PFSMs is transformed into an optimization problem. Finally, by using the stabilizability condition and deep Q-network, an algorithm for solving the optimization problem (equivalently, computing a state feedback stabilizer) is provided. Compared with the traditional Q learning, DQN avoids the limited capacity problem. So our method can deal with high-dimensional complex systems efficiently. The effectiveness of our method is further demonstrated through an illustrative example.

Simulation of human risk scenarios for nuclear power plant operation based on finite state machine

This paper addresses risk scenarios caused by Human-Machine Interaction (HMI) during the Nuclear Power Plant (NPP) operation. A simulation method is proposed in this research based on a Finite State Machine (FSM) for analyzing human-related risk scenarios in NPP operations. The risk evolution mechanism between the physical system and operator is modeled by FSM to clarify how risk accumulation leads to accidents. It has the advantage of managing and simplifying the modeling process of complex relationships in NPP with the integration of human reliability analysis. The study focuses on the dynamic interaction between the physical system in the primary loop and the operators during the startup of NPP and evaluates human-related safety risk through simulation of the behavior of the operators handling the control rods. This research aims to present an FSM-based methodology for analyzing HMI-induced risks in NPP operations, which can be utilized to illustrate the risk evolution, especially human-related risk.

A Finite State Machine Model for Business Flow in Active Distribution Networks Considering the Multi-flow Fusion Process

A Finite State Machine Model for Business Flow in Active Distribution Networks Considering the Multi-flow Fusion Process

Robust models to infer flexible nondeterministic finite automata

Grammatical inference involves learning a formal grammar as a finite state machine or a set of rewrite rules. This paper focuses on inferring nondeterministic finite automata (NFA) from a given sample of words: the NFA must accept some words, and reject the others. To address this task we construct several over-constrained inference models capable of finding NFA of size k+1, which are directly convertible to NFA of size k. Additionally, we propose an NFA evaluation method based on random walks along with two methods used to enhance the acceptance rates of the inferred NFA. The effectiveness of our approach is demonstrated through the results of comprehensive experiments conducted on several benchmark sets. This paper is an extended version of the ICCS 2023 paper entitled “Inference of over-constrained NFA of size k+1 to efficiently and systematically derive NFA of size k for grammar learning” (Jastrzab et al., 2023 [1]).

ParDiff: Practical Static Differential Analysis of Network Protocol Parsers

Countless devices all over the world are connected by networks and communicated via network protocols. Just like common software, protocol implementations suffer from bugs, many of which only cause silent data corruption instead of crashes. Hence, existing automated bug-finding techniques focused on memory safety, such as fuzzing, can hardly detect them. In this work, we propose a static differential analysis called ParDiff to find protocol implementation bugs, especially silent ones hidden in message parsers. Our key observation is that a network protocol often has multiple implementations and any semantic discrepancy between them may indicate bugs. However, different implementations are often written in disparate styles, e.g., using different data structures or written with different control structures, making it challenging to directly compare two implementations of even the same protocol. To exploit this observation and effectively compare multiple protocol implementations, ParDiff (1) automatically extracts finite state machines from programs to represent protocol format specifications, and (2) then leverages bisimulation and SMT solvers to find fine-grained and semantic inconsistencies between them. We have extensively evaluated ParDiff using 14 network protocols. The results show that ParDiff outperforms both differential symbolic execution and differential fuzzing tools. To date, we have detected 41 bugs with 25 confirmed by developers.

Unifying frameworks for complete test strategies

The field of model-based testing has witnessed the development of several test strategies on finite state machines. Although these strategies are often related, little effort has been made to explicitly identify patterns shared between them, and their concrete implementations as well as completeness proofs regularly exhibit redundancy. In this paper, we propose an approach for the systematic verification and implementation of strategies for the language-equivalence conformance relation. We present frameworks in the form of higher order functions that implement shared behaviour once and encapsulate diverging behaviour in procedural parameters, thus reducing duplication and improving maintainability and extensibility. We show that this simplifies completeness proofs by proving complete all considered strategies using the same argument. All presented frameworks, proofs, and concrete strategy implementations have been mechanised using the proof assistant Isabelle.

Maze Of Faith : The Perfect Pray

The development of the gaming industry demands developers to create more immersive playing experiences. In this context, the use of Finite State Machine (FSM) has become a popular and effective solution for managing storyline progression and structured character development. This article discusses the basic concepts of FSM and illustrates its implementation in the game "Maze of Faith: Perfect Prayer" as a means of education and entertainment. The application of FSM in this game aims to enhance gameplay dynamics and provide a clear structure to the player's journey. The article elaborates on the fundamental FSM concepts applied in the game, highlighting how FSM usage can improve the quality of the playing experience for gamers. Through an analysis of FSM implementation in this game, the article provides a deeper understanding of how the FSM framework can be utilized to control storyline progression, character behavior, and overall gameplay dynamics. The implementation of FSM in "Maze of Faith: Perfect Prayer" has a positive impact on creating a well-structured, dynamic, and relevant playing experience aligned with modern player expectations.

Polynomial computational complexity of matrix elements of finite-rank-generated single-particle operators in products of finite bosonic states

It is known that computing the permanent of the matrix 1+A, where A is a finite-rank matrix, requires a number of operations polynomial in the matrix size. Motivated by the boson-sampling proposal of restricted quantum computation, I extend this result to a generalization of the matrix permanent: An expectation value in a product of a large number of identical bosonic states with a bounded number of bosons. This result complements earlier studies on the computational complexity in boson sampling and related setups. The proposed technique based on the Gaussian averaging is equally applicable to bosonic and fermionic systems. This also allows us to improve an earlier polynomial complexity estimate for the fermionic version of the same problem.

Yet another canonical nondeterministic automaton

Several canonical forms of finite automata have been introduced over the decades. In particular, if one considers the minimal deterministic finite automaton (DFA), the canonical residual finite state automaton (RFSA), and the átomaton of a language, then the átomaton can be seen as the dual automaton of the minimal DFA, but no such dual has been presented for the canonical RFSA so far. We fill this gap by introducing a new canonical automaton that we call the maximized prime átomaton, and study its properties. We also describe how these four automata can be extracted from suitable observation tables used in the automata learning context.

On comparisons of information structures with infinite states

Blackwell's theorem on the comparison of information structures is by now sufficiently well-understood for a finite state space, but important gaps remain in the infinite case. While the equivalence of (i) sufficiency and (ii) more-informativeness is known, we present a comprehensive theory that establishes equivalences between these two orders (in both their original and almost all versions) and three additional prior-dependent criteria on general (Polish) state spaces. We consider (iii) Bayesian preference, (iv) convex dominance, and (v) mean-preserving-spread (dilation) for all priors as well as for a given full-support prior. We provide counterexamples to underscore the necessity of the assumptions underlying some of our findings, and offer a generalization of the Hirschleifer-Schlee theorem as an application.

Zombie cheminformatics: extraction and conversion of Wiswesser Line Notation (WLN) from chemical documents

PurposeWiswesser Line Notation (WLN) is a old line notation for encoding chemical compounds for storage and processing by computers. Whilst the notation itself has long since been surpassed by SMILES and InChI, distribution of WLN during its active years was extensive. In the context of modernising chemical data, we present a comprehensive WLN parser developed using the OpenBabel toolkit, capable of translating WLN strings into various formats supported by the library. Furthermore, we have devised a specialised Finite State Machine l, constructed from the rules of WLN, enabling the recognition and extraction of chemical strings out of large bodies of text. Available open-access WLN data with corresponding SMILES or InChI notation is rare, however ChEMBL, ChemSpider and PubChem all contain WLN records which were used for conversion scoring. Our investigation revealed a notable proportion of inaccuracies within the database entries, and we have taken steps to rectify these errors whenever feasible.Scientific contributionTools for both the extraction and conversion of WLN from chemical documents have been successfully developed. Both the Deterministic Finite Automaton (DFA) and parser handle the majority of WLN rules officially endorsed in the three major WLN manuals, with the parser showing a clear jump in accuracy and chemical coverage over previous submissions. The GitHub repository can be found here: https://github.com/Mblakey/wiswesser.

Current dependence of the low bias resistance of small capacitance Josephson junctions

The dc current-voltage characteristics of small Josephson junctions reveal features that are not observed in larger junctions, in particular, a switch to the finite voltage state at current values much less than the expected critical current of the junction and a finite resistance in the nominally superconducting regime. Both phenomena are due to the increased sensitivity to noise associated with the small capacitance of the Josephson junction and have been extensively studied a few decades ago. Here I focus on the current bias dependence of the differential resistance of the junction at low current bias in the nominally superconducting regime, using a quantum Langevin equation approach that enables a physically transparent incorporation of the noise environment of the junction. A similar approach might be useful in modeling the sensitivity of superconducting qubits to noise in the microwave regime.

Nonlinearity in the Medium for Electromagnetic Wave Propagation Induced by Quantum Mechanical Interaction of the Field With Finite-State Atoms

We consider the interaction between a classical time-varying electric field and the atomic electrical dipole moment operator of a finite state atom and calculate approximately the mixed state evolution of the atom with such an interaction Hamiltonian up to quadratic orders in the external classical electric field using Schrodinger’s equation for mixed state evolution. From this approximate expression for the state evolution, we evaluate the average atomic electric dipole moment as a linear-quadratic function of the electric field. Later on, we also take into account Brownian motion bath noise that couples to the atomic dynamics, so that the Schrodinger equation dynamics gets modified to the Lindblad dynamics of an open quantum system, i.e., the master equation. From the average atomic dipole moment, we obtain an expression for the average polarization field of atoms constituting the medium through which the classical electromagnetic wave propagates. This average polarization is a linear-quadratic causal functional of the electric field. The resulting Maxwell equations for wave propagation, taking into account this polarization current density, thus acquire a quadratic nonlinearity which can be used to explain higher harmonic frequencies at the output of an optical fiber when the input is excited by the electric field coming from a monochromatic laser. We are then also able to calculate the mean square fluctuations in the polarization field using the evolving atomic state, and hence the electric field propagating through the medium of atoms acquires quantum fluctuations which can be used to explain time-varying random shifts in the spectral lines. As mentioned above, we include in this article an extension of this problem to the case when there is white Gaussian noise corrupting the state evolution dynamics of the atom and also the case when there is a pairwise interaction between the spin dipoles of atoms at two different locations. In the former case, we derive an approximate formula for the polarization field as well as its temporal statistical correlations, and we show how this computation can be extended to the latter case too. When there is a pairwise interaction between the atomic spins, we use a version of the approximate quantum Boltzmann equation to calculate the first and second order marginals of the atomic states, which is then used to compute the average polarization as well as its spatio-temporal correlations. Temporal correlations using second marginals of the atomic states cannot be computed using classical probabilistic methods owing to Heisenberg uncertainty, so we propose a sequential measurement-based strategy involving computation of the joint probabilities of the atomic spin components at two different times by first measuring one component of the spin, allowing for state collapse, and then following it up with nonlinear time evolution from the collapsed two-particle state using the quantum Boltzmann equation, followed further by measurement of another component of the spin at the second time.

Visually quantifying single-qubit quantum memory

To store quantum information, quantum memory plays a central intermediate ingredient in a network. The minimal criterion for a reliable quantum memory is the maintenance of the entangled state, which can be described by the non-entanglement-breaking (non-EB) channel. In this work, we show that all single-qubit quantum memory can be quantified without trusting input state generation. In other words, we provide a semi-device-independent approach to quantify all single-qubit quantum memory. More specifically, we apply the concept of the two-qubit quantum steering ellipsoids to a single-qubit quantum channel and define the channel ellipsoids. An ellipsoid can be constructed by visualizing finite output states within the Bloch sphere. Since the Choi-Jamiołkowski state of a channel can all be reconstructed from geometric data of the channel ellipsoid, a reliable quantum memory can be detected. Finally, we visually quantify the single-qubit quantum memory by observing the volume of the channel ellipsoid. Published by the American Physical Society 2024

General Fuzzy Switchboard Transformation Semigroup

Commutative and switching processing are central to computation. However, some problems arise when classical versions do not reflect the true needs of current computer science, whereby they cannot predict the flow of the next input information in the specified output. The Switchboard State Machine is a controller that controls the direct flow of information from one state to another and also plays the main part in communication between the subsystems. If the simple system satisfies the two properties of the switching and commutative state machines, the system is therefore a finite switchboard state machine. The general fuzzy switchboard automation (GFSA) was introduced by incorporating the switchboard into the general fuzzy automation. This paper is intended to introduce the concept of a general fuzzy switchboard transformation semigroup (GFSTS) by combining the GFSA and the transformation semigroup. Some related definitions and properties are established. The example of certain products, such as GFSTS cascade products, has also been studied. General Fuzzy Switchboard Poly-Transformation Semigroup (GFSPS) is also introduced since it fulfils the switchboard state machine properties. Some of the definitions and properties associated with the GFSPS are defined. Applications for general fuzzy switchboard automata, such as washing machines, are also provided.

DESIGN MODELS OF BIT-STREAM ONLINE-COMPUTERS FOR SENSOR COMPONENTS

Context. Currently, distributed real-time control systems need the creation of devices that perform online computing operations close to the sensor. The proposed online-computers of elementary mathematical functions can be used as components for the functional conversion of signals in the form of pulse streams received from measuring sensors with frequency output.
 Objective. The objective of the study is the development of mathematical, architectural and automata models for the design of bit-stream online-computers of elementary mathematical functions in order to create a unified approach to their design, due to which the accuracy of calculating functions can be increased, functional capabilities expanded, hardware costs reduced, and design efficiency increased.
 Method. Mathematical models of devices were developed using the method of forming increments of ascending step functions based on inverse functions with minimization of calculation error. Automata models of online-computers based on Moore’s Finite State Machine have been developed, the graph diagrams of which made it possible to ensure the clarity of function implementation algorithms, to increase visibility and invariance of implementation in formal languages of programming and hardware description.
 Results. The paper presents the results of research, development and practical approbation of design models of bit-stream onlinecomputers of power functions and root extraction function. A generalized architecture of an online-computer was proposed.
 Conclusions. The considered functional online-computers are effective from the point of view of calculation accuracy, simplicity of technical implementation, and universality of the architecture

Reward function design method to achieve system-level objectives in ambulance diversion problem

This study addresses a reward function design problem in multi-agent systems, using ambulance diversion (AD) problem in emergency medical services (EMS) as an application. While Emergency Departments (EDs) may declare AD to alleviate overcrowding, excessive use of this strategy by individual EDs can have negative consequences, such as limited access and delayed care for emergency patients. To eliminate negative consequences caused by individual decisions, we propose a reward function design method to align individual agents’ decisions with socially optimal strategies. This method ensures that an equilibrium status is achieved under socially optimal operation by removing incentives for deviations from socially optimal behavior while maximizing system welfare without extra investment. The proposed method offers an exact solution for designing reward function of a stochastic game model with finite state and action spaces. We conduct numerical experiments for a 2-ED system and demonstrate that the designed reward function properly guides the EDs toward a socially optimal AD strategy.

Robust Control: From Continuous-State Systems to Finite State Machines

Robust Control: From Continuous-State Systems to Finite State Machines

BM-UNet: lightweight multiclass-fire segmentation network and robust methods of aiming to the fire based on state-machine of mean segmentation mask and contour center-mass

The article presents the BM-UNet neural network architecture optimized for mobile devices. The article focuses on the possibility of real-time operation, takes into account various techniques for lightening the model, and provides a comparison with UNet-half. An improved model in terms of performance/accuracy ratio is applied in the algorithm of frame-by-frame segmentation of the flame on video, the result of which is averaged, and the optimal extinguishing point is found. For the latter, an approach to organizing a finite state machine is presented for switching between time-averaging windows for the possibility of timely response and minimization of mechanical losses of targeting. Combined, the method of frame-by-frame segmentation of flame contours and the algorithm for finding the center-mass point can be used in robotic flame extinguishing systems.