Finite State Machine Model Research Articles

Automated dynamic approach for detecting ransomware using finite-state machine

Ransomware is a type of malware that affects the victim data by modifying, deleting, or blocking their access. In recent years, ransomware attacks have resulted in critical data and financial losses to individuals and industries. These disruptions force the need for developing effective anti-ransomware methods in the research community. However, most of the existing techniques are designed to detect a specific ransomware variant instead of providing a generic solution mainly because of the obfuscation techniques used by ransomware or the use of static analysis methods. In this context, this paper proposes a novel ransomware-detection technique that identifies ransomware attacks by evaluating the current state of a computer system with knowledge of a ransomware attack. The finite-state machine model is used to synthesise the knowledge of the ransomware attack with respect to the victim machine. The proposed method monitors the changes happening in the computer system in terms of utilisation, persistence, and lateral movement of its resources to detect ransomware attacks. The experimental results demonstrate that the proposed method can accurately detect attacks from different ransomware variants with significantly few false predictions.

Design and Implementation of FSM Based MBIST using March Algorithm

The research article aims at identifying memory testing in static random access memory which is significant in deep sub micron era. Built in self test provides a best solution replacing the external Automatic test equipment. Built in Self Test is a technique of designing additional hardware and software feature into Integrated circuits to allow them to perform testing. BIST works in the background checking memories for faults without interfering with actual functionality of the memory. The objective of the proposed work is to identify faults associated with the memory, perform test algorithms to detect the faults in memory BIST architecture.The implementation of Memory BIST is done using Finite state machine model. The design of memory BIST is accomplished using Xilinx Vivado IDE for 32X8 memory.

IMPLEMENTASI FSM SEBAGAI RESPON OTONOM DAN ADAPTIF NPC PADA GAME “AWANG MENJELAJAHI KOTA TENGGARONG”

Development of the game towards a tourist promotion tool is very important. The game "Awang exploring the City of Tenggarong" is an android smartphone game that tells the story of a tourist / traveler named Awang who roams every tourist destination in the Tenggarong City. The development of this game uses multimedia development life cycle, starting from the concept, assembly, and testing. Artificial intelligence is also needed in the development of this game. Players will be accompanied by NPC (Non Player Character) in the form of a female tour guide. NPCs equipped with the Finite State Machine (FSM) model can guide players to go to each Tenggarong tourist attraction, and it can also provide information related to these tourist attractions. The final result of this research is a game that can be a media for tourism promotion in Tenggarong city

Enhanced Hierarchical Control Framework of Microgrids With Efficiency Improvement and Thermal Management

This article presents an enhanced hierarchical control framework of microgrids, as an effective complementary of conventional hierarchical control, to improve operation efficiency and perform thermal management. The enhanced hierarchical control framework is implemented by two control levels. In primary control level, droop control strategy with active thermal management is proposed to perform identical thermal sharing among paralleled inverters. Furthermore, secondary control level is developed to improve operation efficiency and perform thermal management in system-level according to time-varying load profiles, where finite state machine model is adopted to optimize operation number of inverters. In addition, in grid-connected microgrid, resonance phenomena may be caused by time-varying grid impedance. Hence, resonance mitigation strategy of microgrid is developed in the secondary control level, and small signal model of grid-connected microgrid is established to investigate the resonance phenomenon. Simulation and experimental results show that the proposed hierarchical control framework is able to perform active thermal management, improve operation efficiency, and mitigate harmonic resonance in either autonomous mode or grid-connected mode. The proposed hierarchical control framework is an enhanced complementary for conventional hierarchical control of microgrids.

Enhanced appearance-based finger detection and tracking using finite state machine control

Real-time finger detection and tracking systems have been growing rapidly in the past decade. Among those methods, Appearance-based and Model-based methods have produced excellent results. However, the occlusion issue is one of the main challenges in this field. In this study, we address this issue by considering the repeating-finite gestures of a guitar-strumming or a hand puppet and, represent using a Finite State Machine model. Also we proposed a novel finger pose tracking system using FSM Model combining with the appearance-based method.. The proposed system consists of two parts: FSM-FT builder creates the FSM hand, and the FSM-FT runner controls the FSM-FT system. Empirically, we conducted an experimental study involving one sample repeating hand gesture and our approach achieved a significance recognition rate of 82% in the testing phase.

Method for the Functional Diagnosis of Nondeterministic Finite State Machines

The problem of the functional diagnosis of critical systems, described by a nondeterministic finite state machine model, is considered. A new method for solving the problem is proposed, the distinguishing feature of which is the use of the mathematical apparatus of the algebra’s covers. The features and advantages of the method are illustrated by the example of the problem of monitoring the errors of IT system operators.

Influence of the combination of big data technology on the Spark platform with deep learning on elevator safety monitoring efficiency.

To effectively minimize elevator safety accidents, big data technology is combined with deep learning technology based on the Spark platform. This study first introduces the relevant theories of elevator safety monitoring technology, namely big data technology and deep learning technology. Then, the fault types that occur in the running state of the elevator are identified, and a finite state machine model is established. An elevator fault monitoring method based on the Spark platform is proposed, namely finite state machine (FSM), and the results of elevator safety fault monitoring are evaluated. Based on deep learning, an elevator fault warning model is constructed and its early warning performance is evaluated. The results show that the study can realize real-time and effective monitoring in the operation state of the elevator, and can determine the fault type of the elevator by binding the abnormal operation state with the corresponding fault. The feasibility of the elevator safety monitoring efficiency is evaluated based on three indexes: mutual information, accuracy, and false positives. Compared with other algorithms, the proposed FSM algorithm has the largest mutual information (0.1337), the highest accuracy (0.9899), the lowest false positive rate (0.0624), and the lowest false negative rate (0.1126); compared with other models, the elevator fault warning model proposed in this study has the lowest root mean-square error (RMSE) value (0.0201), the highest accuracy (0.9834), the lowest Loss value (0.0012), and the shortest convergence time (88.2608s), indicating that the elevator safety monitoring system and elevator fault warning model are feasible. This study establishes a good direction for elevator safety monitoring efficiency in China.

Reachability Analysis of Networked Finite State Machine With Communication Losses: A Switched Perspective

Networked finite state machine takes into account communication losses in industrial communication interfaces due to the limited bandwidth. The reachability analysis of networked finite state machine is a fundamental and important research topic in blocking detection, safety analysis, communication system design and so on. This paper is concerned with the impact of arbitrary communication losses on the reachability of networked finite state machine from a switched perspective. First, to model the dynamics under arbitrary communication losses in communication interfaces (from the controller to the actuator), by resorting to the semi-tensor product (STP) of matrices, a switched algebraic model of networked finite state machine with arbitrary communication losses is proposed, and the reachability analysis can be investigated by using the constructed model under arbitrary switching signal. Subsequently, based on the algebraic expression and its transition matrix, necessary and sufficient conditions for the reachability are derived for networked finite state machine. Finally, some typical numerical examples are exploited to demonstrate the effectiveness of the proposed approach. Note that current results provide valuable clues to design reliable and convergent Internet-of-Things networks.

Enhanced fuzzy finite state machine for human activity modelling and recognition

A challenging key aspect of modelling and recognising human activity is to design a model that can deal with the uncertainty in human behaviour. Several machine learning and deep learning techniques are employed to model the Activity of Daily Living (ADL) representing the human activity. This paper proposes an enhanced Fuzzy Finite State Machine (FFSM) model by combining the classical FFSM with Long Short-Term Memory (LSTM) neural network and Convolutional Neural Network (CNN). The learning capability in the LSTM and CNN allows the system to learn the relationship in the temporal human activity data and to identify the parameters of the rule-based system as building blocks of the FFSM through time steps in the learning mode. The learned parameters are then used for generating the fuzzy rules that govern the transitions between the system’s states representing activities. The proposed enhanced FFSMs were tested and evaluated using two different datasets; a real dataset collected by our research group and a public dataset collected from CASAS smart home project. Using LSTM-FFSM, the experimental results achieved 95.7% and 97.6% for the first dataset and the second dataset, respectively. Once CNN-FFSM was applied to both datasets, the obtained results were 94.2% and 99.3%, respectively.

Real-Time Diagnosis of Multiple Transistor Open-Circuit Faults in a T-Type Inverter Based on Finite-State Machine Model

This paper proposes a fault diagnosis method to diagnose multiple transistor open-circuit faults in a T-type three-level inverter. In this method, a finite-state machine (FSM) tracks state transitions caused by abnormal fault-linked current paths, and rough set theory (RST) is employed to optimize and obtain a minimum set of variables necessary to distinguish state transitions under various fault scenarios. After applying RST, voltage state variables expressed by Boolean logic relationships are adopted in the FSM to identify faults. This can also effectively reflect state transitions between single and multiple fault cases. The approach is immune to load disturbances and dead times. Through logic relationships, a circuit is designed for fast online fault location to minimize the impact of sampling frequency on diagnosis. Factors that affect diagnosis time and accuracy are considered and analyzed to ensure the reliability of the proposed method. Experimental results obtained under various conditions verify the effectiveness of this approach.

Finite State Machine Modelling to Facilitate the Resilience of Infrastructures: Reflections

The ability of an infrastructure to be resistant against hazards or to accommodate and recover from hazard-induced destructions and disturbances is characterized as resilience. Usually, infrastructures are engineered socio-technical systems or systems-of-systems. Jackson and Ferris consider the use of finite state machine (FSM) modelling as a suitable means to depict and investigate the resilience of such engineered systems. This paper discusses the capabilities and limitations of the FSM model proposed by Jackson and Ferris and if it should be used for the representation and evaluation of the resilience of an infrastructure. The discussion is conducted on a more general level. However, special attention is paid to monitoring because, on the one hand, monitoring is one of the cornerstones of resilience and, on the other hand, Scott and Ferris define a state that is emphasized by an increased level of situational awareness as a result of happened and perceived events. Consequently, the question has to be answered of how the models are able to reflect the need for routine monitoring of the resilience of infrastructures in order to initiate, if necessary, adjustment procedures as an appropriate response to changes in internal and external conditions. The results of this theoretical study are a fundamental step towards the practical application of the FSM approach for the design of resilient infrastructures.

Online Gait Planning of Lower-Limb Exoskeleton Robot for Paraplegic Rehabilitation Considering Weight Transfer Process

People who suffer from paraplegia completely lose sensory and locomotor functions; there are no known treatment methods for their recovery at this time. Exoskeleton robots have the potential to dramatically improve the locomotor ability of these individuals. Although some exoskeleton robots for paraplegic patients have been commercialized and are able to restore walking motion at present, the pilot must acquire the ability to maintain their balance and shift their weight using forearm crutches, which is very challenging for paraplegics. To make this easier, we propose a new automated intelligent gait planning method that integrates a finite-state machine (FSM) model as an underlying foundation and a gait generation model in addition to the exoskeleton system. The underlying FSM model is defined using an inverted pendulum model and a minimum jerk algorithm. To compare the planning gait, 33 volunteers provide normal walking gaits; there are two more volunteers (paraplegic and nonparaplegic) wearing the Shenzhen Institute of Advanced Technology (SIAT) exoskeleton robot to validate the effects of the proposed gait and offer the groups of surface electromyogram (sEMG) data for analysis. As a result, the input of the proposed gait planning method is simplified to two parameters. The proposed walking gait significantly reduces the arm muscle output. Note to Practitioners-This article was motivated by the problem that the four-degree of freedom (DoF) underactuated paraplegic rehabilitation lower limb exoskeleton robot lacks of the center of gravity (COG) transfer process when coordinating with paraplegia patients during the training process for beginner. The existing approach to deal with this problem generally is to train the pilot for obtaining the COG transfer ability by using crutches. This article suggests a gait planning method for the four-DOF underactuated rehabilitation lower limb exoskeleton robot considering the COG transfer process to make the exoskeleton robot coordinate with a pilot and ensure safety. The gait planning method is based on the inverted pendulum model and simplified to several parameters. By adjusting these parameters, the step length, step height, walking speed, and the shape of gait can be adjusted according to the requirements of the exoskeleton robot and pilot. In this article, we mathematically characterize a gait planning method for the exoskeleton control strategy. Preliminary online experiments suggest that this approach is feasible and can significantly reduce the arm muscle output of pilot. In future research, we will adjust the gait by estimating the velocity of center of mass (COM) of the pilot to make the exoskeleton robot coordinate with pilot actively.

Improving Characteristics of LUT-Based Moore FSMs

Almost any digital system includes sequential blocks which can be represented using a model of finite state machine (FSM). It is very important to improve such characteristics of FSM circuits as the number of logic elements, operating frequency and consumed energy. The paper proposes a novel design method targeting a decrease in the number of look-up table (LUT) elements in logic circuits of FPGA-based Moore FSMs. The method is based on using two sets of variables for encoding the collections of outputs. It results in a partition of the set of outputs by two blocks. The outputs from the first block depend on state variables, the outputs from the second block on additional variables. A method is proposed for splitting the set of outputs. The conditions for using the proposed method are given. An example of synthesis is shown. The results of experiments with standard benchmarks are discussed. The experiments outcomes show that the proposed approach allows diminishing the number of LUTs and consumed energy. Also, it leads to an increase in the operating frequency. The method targets rather complex FSMs when the number of state variables exceeds the number of LUT’s inputs.

A Novel Effectiveness Evaluation Method Based on Simultaneous Probabilistic Finite-State Machines

To reflect the influence of diverse battlefield information to the operational effectiveness (OE) of weapon systems, this article gives a novel evaluation method. The method is based on a set of simultaneous probabilistic finite-state machines (SPFSMs). It can evaluate the OE of the whole system and subeffectiveness of subsystems simultaneously. This makes it convenient to analyze the main OE influencing factors and to provide effectiveness improvement plans. To achieve this, the traditional probabilistic finite-state machine (PFSM) model of the whole system is decomposed into a set of PFSM models of subsystems. This makes it possible to consider more influencing factors of both friendly and hostile forces, without a sharp increase in the model size. To make the method applicable universally, this article proposes a complete theoretical system of SPFSM sets, including the definition and properties. And to reflect the random property of system inputs, a set of input probabilities is added to traditional PFSM definitions. Based on this theory, this article gives concrete OE evaluation methods and steps. A simulation example of an antiship missile is given and analyzed to demonstrate the utility of the proposed method.

Comparing Graph-Based Algorithms to Generate Test Cases from Finite State Machines

Model-Based Testing (MBT) is a well-known technique that employs formal models to represent reactive systems’ behavior and generates test cases. Such systems have been specified and verified using mostly Finite State Machines (FSMs). There is a plethora of test generation algorithms in the literature; most of them are based on graphs once an FSM can be formally defined as a graph. Nevertheless, there is a lack of studies on analyzing cost and efficiency of FSM-based test generation algorithms. This study compares graph-based algorithms adopted to generate test cases from FSM models. In particular, we compare the Chinese Postman Problem (CPP) and H-Switch Cover (HSC) algorithms with the well-known Depth-First Search (DFS) and Breadth-First Search (BFS) algorithms in the context of covering all-transitions and all-transition-pairs criteria in an FSM. First, a systematic literature mapping was conducted to summarize the methods that have been adopted in MBT, considering FSMs. Second, the main methods found were implemented and analyzed on randomly-generated FSMs, as well as real-world models that represent embedded systems of space applications. To make comparisons, we considered analyses in terms of cost (time), efficiency (mutant analysis) and characteristics of the generated test suites (number of test cases, average length of test cases, largest and smallest test cases, standard deviation and distribution of test cases). In general, CPP presented the best results in terms of number of test cases and test suite size. In addition, CPP also presented low distribution of average length compared to other algorithms.

Utilization of finite state machine approach for microgrid modeling

The paper describes the finite state machines (FSM) approach in a microgrid’s distributed energy sources modeling. It defines FSM models for a small hydro power plant, a cogeneration unit and a photovoltaic power plant. A comparison of simulation results obtained by FSM models and purely dynamic simulations models, from an execution time point of view, is presented as well.

Lightweight signature scheme to protect intellectual properties of Internet of things applications in system on chip field-programmable gate arrays

Billions of smart objects in the edge devices offer advanced connectivity to networks which increase the security and complexity of the Internet of things (IoT) applications. To make such entities smarter heterogeneous intellectual property (IP) cores from multiple service providers are reused in system on chip platform. Enabling both chip and IP protection at post fabrication level is imperative. The IoT-based IP cores are signed with the hybrid physical unclonable function and finite state machine model to protect from cloning, misuse, unauthorized user access, and physical attacks. The extended finite-state machine is used to verify the signature, which reduces the space and time complexity. The goal is to design a secure plug-and-play system which supports IoT IP core for the unique chip without using any complex algorithms and huge memory storage. Experimental results show that the average overhead values of area and power are 1.05 % and 1.08 % less, respectively, compared with the existing IP protection.

Self-Adaptive Framework Based on MAPE Loop for Internet of Things.

The Internet of Things (IoT) connects a wide range of objects and the types of environments in which IoT can be deployed dynamically change. Therefore, these environments can be modified dynamically at runtime considering the emergence of other requirements. Self-adaptive software alters its behavior to satisfy the requirements in a dynamic environment. In this context, the concept of self-adaptive software is suitable for some dynamic IoT environments (e.g., smart greenhouses, smart homes, and reality applications). In this study, we propose a self-adaptive framework for decision-making in an IoT environment at runtime. The framework comprises a finite-state machine model design and a game theoretic decision-making method for extracting efficient strategies. The framework was implemented as a prototype and experiments were conducted to evaluate its runtime performance. The results demonstrate that the proposed framework can be applied to IoT environments at runtime. In addition, a smart greenhouse-based use case is included to illustrate the usability of the proposed framework.

Data residency as a service: A secure mechanism for storing data in the cloud

Recently, researchers have been working on cloud data assurance models to ensure that the data is in compliance with the policies. In such a model, the data is placed in the intended data centres without focusing on data residency issues. Thus, in order to solve such issues, we have proposed a framework known as the data resident storage (DRS) which is intended to achieve data residency protection, thus providing data residency as a service (DRaaS). The finite state machine model is developed for the purpose of verifying the methodology in terms of data location and data access. The validation is carried out in the CloudSim toolkit with the help of defined services. The developed test automation framework places the data only in data centres that are in compliance with data residency laws. Finally, this framework can be hosted by the cloud service provider to provide DRaaS to the end users.

Hardware Signature Generation Using a Hybrid PUF and FSM Model for an SoC Architecture

Security is becoming an important issue in the recent System on Chip (SoC) design due to various hardware attacks that can affect manufacturers, system designers or end users. Major issues include hardware Trojan attack, hardware intellectual property (IP) theft, such as an illegal sale or use of firm intellectual property cores or integrated circuits (ICs) and physical attacks. A hybrid model consisting of Arbiter PUF and Butterfly PUF are used to generate random responses which are fed to a Finite State Machine (FSM). A three-level FSM was designed to generate the signature correctly to authenticate IPs. The results were obtained with the help of three Intellectual Property (IP) cores – Zedboard OLED IP, ISCAS’89 s1423 Benchmark IP and a Full Adder IP. A 16-bit arbiter PUF and Butterfly PUF have been implemented on a 28nm FPGA. The average execution time to generate hardware signature for three IP cores was found to be 4.78 seconds (5 iterations) which is considerably low.