Threat Monitoring Research Articles

Wazuh SIEM for Cyber Security and Threat Mitigation in Apparel Industries

Security of IT infrastructure is critical in the modern digital environment, particularly for industries that manufacture clothing. The application of the Wazuh Security Information and Event Management (SIEM) system to improve security monitoring and compliance for an IT system in the apparel industry is the main topic of this study. To improve the apparel industry cyber threat monitoring system in real-time monitoring, detecting the threat vectors that support the centralized management system, to meet these objectives of the company, an SIEM security management system has been installed and configured with the components of Wazuh manager, indexer, Wazuh agent, and Wazuh dashboard as well. An Oracle virtual lab environment was created to install and configure the Wazuh system a robust Linux operating system was installed at the server level and Windows 10 was installed on the client end to the authenticity failure of the security system. To implement the Wazuh system, the project management guideline was followed which ensured the planning and implementation of the project, testing phases, and along with review and maintenance. Wazuh security management system successfully identified the authentical failure reports and vulnerabilities and generated automated reports from the system that showed the result of 1 critical, 19 high, and 5 medium levels of vulnerabilities shown in Figure 6, and the necessary patch recommended to solve the critical issues. On the other hand, the system also generated security events as well as integrity monitoring system reports. The deployment of the Wazuh security management system ensured the robust cost-effective security management system with high quality for detecting cyber threats which supports the regulatory and compliance requirements and is a viable tool for the apparel industry to protect the stakeholders’ interest.

Securing educational institutions against an active killer on the example of the United States of America. Best practices and recommendations

Educational institutions can become targets for active killers and this threat seems greater today than it did a few years ago. The aim of the article is to describe the main elements and proposals for securing these facilities against attacks of this type of perpetrators, which are recommended in the United States, and then to indicate solutions that could be introduced in Polish facilities. They concern both physical security and broadly understood prevention and monitoring of threats.

Cybersecurity Threats and Vulnerabilities in Online Banking Systems

The rapid expansion of online banking has introduced significant convenience and accessibility for consumers and financial institutions alike. However, it also brings a substantial increase in cybersecurity threats, making online banking systems prime targets for cybercriminals. This paper provides a comprehensive examination of the prevalent cybersecurity threats that online banking faces, including phishing attacks, malware, ransomware, man-in-the-middle (MITM) attacks, insider threats, and distributed denial-of-service (DDoS) attacks. We analyze these threats in-depth, exploring how each tactic is deployed to compromise security and exploit vulnerabilities within online banking systems. Moreover, this paper discusses specific vulnerabilities that exist in online banking platforms, such as weak authentication practices, insecure network connections, outdated software, and risks associated with third-party integrations. Through tables and graphical data, the paper offers a clear overview of the most common vulnerabilities and their prevalence, providing insights into how these weak points are exploited in the cyber landscape. The impact of such cybersecurity breaches on financial institutions is also considered, highlighting the consequences that follow a security breach, such as financial losses, reputational damage, regulatory fines, and customer distrust. The findings reveal that these impacts not only affect individual financial institutions but can also undermine public confidence in digital banking as a whole. Finally, the paper proposes several strategic defenses against these threats. Solutions include multi-factor authentication, end-to-end encryption, robust threat monitoring, regular security audits, and customer education initiatives, among others. Statistical data on the effectiveness of these strategies demonstrates their role in mitigating cyber risks and fortifying online banking systems against future attacks. This study concludes by emphasizing the critical need for continuous innovation in cybersecurity practices, as cyber threats continue to evolve in sophistication.

CLASSIFYING CYBERCRIME NETWORKS: ORGANIZED AND TRANSNATIONAL SCHEMES IN THE DIGITAL ERA

The purpose of this study is to analyze and classify the main schemes of organized and transnational cybercriminal activity, as well as to identify the methods and technologies used by cybercriminals to carry out criminal activities in the digital space. The article explores current trends in cybercrime, such as the rise in financial fraud, ransomware attacks, phishing, and DDoS attacks. The study shows that the use of the latest technologies, particularly cryptocurrencies and the Internet of Things (IoT), significantly complicates the detection and prosecution of cyber criminals, necessitating the constant improvement of legal and technical tools to combat cybercrime. Key findings indicate that the transnational nature of cybercrime often makes it difficult to prosecute criminals across jurisdictions, requiring greater international coordination. It was also found that cybercriminals are using increasingly sophisticated methods to conceal their activities, incorporating modern technological solutions into criminal schemes. This underscores the need for continuous monitoring of new threats and the updating of protection methods for both organizations and individual users. The main conclusions of the study emphasize the importance of global cooperation in the fight against cybercrime, the need to enhance technological protection, and the development of legal mechanisms to counter modern cyber threats.

Empirical evaluation of feature selection methods for machine learning based intrusion detection in IoT scenarios

This paper delves into the critical need for enhanced security measures within the Internet of Things (IoT) landscape due to inherent vulnerabilities in IoT devices, rendering them susceptible to various forms of cyber-attacks. The study emphasizes the importance of Intrusion Detection Systems (IDS) for continuous threat monitoring. The objective of this study was to conduct a comprehensive evaluation of feature selection (FS) methods using various machine learning (ML) techniques for classifying traffic flows within datasets containing intrusions in IoT environments. An extensive benchmark analysis of ML techniques and FS methods was performed, assessing feature selection under different approaches including Filter Feature Ranking (FFR), Filter-Feature Subset Selection (FSS), and Wrapper-based Feature Selection (WFS). FS becomes pivotal in handling vast IoT data by reducing irrelevant attributes, addressing the curse of dimensionality, enhancing model interpretability, and optimizing resources in devices with limited capacity. Key findings indicate the outperformance for traffic flows classification of certain tree-based algorithms, such as J48 or PART, against other machine learning techniques (naive Bayes, multi-layer perceptron, logistic, adaptive boosting or k-Nearest Neighbors), showcasing a good balance between performance and execution time. FS methods' advantages and drawbacks are discussed, highlighting the main differences in results obtained among different FS approaches. Filter-feature Subset Selection (FSS) approaches such as CFS could be more suitable than Filter Feature Ranking (FFR), which may select correlated attributes, or than Wrapper-based Feature Selection (WFS) methods, which may tailor attribute subsets for specific ML techniques and have lengthy execution times. In any case, reducing attributes via FS has allowed optimization of classification without compromising accuracy. In this study, F1 score classification results above 0.99, along with a reduction of over 60% in the number of attributes, have been achieved in most experiments conducted across four datasets, both in binary and multiclass modes. This work emphasizes the importance of a balanced attribute selection process, taking into account threat detection capabilities and computational complexity.

Deep hybrid approach with sequential feature extraction and classification for robust malware detection

Malware attacks have escalated significantly with an increase in the number of internet users and connected devices. With the increasingly different types of malware released by hackers, designing new and competitive techniques to detect advanced malware is essential. In the proposed research, we have developed a multi-level feature extraction technique using deep learning architectures and a classification model to classify malware families. The essential features from the malware images are extracted using the Gated Recurrent Unit in the first step, which are further fed to a Convolutional Neural Network model for extracting the final feature vector. The multi-level feature selection is followed by classification into various malware families using Cost-sensitive Boot Strapped Weighted Random Forest (CSBW-RF). The proposed approach gave promising results of 99.58 % accuracy in distinguishing the 25 different malware families on the Mallmg dataset. This hybrid model gave significantly better performance scores for classifying visually similar malware families. The generalizability of the proposed model is benchmarked with the popular Microsoft Big 2015 dataset and has achieved comparatively higher performance scores than many existing models. This benchmarking demonstrates the robustness and scalability of our approach. The use of cost-sensitive learning and bootstrapping techniques also contributed to the model’s ability to generalize well to new and unseen data. These enhancements ensure that our model can be effectively applied in diverse real-world scenarios, maintaining high performance across different environments and malware types. This research can contribute to detecting malware attacks and can be integrated in threat monitoring systems. The successful application of this hybrid model indicates its potential for deployment in real-world cybersecurity environments, providing a strong defense against evolving malware threats.

Methodology for assessing the impact of emergencies on the spread of infectious diseases

The spread of infectious diseases is significantly influenced by emergencies, particularly military conflicts, which disrupt healthcare systems and increase the risks of epidemics. The full-scale Russian invasion of Ukraine has exacerbated these challenges, causing environmental damage, mass displacement, and the breakdown of healthcare services, all of which contribute to the spread of infectious diseases. This study aims to develop a comprehensive methodology for assessing the impact of emergencies on the spread of infectious diseases, focusing on the full-scale invasion of Ukraine. The object of this study is to address epidemic threats posed by emergencies, particularly the increased spread of infectious diseases due to war-related disruptions. The subject of this study is methods and models of infectious disease transmission under conditions of emergencies, emphasizing the Russian full-scale invasion of Ukraine. The tasks of this study are to provide an analysis of the current state of research and develop a methodology for assessing the impact of emergencies on the spread of infectious diseases. The proposed methodology includes several key components. Comprehensive data from public health organizations includes infectious disease statistics, demographic shifts, healthcare disruptions, and environmental factors exacerbated by emergencies. Data preprocessing removes inconsistencies, standardization of formats, and normalization for population size differences. Machine learning models, including convolutional neural networks and recurrent neural networks, have been developed to simulate the spread of diseases based on demographic, environmental, and healthcare-related variables. Deep learning models analyze spatial and temporal patterns, whereas compartmental models such as SIR estimate changes in reproductive numbers (R₀ and Re). Additionally, models of excess mortality incorporate mixed effects to account for regional and time-based variations. The methodology incorporates real-time monitoring of epidemic threats using real-time data from multiple sources, enabling dynamic assessments of disease spread and facilitating predictive modeling. The models were trained on historical data and validated using cross-validation techniques to ensure robustness and reliability, with a specific focus on the pre- and post-invasion phases in Ukraine.Results: The study provides a comprehensive framework for collecting and processing data on infectious diseases and epidemic threats in emergencies. The proposed model introduces advanced machine learning and epidemiological models trained on pre- and post-invasion data to analyze disease transmission patterns and forecast future epidemic dynamics. Conclusion: The proposed methodology addresses current gaps in infectious disease during emergencies by integrating real-time data and machine learning techniques. This research improves decision-making in public health management and biosafety during crises, particularly in war-affected regions like Ukraine.

FIDWATCH: Federated incremental distillation for continuous monitoring of IoT security threats

The fast evolutions of Internet of Things (IoT) technologies have been accelerating their applicability in different sectors of life and becoming a pillar for sustainable development. However, this revolutionary expansion led to a substantial increase in attack surface, raising many concerns about security threats and their possible consequences. Machine learning has significantly contributed to designing intrusion detection systems (IDS) but suffers from critical limitations such as data privacy and sovereignty, data imbalance, concept drift, and catastrophic forgetting. This collectively makes existing IDSs an improper choice for securing IoT environments. This paper presents a federated learning approach called FIDWATCH to continuously monitor and detect a broad range of IoT security threats. The local side of FIDWATCH introduces contrastive focal loss to enhance the ability of the local model (teacher) to discriminate between diverse types of IoT security threats while putting an increased emphasis on hard-to-classify samples. A fine-grained Knowledge Distillation (KD) is introduced to allow the client to distill the required teacher's knowledge into a lighter, more compact model termed the pupil model. This greatly assists the competence and flexibility of the model in resource-constrained scenarios. Furthermore, an adaptive incremental updating method is introduced in FIDWATCH to allow the global model to exploit the distilled knowledge and refine the shared dataset. This helps generate global anchors for improving the robustness of the mode against the distributional shift, thereby improving model alignment and compliance with the dynamics of IoT security threats. Proof-of-concept simulations are performed on data from two public datasets (BoT-IoT and ToN-IoT), demonstrating the superiority of FIDWATCH over cutting-edge performance with an average f1-score of 97.07% and 95.63%, respectively.

Devising an integrated methodology for energy safety assessment at an industrial power-generating enterprise

This paper’s research object is a process of estimating the level of energy safety at an industrial enterprise within an electricity-power system at a microeconomic level. The task to estimate the energy safety of an industrial enterprise was solved on the basis of a combined method for the continuous monitoring of threats under an automated mode to the hour, in order to make management decisions related to reducing the influence of threats to energy safety. Analysis of known methods for investigating the state of energy safety was performed, which revealed the lack of continuous control over the dynamic state of research objects, as well as the complexity in objectivity when giving exact and reliable primary information to calculate indicators-indexes. It has been shown that the use of the combined approach could underlie the development of methodology for estimating the energy safety of industrial power-generating enterprise. Such approach involves comprehensive combination of logical methods when implementing continuous monitoring of technological parameters at generation, transmission, distribution, and energy consumption facilities. The process of choosing the objects of research and criteria for describing their properties and functions has been substantiated, in order to build the system of indicators-indexes when estimating the level of enterprise energy safety. It has been shown that a computation algorithm makes it possible to monitor the dynamics of changes in indicators and take a management decision to eliminate energy safety threats, which leads to a reduction in economic losses in the amount exceeding USD 150,000. Practical significance of the current research relates to the fact that the results could be used by enterprises (public authorities) for determining obstacles on a way to reducing the consumption of power resources and devising a set of organizational-economic measures

Data privacy and cybersecurity challenges in the digital transformation of the banking sector

In the digital transformation of the banking sector, incorporating advanced technologies such as cloud computing, big data analytics, artificial intelligence, and blockchain has revolutionized financial services. However, this rapid digitalization brings significant data privacy and cybersecurity challenges. This study investigates the challenges banks have maintaining data privacy and cybersecurity while implementing new technologies, how they perceive these challenges, and what steps they take to reduce the risks involved. This qualitative study uses thematic analysis to examine interviews conducted with IT specialists in the banking sector. NVivo 14 software is employed to identify key themes and patterns related to the challenges, perceptions, and strategies regarding data privacy and cybersecurity in technology adoption. The findings reveal that the primary challenges faced by banks include integrating legacy systems, evolving compliance management, managing vendor risks, maintaining customer confidence, and mitigating emerging risks. Banks perceive robust data privacy and cybersecurity as critical for competitive advantage, regulatory compliance, and customer trust. Strategies include robust access controls, continuous threat monitoring, employee training, regulatory compliance with governance frameworks, and data encryption. This study provides original insights into the specific challenges and strategies related to data privacy and cybersecurity faced by banks. It contributes to the existing literature by highlighting the unique context of the banking sector and employing qualitative analysis to uncover nuanced perceptions and practices of IT specialists.

Printed and Laser Induced Graphene Electrochemical Biosensors for Rapid Environmental and Biomedical Monitoring

This presentation will demonstrate how low-cost printed and flexible graphene circuits can overcome the challenges associated with conventional electrical circuits to open the door to a highly sensitive monitoring of biochemical threats and toxins including nerve agents/pesticides. The use of inkjet and aerosol printing; spin coating; and laser writing (i.e., laser induced graphene) can be used to create such circuits with high resolution (line widths as low as 20 µm). Moreover, the use of rapid-pulse laser annealing of the printed graphene and/or salt porogens added into graphene inks before printing can be used to further improve the electrical conductivity (< 10 ohms/sq.) and electroactive surface area (nano/micro structuring) of the graphene circuits as well as be used to tune the surface of the printed graphene from one that is hydrophilic (water contact angle (WCA) = 48°) to one that is superhydrophobic (WCA = 158°). More recently we demonstrate how graphene synthesis, patterning and surface wettability can all be controlled through a laser induced graphene (LIG) process that creates a high surface area graphene surface directly from polyimide. Such LIG circumvents the need to create solution-phase graphene inks for printing. These printing and laser fabrication techniques are also amenable for use on flexible and chemically/thermally sensitive surfaces including polymers and paper.Moreover, the laser etched printed graphene and LIG exhibits an inherent 3D morphology that significantly increases the electrode electroactive surface over traditional 2D planar graphene electrodes. This large increase in surface area significantly improves the loading of enzyme onto the electrode surface and subsequently increases the heterogenous charge transport during catalytic pesticide sensing. Such ion sensors displayed near-Nernstian sensitivities, wide sensing ranges over four orders of magnitude extending from the 10–2 M down to detection limits in the 10–6 M range or for fertilizer ions (nitrate, ammonium, potassium, calcium, and magnesium) reaching down to detection limits less than 1 ppm in some cases in soil and water samples. The pesticide sensing capability of the sensor is able to reach an extremely low picomolar detect limit for organophosphates (paraoxon) and to the nanomolar regime for neonicotinoids in surface waters. We also demonstrate how this flexible LIG electrodes can be coupled with polymeric microfluidics and adhered to the body for continuous monitoring of lactate, glucose, and sodium in the sweat. Finally, we demonstrate how such surface wettability patterning of the printed graphene and LIG itself through laser etching can be used to make hydrophilic LIG tracks surrounded by near superhydrophic sidewalls for open microfluidic fluid transport without the need for polymeric microfluidics. Such open microfluidic tracks enable the splitting and transport of fluid samples to distinct electrochemical sensors for multiplexed biosensing of both fertilizer ion and pesticide monitoring from water samples and assist in harvesting sprayed pesticides in the field. Figure 1

Detection and prediction of anomalous behaviors of enterprise’s employees based on data-mining and optimization algorithm

Internal employees have always been at the core of organizational security management challenges. Once an employee exhibits behaviors that threaten the organization, the resulting damage can be profound. Therefore, analyzing reasonably stored behavioral data can equip managers with effective threat monitoring and warning solutions. Through data-mining, a knowledge graph for internal threat data is deduced, and models for detecting anomalous behaviors and predicting resignations are developed. Initially, data-mining is employed to model the knowledge ontology of internal threats and construct the knowledge graph; subsequently, using the behavioral characteristics, the BP neural network is optimized with the Sparrow Search Algorithm (SSA), establishing a detection model for anomalous behaviors (SBP); additionally, behavioral sequences are processed through data feature vectorization. Utilizing SBP, the LSTM network is further optimized, creating a predictive model for employee behaviors (SLSTM); ultimately, SBP detects anomalous behaviors, while SLSTM predicts resignation intentions, thus enhancing detection strategies for at-risk employees. The integration of these models forms a comprehensive threat detection technology within the organization. The efficacy and practicality of detecting anomalous behaviors and predicting resignations using SBP and SLSTM are demonstrated, comparing them with other algorithms and analyzing potential causes of misjudgment. This work has enhanced the detection efficiency and update speed of abnormal employee behaviors, lowered the misjudgment rate, and significantly mitigated the impact of internal threats on the organization.

AI-POWERED PREDICTIVE ANALYTICS FOR INTELLECTUAL PROPERTY RISK MANAGEMENT IN SUPPLY CHAIN OPERATIONS: A BIG DATA APPROACH

The rapid advancement of technology and the increasing complexity of global supply chains have heightened the need for robust intellectual property (IP) risk management strategies. This study explores the application of artificial intelligence (AI) and big data analytics in enhancing IP risk management within supply chains. A comprehensive literature review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, identifying 578 records through database searches and an additional 90 records through other sources. After removing duplicates, 568 records were screened, with 196 full-text articles assessed for eligibility. Ultimately, 135 articles were included in the final synthesis. The findings reveal that AI-driven predictive analytics significantly enhance the detection and mitigation of IP risks by analyzing large volumes of data from various sources, such as patent filings, market trends, and social media. Big data analytics tools like Hadoop and Spark facilitate real-time monitoring and early identification of potential IP threats, providing a comprehensive view of the supply chain landscape. Several successful case studies across different industries, including pharmaceuticals, electronics, and fashion, demonstrate the practical applications of these technologies in addressing IP risks. However, the review also highlights several challenges, including data quality, scalability, model interpretability, data privacy, and integration with legacy systems. Despite these challenges, the benefits of AI and big data analytics in IP risk management are substantial, enabling organizations to protect their intellectual assets more effectively. The study underscores the need for future research to address these challenges and explore innovative solutions to maximize the potential of AI and big data analytics in IP risk management. By investing in the necessary infrastructure and expertise, organizations can enhance their resilience and maintain a competitive edge in the global market.

Justification of the Architecture a Promising Automated System for Monitoring Radiation, Chemical and Biological Environment Using Artificial Intelligence

The most progressive direction for identifying and assessing the chemical warfare situation is the introduction of technologies based on artificial intelligence. The goal of the work is to develop the architecture of a promising system for monitoring the radiation, chemical and biological situation using artificial intelligence. Research information base. Publications on the use of mathematical models in artificial intelligence (AI), available via the Internet. The research method is analytical, from general to specific. We considered the features of using artificial intelligence in automated control systems. Results and discussion. Confrontations with Ukraine and NATO are multivariate and spatial in nature, and require constant monitoring in the face of a lack of specific information about attacks being prepared and already carried out. The use of AI technologies will allow us to go beyond simply displaying the current situation, providing tools for predicting the development of events. The proposed architecture of a promising system involves the creation of a single database filled with verified information from open sources. The developed structure of the web application, which is an interactive interface for analyzing and responding to changes in the chemical warfare situation, will allow flexible switching between information layers and obtaining data in real time. Conclusion. The use of neural network technologies by NBC protection troops will make it possible to search according to specified parameters and conduct retrospective data analysis, which will significantly simplify monitoring of NBC threats for the troops and population of the Russian Federation.

Етапи та заходи антикризового управління на підприємстві

The instability of the global economic environment leads to the formation of crisis phenomena, which negatively affect the activities of enterprises and necessitate the implementation of crisis management. The purpose of the work is to study and develop the stages of conducting crisis management at the enterprise based on the identification of the type of crisis and its phases, which will enable the selection of the correct methods of combating and prevention measures. The essence of crisis management lies in systemic management of crisis situations to prevent, adapt to, and overcome their negative consequences. It solves the tasks of ensuring the stability, success of operation, and competitiveness of the enterprise in conditions of market instability by forecasting and analyzing crisis situations, evaluating external and internal threats that may lead to a crisis situation at the enterprise. The subject of crisis management by the enterprise is the factors that cause the crisis, while the object is the enterprise itself and its operational activities. The subjects of crisis management by the enterprise are all interested parties in overcoming the crisis, primarily the leadership of the enterprise. The research conducted in the article allowed to identify a certain algorithm of the sequence of crisis management at the enterprise, which includes ten consecutive stages – from diagnosing the current state of the enterprise and identifying threats to preventive monitoring of possible threats. Moreover, the sequence of stages of crisis measures depends on the type of crisis that has arisen in the external or internal environment of the enterprise. The authors concluded that enterprise management should demonstrate flexibility and the ability to quickly react to changes and, if necessary, use all tools and means of crisis management, which will help it better resist crisis situations and maintain the stability of the enterprise. To overcome manifesta- tions of macroeconomic and microeconomic crises at the enterprise, it is necessary to implement crisis measures, the list of which the enterprise will choose based on the type of crisis, its phase, scale of coverage, as well as a number of other factors.

Surveillance 5.0: Next-Gen Security Powered by Quantum AI Optimization

Surveillance 5.0, powered by Quantum AI Optimization, represents the highpoint of next-generation security, transforming traditional surveillance paradigms through the fusion of quantum-powered technologies and advanced artificial intelligence. Quantum AI Optimization stands as the essential, revolutionizing security operations for enabling real-time threat detection, proactive response approaches, and adaptive risk mitigation measures. Moreover, privacy preservation and ethical governance plays a major role in ensuring that surveillance activities maintain higher security and privacy rights. From real-time threat monitoring to emergency response coordination, Surveillance 5.0 empowers organizations across diverse sectors to safeguard assets, protect individuals, and enhance societal resilience. Lastly, prospective technologies and applications underscore the limitless potential of surveillance 5.0, with emerging technologies such as Internet of Things (IoT), artificial intelligence (AI), Blockchain, and edge computing driving continuous innovation and expanding the frontiers of security capabilities. In summary, Surveillance 5.0 represents a quantum leap forward in security, harnessing the interactions of Quantum AI Optimization to leverage protection, privacy, and ethical governance in an increasingly complex and interconnected world.

Securing Digital Lives: Personalized Measures for Comprehensive Data Protection for Home Networks

This research paper addresses the increasing cyber threats targeting home networks and the lack of comprehensive security solutions tailored for consumers. It proposes a comprehensive solution that addresses data protection, threat monitoring, and impact mitigation, which could be automated through AI and Federated Learning (FL) to simplify security for non-experts. The paper conducts a literature review and qualitative analysis to examine cyber threats and security research, proposing an architecture that utilizes FL across home devices. It discusses challenges such as handling biases and diverse devices, limitations of interoperability, and hardware requirements. The paper emphasizes the urgent need for automated and personalized cybersecurity tailored for consumers to address the escalating threats to home networks, arguing that FL has the potential to enable this while addressing privacy concerns associated with centralized AI.

A multiscalar approach to survey of military and trade architecture in Jordan: the case of Khirbet al-Khalde

AbstractThis article reports on the archaeological survey of a (military) fort and (trade) caravanserai at Khirbet al-Khalde in southern Jordan, along the eastern Roman frontier. The results reveal the site's resilience and destruction up until the present day and the need for monitoring of threats to its preservation.

Synergistic Evaluation of Computer Network Security Using Attack Graphs and Security Event Processing

This study presents an intricate evaluation framework for enhancing computer network security through converging attack graph analysis and security event processing (SEP). The researchers construct a comprehensive methodology integrating these techniques to systematically identify vulnerabilities, visualize potential attack paths, and implement real-time threat monitoring. The findings demonstrate the efficacy of this integrated approach in fortifying network defenses and minimizing exposure to cyber threats.

Edge HPC Architectures for AI-Based Video Surveillance Applications

The introduction of artificial intelligence (AI) in video surveillance systems has significantly transformed security practices, allowing for autonomous monitoring and real-time detection of threats. However, the effectiveness and efficiency of AI-powered surveillance rely heavily on the hardware infrastructure, specifically high-performance computing (HPC) architectures. This article examines the impact of different platforms for HPC edge servers, including x86 and ARM CPU-based systems and Graphics Processing Units (GPUs), on the speed and accuracy of video processing tasks. By using advanced deep learning frameworks, a video surveillance system based on YOLO object detection and DeepSort tracking algorithms is developed and evaluated. This study thoroughly assesses the strengths, limitations, and suitability of different hardware architectures for various AI-based surveillance scenarios.