Real-time Behavior Research Articles

DRIVER DROWSINESS DETECTION SYSTEM

The proposed system utilizes facial landmark detection and eye aspect ratio calculation to detect drowsiness in real-time from a webcam feed. It employs the dlib library for face detection and facial landmark prediction. The eye aspect ratio (EAR) is computed from the distances between specific points on the eyes. When the EAR falls below a predefined threshold for a certain number of consecutive frames, it triggers an alert indicating potential drowsiness. The alert includes visual cues on the video feed, an auditory alert using the playsound library, and even sends an instant WhatsApp message using pywhatkit to notify a designated contact about the drowsiness. This system serves as a useful tool for monitoring driver fatigue or alertness in various contexts, promoting safety and awareness. Its capacity to swiftly identify and alert users to the onset of drowsiness represents a significant leap forward in safety technology. By mitigating the risk of accidents stemming from fatigue-induced impairments, this project underscores the pivotal role of computer vision in real-time human behavior monitoring, offering a versatile and invaluable tool for fostering safety and awareness across various domains. Keywords: Drowsiness Detection; Python; Whatsapp messaging; Webcam feed; Computer Vision; Alert System; Real-time monitoring; Eye-Aspect Ratio; Frame Processing; Facial Recognition.

Adaptive temporal compression for reduction of computational complexity in human behavior recognition

The research on video analytics especially in the area of human behavior recognition has become increasingly popular recently. It is widely applied in virtual reality, video surveillance, and video retrieval. With the advancement of deep learning algorithms and computer hardware, the conventional two-dimensional convolution technique for training video models has been replaced by three-dimensional convolution, which enables the extraction of spatio-temporal features. Specifically, the use of 3D convolution in human behavior recognition has been the subject of growing interest. However, the increased dimensionality has led to challenges such as the dramatic increase in the number of parameters, increased time complexity, and a strong dependence on GPUs for effective spatio-temporal feature extraction. The training speed can be considerably slow without the support of powerful GPU hardware. To address these issues, this study proposes an Adaptive Time Compression (ATC) module. Functioning as an independent component, ATC can be seamlessly integrated into existing architectures and achieves data compression by eliminating redundant frames within video data. The ATC module effectively reduces GPU computing load and time complexity with negligible loss of accuracy, thereby facilitating real-time human behavior recognition.

Enhancing Industrial Process Control: Integrating Intelligent Digital Twin Technology with Proportional-Integral-Derivative Regulators

This paper explores the integration of intelligent digital twin technology with PID regulators in industrial process control utilizing smart meter data. It presents a novel approach involving the creation of mathematical models to simulate real-time system behavior, thereby enhancing the PID control loop. The focus is on the development of specialized IT infrastructure to support this integration, which includes data acquisition, processing, and control optimization. This integration aims to not only improve control system efficiency but also introduce a robust predictive maintenance framework, offering significant benefits across a wide range of industrial applications.

Fabric tearing performance state perception and classification driven by multi-source data.

The tear strength of textiles is a crucial characteristic of product quality. However, during the laboratory testing of this indicator, factors such as equipment operation, human intervention, and test environment can significantly influence the results. Currently, there is a lack of traceable records for the influencing factors during the testing process, and effective classification of testing activities is not achieved. Therefore, this study proposes a state-awareness and classification approach for fabric tear performance testing based on multi-source data. A systematic design is employed for fabric tear performance testing activities, which can real-time monitor electrical parameters, operational environment, and operator behavior. The data are collected, preprocessed, and a Decision Tree Support Vector Machine (DTSVM) is utilized for classifying various working states, and introducing ten-fold cross-validation to enhance the performance of the classifier, forming a comprehensive awareness of the testing activities. Experimental results demonstrate that the system effectively perceives fabric tear performance testing processes, exhibiting high accuracy in the classification of different fabric testing states, surpassing 98.73%. The widespread application of this system contributes to continuous improvement in the workflow and traceability of fabric tear performance testing processes.

Cost Minimization of Digital Twin Placements in Mobile Edge Computing

In the past decades, explosive numbers of Internet of Things (IoT) devices (objects) have been connected to the Internet, which enable users to access, control, and monitor their surrounding phenomenons at anytime and anywhere. To provide seamless interactions between the cyber world and the real world, Digital twins (DTs) of objects (IoT devices) are key enablers for real time monitoring, behavior simulations and predictive decisions on objects. Compared to centralized cloud computing, mobile edge computing (MEC) has been envisioning as a promising paradigm for low latency IoT applications. Accelerating the usage of DTs in MEC networks will bring unprecedented benefits to diverse services, through the co-evolution between physical objects and their virtual DTs, and DT-assisted service provisioning has attracted increasing attention recently. In this paper, we consider novel DT placement and migration problems in an MEC network with the mobility assumption of objects and users, by jointly considering the freshness of DT data and the service cost of users requesting for DT data. To this end, we first propose an algorithm for the DT placement problem with the aim to minimize the sum of the DT update cost of objects and the total service cost of users requesting for DT data, through efficient DT placements and resource allocation to process user requests. We then devise an approximation algorithm with a provable approximation ratio for a special case of the DT placement problem when each user requests the DT data of only one object. Meanwhile, considering the mobility of users and objects, we devise an online, two-layer scheduling algorithm for DT migrations to further reduce the total service cost of users within a given finite time horizon. We finally evaluate the performance of the proposed algorithms through experimental simulations. The simulation results show that the proposed algorithms are promising.

A Comparative Study on the Operational Efficiency of Joystick and Touch Screen Control Methods for Sugarcane Harvesters

Reducing cumbersome mechanical control components is one of the trends of modern agricultural machinery towards a high degree of automation. Whether the control components of sugarcane harvesters can gradually be replaced by central control screens, similar to car cabins, is an unresolved question. At the level of human machine interaction, this involves comparing the efficiency between joystick and touch screen control. This paper conducts a simulated experiment to compare and study the efficiency and user experience of joystick and touch screen control in adjusting the topper and base cutter heights of sugarcane harvesters, aiming to provide a reasonable basis for the design of control interfaces in sugarcane harvester cabins. The electromyographic signals, experiment duration, and subjective cognitive evaluations of participants in both the topper and base cutter groups were analyzed. The results showed that the efficiency, learnability, and ease of use of different control methods varied under different operational tasks. For the topper that corresponds to the operating behavior and height transformation in real time, joystick control demonstrated superior ease of use and operational efficiency compared to touch screen control, with weaker learnability. There was no discernible difference in muscle activation levels between the two control methods. Consequently, joystick control is deemed more suitable for the height adjustment of the topper. Regarding the base cutter with non-real-time mapping of operating behavior and height changes, no significant disparity in ease of use and learnability was observed between the two control methods. Touch screen control yielded lower muscle activation levels and garners higher overall subjective cognitive scale ratings. Thus, touch screen control is considered more suitable for base cutter height adjustment. Lastly, the paper proposes the optimal combination of software and hardware for control components in sugarcane harvester cabins, and provides an objective and multidimensional experimental analysis method for future research on similar human machine interaction interfaces.

Integration of Decentralized Graph-Based Multi-Agent Reinforcement Learning with Digital Twin for Traffic Signal Optimization

Machine learning (ML) methods, particularly Reinforcement Learning (RL), have gained widespread attention for optimizing traffic signal control in intelligent transportation systems. However, existing ML approaches often exhibit limitations in scalability and adaptability, particularly within large traffic networks. This paper introduces an innovative solution by integrating decentralized graph-based multi-agent reinforcement learning (DGMARL) with a Digital Twin to enhance traffic signal optimization, targeting the reduction of traffic congestion and network-wide fuel consumption associated with vehicle stops and stop delays. In this approach, DGMARL agents are employed to learn traffic state patterns and make informed decisions regarding traffic signal control. The integration with a Digital Twin module further facilitates this process by simulating and replicating the real-time asymmetric traffic behaviors of a complex traffic network. The evaluation of this proposed methodology utilized PTV-Vissim, a traffic simulation software, which also serves as the simulation engine for the Digital Twin. The study focused on the Martin Luther King (MLK) Smart Corridor in Chattanooga, Tennessee, USA, by considering symmetric and asymmetric road layouts and traffic conditions. Comparative analysis against an actuated signal control baseline approach revealed significant improvements. Experiment results demonstrate a remarkable 55.38% reduction in Eco_PI, a developed performance measure capturing the cumulative impact of stops and penalized stop delays on fuel consumption, over a 24 h scenario. In a PM-peak-hour scenario, the average reduction in Eco_PI reached 38.94%, indicating the substantial improvement achieved in optimizing traffic flow and reducing fuel consumption during high-demand periods. These findings underscore the effectiveness of the integrated DGMARL and Digital Twin approach in optimizing traffic signals, contributing to a more sustainable and efficient traffic management system.

A Bluetooth-Based Smartphone App for Detecting Peer Proximity: Protocol for Evaluating Functionality and Validity.

While ecological momentary assessment (EMA) is commonly used to study social contexts and social influence in the real world, EMA almost exclusively relies on participant self-report of present circumstances, including the proximity to influential peers. There is the potential for developing a proximity sensing approach that uses small Bluetooth beacons and smartphone-based detection and data collection to collect information about interactions between individuals passively in real time. This paper aims to describe the methods for evaluating the functionality and validity of a Bluetooth-based beacon and a smartphone app to identify when ≥2 individuals are physically proximal. We will recruit 20 participants aged 18 to 29 years with Android smartphones to complete a 3-week study during which beacon detection and self-report data will be collected using a smartphone app (MEI Research). Using an interviewer-administered social network interview, participants will identify up to 3 peers of the same age who are influential on health behavior (alcohol use in this study). These peers will be asked to carry a Bluetooth beacon (Kontakt asset tag) for the duration of the study; each beacon has a unique ID that, when detected, will be recorded by the app on the participant's phone. Participants will be prompted to respond to EMA surveys (signal-contingent reports) when a peer beacon encounter meets our criteria and randomly 3 times daily (random reports) and every morning (morning reports) to collect information about the presence of peers. In all reports, the individualized list of peers will be presented to participants, followed by questions about peer and participant behavior, including alcohol use. Data from multiple app data sets, including beacon encounter specifications, notification, and app logs, participant EMA self-reports and postparticipation interviews, and peer surveys, will be used to evaluate project goals. We will examine the functionality of the technology, including the stability of the app (eg, app crashes and issues opening the app), beacon-to-app detection (ie, does the app detect proximal beacons?), and beacon encounter notification when encounter criteria are met. The validity of the technology will be defined as the concordance between passive detection of peers via beacon-to-app communication and the participant's EMA report of peer presence. Disagreement between the beacon and self-report data (ie, false negatives and false positives) will be investigated in multiple ways (ie, to determine if the reason was technology-related or participant compliance-related) using encounter data and information collected from participants and peers. Participant recruitment began in February 2023, and enrollment was completed in December 2023. Results will be reported in 2025. This Bluetooth-based technology has important applications and clinical implications for various health behaviors, including the potential for just-in-time adaptive interventions that target high-risk behavior in real time. DERR1-10.2196/50241.

Evaluating a Remotely Delivered Cardio-Oncology Rehabilitation Intervention for Patients With Breast Cancer (REMOTE-COR-B): Protocol for a Single-Arm Feasibility Trial.

Exercise rehabilitation is a promising strategy for reducing cardiovascular disease risk among patients with breast cancer. However, the evidence is primarily derived from programs based at exercise centers with in-person supervised delivery. Conversely, most patients report a preference for home-based rehabilitation. As such, there is a clear need to explore strategies that can provide real-time supervision and coaching while addressing consumer preferences. Evidence from cardiac rehabilitation has demonstrated the noninferiority of a smartphone-based telerehabilitation approach (REMOTE-CR) to improve cardiorespiratory fitness in people with cardiovascular disease compared to a center-based program. This study aims to assess the feasibility, safety, and preliminary efficacy of the REMOTE-CR program adapted for patients with breast cancer at risk of cardiotoxicity (REMOTE-COR-B). We will also assess the satisfaction and usability of REMOTE-COR-B. We will conduct a single-arm feasibility study of the REMOTE-COR-B program among patients with stage I-III breast cancer who are at risk of cardiotoxicity (taking treatment type and dose, as well as other common cardiovascular disease risk factors into account) and who are within 24 months of completing primary definitive treatment. Participants (target sample size of 40) will receive an 8-week smartphone-based telerehabilitation exercise program involving remotely delivered real-time supervision and behavior change support. The platform comprises a smartphone and wearable heart rate monitor, as well as a custom-built smartphone app and web application. Participants will be able to attend remotely monitored exercise sessions during set operating hours each week, scheduled in both the morning and evening. Adherence is the primary outcome of the trial, assessed through the number of remotely monitored exercise sessions attended compared to the trial target (ie, 3 sessions per week). Secondary outcomes include additional trial feasibility indicators (eg, recruitment and retention), safety, satisfaction, and usability, and objective and patient-reported efficacy outcomes (cardiovascular fitness, quality of life, fatigue, self-reported exercise, self-efficacy, habit strength, and motivation). Adherence, feasibility, and safety outcomes will be assessed during the intervention period; intervention satisfaction and usability will be assessed post intervention; and objective and patient-reported efficacy outcomes will be assessed at baseline, post intervention (2-month postbaseline assessment), and at follow-up (5-month postbaseline assessment). Recruitment for this trial commenced in March 2023, and 7 participants had been recruited as of the submission of the manuscript. The estimated completion date for the project is October 2024, with results expected to be published in mid-2025. The REMOTE-COR-B intervention is a novel and promising approach to providing exercise therapy to patients with breast cancer at risk of cardiotoxicity who have unique needs and heightened safety risks. This project will provide important information on the extent to which this approach is satisfactory to patients with breast cancer, safe, and potentially effective, which is necessary before larger-scale research or clinical projects. Australian New Zealand Clinical Trials Registry ACTRN12621001557820; www.anzctr.org.au/ACTRN12621001557820.aspx. DERR1-10.2196/53301.

Adaptive Multi-Layer Security Framework (AMLSF) for real-time applications in smart city networks

<p>This study introduces the Adaptive Multi-Layer Security Framework (AMLSF), a novel approach designed for real-time applications in smart city networks, addressing the current challenges in security systems. AMLSF innovatively incorporates machine learning algorithms for dynamic adjustment of security protocols based on real-time threat analysis and device behavior patterns. This approach marks a significant shift from static security measures, offering an adaptive encryption mechanism that scales according to application criticality and device mobility. Our methodology integrates hierarchical key management with real-time adaptability, further enhanced by an advanced rekeying strategy sensitive to device mobility and communication overhead. The paper’s findings reveal a substantial improvement in security efficiency. AMLSF outperforms existing models in encryption strength, rekeying time, communication overhead, and computational time by significant margins. Notably, AMLSF demonstrates an adaptability increase of over 30% compared to traditional models, with encryption strength and computational time efficiency improving by approximately 25%. These results underscore AMLSF’s capability in delivering robust, dynamic security without sacrificing performance. The achievements of AMLSF are significant, indicating a promising direction for smart city security frameworks. Its ability to adapt in real-time to various security needs, coupled with its performance efficiency, positions AMLSF as a superior choice for smart city networks facing diverse and evolving security threats. This framework sets a new benchmark in smart city security, paving the way for future developments in this rapidly advancing field.</p>

A Multiperspective Fraud Detection Method for Multiparticipant E-Commerce Transactions

Detection and prevention of fraudulent transactions in e-commerce platforms have always been the focus of transaction security systems. However, due to the concealment of e-commerce, it is not easy to capture attackers solely based on the historic order information. Many works try to develop technologies to prevent frauds, which have not considered the dynamic behaviors of users from multiple perspectives. This leads to an inefficient detection of fraudulent behaviors. To this end, this article proposes a novel fraud detection method that integrates machine learning and process mining models to monitor real-time user behaviors. First, we establish a process model concerning the business-to-customer (B2C) e-commerce platform, by incorporating the detection of user behaviors. Second, a method for analyzing abnormalities that can extract important features from event logs is presented. Then, we feed the extracted features to a support vector machine (SVM)-based classification model that can detect fraud behaviors. We demonstrate the effectiveness of our method in capturing dynamic fraudulent behaviors in e-commerce systems through the experiments.

Analysis of Off-Road Tire Cornering Characteristics by Using Advanced Analytical Techniques

ABSTRACT This paper focuses on analysis of the cornering characteristics of an off-road truck tire running under several operating conditions over different soils. The finite element analysis (FEA) method is used to model the Goodyear RHD 315/80R22.5 truck tire, and the smoothed-particle hydrodynamics (SPH) method is used to model the soil. The goal of this research is to provide a virtual testing environment in Pam-Crash software as an alternative to actual tests for FEA and SPH analyses of rolling tire interactions on deformable terrains. The study on the effects of different operating parameters on the cornering performance combined with the sensitivity study can be of interest to tire engineers or vehicle engineers because they provide insight into the design and real-time behavior of a vehicle. Tire and soil models are validated using experimental data and published measurements, showing good agreement. The tire–soil interaction is investigated under different tire conditions, such as longitudinal speed, inflation pressure, vertical load, and slip angle, and under various soil characteristics, such as cohesion, internal friction angle, and rut depth. Cornering force, self-aligning moment, and overturning moment are studied as the fundamental cornering characteristics that affect truck lateral stability and control. Owing to the excessive computational demands posed by the FEA-SPH tire–soil models, we propose unique mathematical relationships for estimating the cornering characteristics of free-rolling as well as driven truck tires in an efficient manner. The genetic algorithm (GA) technique is used to develop relationships between the cornering parameters and operating conditions. We conclude that the identified mathematical relationships could provide very good estimations of the cornering characteristics under a broad range of operating conditions and soils. The GA equations will ultimately be implemented into a full vehicle model to evaluate the full vehicle performance.

Review of Poultry Monitoring using Computer Vision

Poultry farming is an important unit in global agronomy, contributing immensely to the production of meat and eggs. Safeguarding the health and welfare of poultry is vital for ethical and financial reasons. In recent years, computer vision awareness has gained prominence as a powerful tool for poultry monitoring. This review paper provides an outline of the application of computer vision in poultry monitoring. We explore the different phases of this technology, with real-time image acquisition, object recognition, and behavior analysis. By connecting cameras and sophisticated algorithms, a computer vision system can distinguish unusual behavior, track poultry activities and observe its environmental conditions. Furthermore, this study discovers the benefits of computer vision in poultry farming, including early detection of disease, better production efficiency and improved animal welfare. In conclusion, the application of computer vision in monitoring poultry holds immense potential for the industry; by providing a corridor to a more sustainable and ethical poultry farming system with increased productivity. This paper equally discusses recent developments, challenges, forthcoming prospects in the field and academic research gaps identified.

Investigating micro-cutting mechanism of glow discharge polymer based on material properties and removal behaviors analysis

Glow discharge polymer (GDP) is the unique artificial material for the target balls in Inertial Confinement Fusion tests, while its practical micromachining such as the fabrication of microstructure on material surfaces keeps challenging due to its unclear micro-cutting mechanism. Hence, this paper seeks to investigate the micro-cutting mechanism of GDP from the perspective of the material flow and cutting energy. To achieve it, the energy conservation equation of three cutting modes triggered by different ratios of uncut chip thickness to the tool cutting edge radius (RTS) was established based on cutting deformation behaviors. Meanwhile, the diamond cutting tests and the FEM simulation at different RTS were developed. The experimental observation of cutting forces and specific cutting forces verified the evolution of three cutting modes, including shearing, shearing-ploughing and ploughing in the micro-cutting of GDP with the decrease of RTS. Next, from the change of the node displacement vector observed from the simulated results, it can be seen that the real-time material flow behavior during micro-cutting of GDP varies obviously with the evolution of cutting modes. Besides, the fracture toughness Gc, and the energy dissipation of different cutting modes were analyzed. The proportion of the energy spent on material fracture (Gc=9.95 N/mm) is the largest one in shearing and shearing-ploughing modes, while in ploughing mode, the material plastic deformation consumed the most energy. The above results reveal the specific material properties and removal behaviors of GDP and contribute to optimizing the machining strategies for the practical micromachining of microstructures on material surfaces.

Mechatronic automatic control system of electropneumatic manipulator

Mechatronic systems of electropneumatic automation are one of the main classes of industrial automation systems. A laboratory stand for the study of the mechatronic system of automatic control of the pneumatic manipulator and a computer model for preliminary experiments on the adjustment of the automatic control system were developed. Manual and software control modes are provided for research of indicators of safety and quality of management in both modes. To implement the software control mode, a microcontroller part of the laboratory stand based on ADuC841 was developed, with the help of which it is possible to simulate a part of a certain technological process, to detect and eliminate faults in the automatic control system. A study of automatic control systems using a traditional relay-contactor control system, based on GrafCet technology and using a virtual controller. The combination of computer modeling of technological processes and physical modeling of executive mechanisms is a kind of digital double that displays its state, parameters and behavior in real time. The use of a laboratory stand in combination with an adequate simulation model reduces the complexity of developing control systems for practical applications, and also contributes to the formation of students' creative component, ability to analyze the results, and make decisions in unusual situations, which will increase their theoretical and practical training. The study of mechatronic systems of pneumatic manipulators will allow to increase their efficiency and productivity, to optimize their speed and accuracy for various applications in production. The interaction of mechatronic systems of pneumatic manipulators with other technologies, such as machine learning, artificial intelligence, IoT is the basis for creating more integrated and intelligent systems.

Data Acquisition and Performance Analysis during Real-Time Driving of a Two-Wheeler Electric Vehicle—A Case Study

Data acquisition from a vehicle operating in real driving conditions is extremely useful for analyzing the real-time behavior of the vehicle and its components. A few studies have measured the real-time data for a four-wheeler electric vehicle. However, no attempts have been reported to measure the real-time data and find the inverter efficiency for a two-wheeler electric vehicle. The present work has accomplished successful real-time data acquisition from a two-wheeler electric vehicle. The real-time current and voltage coming in and going out from the inverter, frequency of the motor operation, power factor, distance covered, and velocity have been measured. The inverter efficiency is found to be over 95% for over 80% of the total drive time, and the power factor for the motor is over 0.8 for almost 50% of the total drive time. A few insights on driver behavior and finally the torque-speed characteristics and two quadrant operation of the motor are discussed.

Waterproof, stretchable and wearable corrugated conductive carbon fiber strain sensors for underwater respiration monitoring and swimming instruction

Respiration is one of crucial vital signs, which can be used for health assessment and sports management. Flexible resistive strain sensors provide an opportunity to monitor personal respiratory behaviors in real time due to high sensitivity, good linearity and fast response. However, there are some critical issues with the resistive strain sensor, such as strain mismatch, poor contact properties and moisture sensitivity, which easily cause unexpected electrical response, limiting its application scenarios. Here, a novel stretchable carbon fiber strain sensor is developed by a simple structural design, which endow the device with a remarkable linearity (R2=0.99601 in 0–40 % tensile strain), excellent anti-jamming performance, long-term durability, and waterproofness. Moreover, a sophisticated wearable respiratory monitoring system is further developed, which can not only precisely distinguish diverse respiratory patterns to enhance the accuracy in early illness diagnosis but also remotely capture real-time breathing information during swimming for guiding swimming training. It demonstrates that our stretchable resistive strain sensor has an enormous application potential in wearable electronics and smart clothing for health monitoring and training guide.

Realtime polymorphic malicious behavior detection in blockchain-based smart contracts

Abstract This paper proposes an innovative approach to achieving real-time polymorphic behavior detection, and its direct application to blockchain-focused smart-contracts. We devise a method based on a non-deterministic finite state machine to perform approximate pattern-matching, using a look-ahead mechanism implemented through a concept similar to that of a sliding window, and using threshold-based similarity checking at every state in the automaton. We introduce and formalize our approach, discuss the challenges we faced and then test it in a real-world environment. The experimental results obtained showed a significant speed-up of our approach, as compared to the classic similarity measures used commonly in such scenarios.

Enhancing Embedded Object Tracking: A Hardware Acceleration Approach for Real-Time Predictability.

While Siamese object tracking has witnessed significant advancements, its hard real-time behaviour on embedded devices remains inadequately addressed. In many application cases, an embedded implementation should not only have a minimal execution latency, but this latency should ideally also have zero variance, i.e., be predictable. This study aims to address this issue by meticulously analysing real-time predictability across different components of a deep-learning-based video object tracking system. Our detailed experiments not only indicate the superiority of Field-Programmable Gate Array (FPGA) implementations in terms of hard real-time behaviour but also unveil important time predictability bottlenecks. We introduce dedicated hardware accelerators for key processes, focusing on depth-wise cross-correlation and padding operations, utilizing high-level synthesis (HLS). Implemented on a KV260 board, our enhanced tracker exhibits not only a speed up, with a factor of 6.6, in mean execution time but also significant improvements in hard real-time predictability by yielding 11 times less latency variation as compared to our baseline. A subsequent analysis of power consumption reveals our approach's contribution to enhanced power efficiency. These advancements underscore the crucial role of hardware acceleration in realizing time-predictable object tracking on embedded systems, setting new standards for future hardware-software co-design endeavours in this domain.

Real-time system call-based ransomware detection

Ransomware, particularly crypto ransomware, has emerged as the go-to malware for threat actors aiming to compromise data on Android devices as well as in general. In this paper, we present a ransomware detection technique based on behaviours observed in the system calls performed by the malware. We first describe our repeatable and extensible methodology for extracting the system call log and patterns. We then identify and present some common high-level system call behavioural patterns exhibited by crypto ransomware, and evaluate these patterns. We further describe the implementation of a streaming implementation that utilises regular expressions for modelling malware behaviours and finite state machines for detecting crypto ransomware behaviours in real time. The success of our proof of concept evaluation allows us to envision our proposed technique applied as part of a self-protection system on Android phones against malware.