Search Results (64)

Due to their vast number and heterogeneity, local public administrations can act as entry points (or attack surfaces) for adversaries targeting national infrastructure. The individual vulnerabilities of these entities function as entry points that can be exploited to compromise higher-level government assets. This study presents a nationwide risk analysis of the exposed perimeter of 7000 municipalities, achieved through the massive collection of 93 technological and contextual variables over three consecutive years and the application of supervised machine learning algorithms. The findings of this study demonstrate that geographical factors are a key predictor of external perimeter cyber risk, suggesting that supra-local entities providing unified, shared security services are better positioned in terms of risk exposure and therefore more resilient. Furthermore, the analysis confirms, contrary to conventional wisdom, that IT budget allocation lacks a significant statistical correlation with external perimeter risk mitigation. It is concluded that large-scale data collection frameworks, enhanced by Artificial Intelligence, provide policymakers with an objective and transparent tool to optimize cybersecurity investments and protection strategies. Full article

For organizations, protecting computer networks has always been a very tough and demanding task. In the current technological era digital resources can now be protected without the need for outdated traditional perimeter-based security techniques. Organizations can use the Zero Trust Network Access (ZTNA) approach to safeguard and filter their vital digital assets for the company’s benefit. This platform uses sophisticated logical authentication to test the system’s ability to authenticate users, and network monitoring is used to look for possible security flaws and system vulnerabilities. By evaluating users’ interactions with the system and their handling of assigned digital resources, multi-factor authentication filters out unwanted access attempts. Three fundamental access control styles are provided by network segmentation, giving administrators the option to manage access in a democratic, strict, or flexible manner (least privilege approach). Full article

In the forests of Jambi Province, Indonesia, the Indigenous Suku Anak Dalam have encountered rapid alterations to the environment upon which they previously depended. Their culinary traditions—and the knowledge that accompanies them—are placed at a greater risk as palm oil plantations expand and forest areas diminish. This research is based on extensive interviews with customary leaders (called Tumenggung, who guide communal life and cultural practices), elders, and women in five settlements in Merangin District. Rather than regarding participants as research subjects, we engaged with their narratives. The image that emerged was not merely one of food scarcity but also one of cultural loss. Instead of forest tubers, untamed fruits, or fish, families now depend on instant noodles or cassava. The rivers are no longer clean, and the trees that were once a source of both sustenance and medicine are largely extinct. Nevertheless, individuals devise strategies to adapt, including cultivating small crops in the vicinity of their dwellings, collecting what is left along the plantation’s perimeter, and distributing their meager possessions to their neighbors. This research demonstrates that food security for Indigenous peoples is not solely dependent on agriculture or nutrition. It is about the right to have a voice in one’s own land, dignity, and memory. Genuine solutions must transcend technical fixes and nutritional aid. The first step is to respect Indigenous voices, protect their territories, and support their methods of knowing and living before they are also lost. Full article

This review systematically examines intelligent event perception in distributed acoustic sensing (DAS) systems. Beginning with the elucidation of the DAS principles, system architectures, and core performance metrics, it establishes a comprehensive theoretical framework for evaluation. This study subsequently delineates methodological innovations in both traditional machine learning and deep learning approaches for event perception, accompanied by performance optimization strategies. Particular emphasis was placed on advances in hybrid architectures and intelligent sensing strategies that achieve an optimal balance between computational efficiency and detection accuracy. Representative applications spanning traffic monitoring, perimeter security, infrastructure inspection, and seismic early warning systems demonstrate the cross-domain adaptability of the technology. Finally, this review addresses critical challenges, including data scarcity and environmental noise interference, while outlining future research directions. This work provides a systematic reference for advancing both the theoretical and applied aspects of DAS technology, while highlighting its transformative potential in the development of smart cities. Full article

This work formulates and solves the actual problem of studying the logistics of unmanned aerial vehicle (UAV) operations in facility security planning. The study is related to security tasks, including perimeter control, infrastructure condition monitoring, prevention of unauthorized access, and analysis of potential threats. Thus, the topic of the proposed publication is relevant as it examines the sequence of logistical actions in the large-scale application of a swarm of drones for facility protection. The purpose of the research is to create a set of mathematical and simulation models that can be used to analyze the capabilities of a drone swarm when organizing security measures. The article analyzes modern problems of using a drone swarm: formation of the swarm, assessment of its potential capabilities, organization of patrols, development of monitoring scenarios, planning of drone routes and assessment of the effectiveness of the security system. Special attention is paid to the possibilities of wave patrols to provide continuous surveillance of the object. In order to form a drone swarm and possibly divide it into groups sent to different surveillance zones, the necessary UAV capacity to effectively perform security tasks is assessed. Possible security scenarios using drone waves are developed as follows: single patrolling with limited resources; two-wave patrolling; and multi-stage patrolling for complete coverage of the protected area with the required number of UAVs. To select priority monitoring areas, the functional potential of drones and current risks are taken into account. An optimization model of rational distribution of drones into groups to ensure effective control of the protected area is created. Possible variants of drone group formation are analyzed as follows: allocation of one priority surveillance zone, formation of a set of key zones, or even distribution of swarm resources along the entire perimeter. Possible scenarios for dividing the drone swarm in flight are developed as follows: dividing the swarm into groups at the launch stage, dividing the swarm at a given navigation point on the route, and repeatedly dividing the swarm at different patrol points. An original algorithm for the formation of drone flight routes for object surveillance based on the simulation modeling of the movement of virtual objects simulating drones has been developed. An agent-based model on the AnyLogic platform was created to study the logistics of security operations. The scientific novelty of the study is related to the actual task of forming possible strategies for using a swarm of drones to provide integrated security of objects, which contributes to improving the efficiency of security and monitoring systems. The results of the study can be used by specialists in security, logistics, infrastructure monitoring and other areas related to the use of drone swarms for effective control and protection of facilities. Full article

The global market for infrastructure security systems based on distributed acoustic sensors is rapidly expanding, driven by the need for timely detection and prevention of potential threats. However, deploying these systems is challenging due to the high costs associated with dataset creation. Additionally, advanced signal processing algorithms are necessary for accurately determining the location and nature of detected events. In this paper, we present an enhanced approach based on semi-supervised learning for developing event classification models tailored for real-time and continuous perimeter monitoring of infrastructure facilities. The proposed method leverages a hybrid architecture combining an autoencoder and a classifier to enhance the accuracy and efficiency of event classification. The autoencoder extracts essential features from raw data using unlabeled data, improving the model’s ability to learn meaningful representations. The classifier, trained on labeled data, recognizes and classifies specific events based on these features. The integrated loss function incorporates elements from both the autoencoder and the classifier, guiding the autoencoder to extract features relevant for accurate event classification. Validation using real-world datasets demonstrates that the proposed method achieves recognition performance comparable to the baseline model, while requiring less labeled data and employing a simpler architecture. These results offer practical insights for reducing deployment costs, enhancing system performance, and increasing throughput for new deployments. Full article

The rapid evolution and deployment of 5G networks have introduced complex security challenges due to their reliance on dynamic network slicing, ultra-low latency communication, decentralized architectures, and highly diverse use cases. Traditional perimeter-based security models are no longer sufficient in these highly fluid and distributed environments. In response to these limitations, this study introduces SecureChain-ZT, a novel Adaptive Zero Trust Policy Framework (AZTPF) that addresses emerging threats by integrating intelligent access control, real-time monitoring, and decentralized authentication mechanisms. SecureChain-ZT advances conventional Zero Trust Architecture (ZTA) by leveraging machine learning, reinforcement learning, and blockchain technologies to achieve autonomous policy enforcement and threat mitigation. Unlike static ZT models that depend on predefined rule sets, AZTPF continuously evaluates user and device behavior in real time, detects anomalies through AI-powered traffic analysis, and dynamically updates access policies based on contextual risk assessments. Comprehensive simulations and experiments demonstrate the robustness of the framework. SecureChain-ZT achieves an authentication accuracy of 97.8% and reduces unauthorized access attempts from 17.5% to just 2.2%. Its advanced detection capabilities achieve a threat detection accuracy of 99.3% and block 95.6% of attempted cyber intrusions. The implementation of blockchain-based identity verification reduces spoofing incidents by 97%, while microsegmentation limits lateral movement attacks by 75%. The proposed SecureChain-ZT model achieved an authentication accuracy of 98.6%, reduced false acceptance and rejection rates to 1.2% and 0.2% respectively, and improved policy update time to 180 ms. Compared to traditional models, the overall latency was reduced by 62.6%, and threat detection accuracy increased to 99.3%. These results highlight the model’s effectiveness in both cybersecurity enhancement and real-time service responsiveness. This research contributes to the advancement of Zero Trust security models by presenting a scalable, resilient, and adaptive policy enforcement framework that aligns with the demands of next-generation 5G infrastructures. The proposed SecureChain-ZT model not only enhances cybersecurity but also ensures service reliability and responsiveness in complex and mission-critical environments. Full article

Gated communities have been widely examined as a contemporary urban phenomenon, yet their emergence in the Middle East reflects broader socioeconomic and cultural transformations rather than a direct continuation of historical spatial practices. Historically, Arabian cities featured compact, human-scaled urban layouts with walled perimeters, narrow streets, and shared courtyards, fostering social cohesion, security, and communal interaction. These spatial characteristics evolved organically, balancing privacy with integration to meet communal needs. This article examines the historical evolution of enclosed neighbourhoods in Arabian cities and their sociospatial connections to modern gated communities, assessing their impact on urban sustainability. By employing historical inquiry, this study investigates how traditional principles, such as privacy, community resilience, and spatial hierarchy, have been inherited, reinterpreted, or redefined in contemporary developments. Findings indicate that historical Arabian cities reinforced internal cohesion and self-governance, whereas modern gated communities introduce deliberate spatial and social segregation, disrupting urban connectivity and weakening social sustainability. The study highlights critical implications for urban planning, suggesting that integrating historical spatial principles can create inclusive and adaptable contemporary developments. Full article

This study proposes a Zero Trust security framework for 6G-enabled Cyber-Physical Systems (CPS), integrating Adaptive Access Control (AAC), end-to-end encryption, and blockchain to enhance security, scalability, and real-time threat detection. As 6G networks facilitate massive device connectivity and low-latency communication, traditional perimeter-based security models are inadequate against evolving cyber threats such as Man-in-the-Middle (MITM) attacks, Distributed Denial-of-Service (DDoS), and data breaches. Zero Trust security eliminates implicit trust by enforcing continuous authentication, strict access control, and real-time anomaly detection to mitigate potential threats dynamically. The proposed framework leverages blockchain technology to ensure tamper-proof data integrity and decentralized authentication, preventing unauthorized modifications to CPS data. Additionally, AI-driven anomaly detection identifies suspicious behavior in real time, optimizing security response mechanisms and reducing false positives. Experimental evaluations demonstrate a 40% reduction in MITM attack success rates, 5.8% improvement in authentication efficiency, and 63.5% lower latency compared to traditional security methods. The framework also achieves high scalability and energy efficiency, maintaining consistent throughput and response times across large-scale CPS deployments. These findings underscore the transformative potential of Zero Trust security in 6G-enabled CPS, particularly in mission-critical applications such as healthcare, smart infrastructure, and industrial automation. By integrating blockchain-based authentication, AI-powered threat detection, and adaptive access control, this research presents a scalable and resource-efficient solution for securing next-generation CPS architectures. Future work will explore quantum-safe cryptography and federated learning to further enhance security, ensuring long-term resilience in highly dynamic network environments. Full article

Accurately identifying optical fiber vibration signals is crucial for ensuring the proper operation of optical fiber perimeter security warning systems. To enhance the recognition accuracy of intrusion events detected by the distributed acoustic sensing system (DAS) based on phase-sensitive optical time-domain reflectometer (φ-OTDR) technology, we propose an identification method that combines empirical mode decomposition (EMD) with convolutional neural networks (CNNs) and long short-term memory (LSTM) networks. First, the EMD algorithm decomposes the collected original optical fiber vibration signal into several intrinsic mode functions (IMFs), and the correlation coefficient between each IMF and the original signal is calculated. The signal is then reconstructed by selecting effective IMF components based on a suitable threshold. This reconstructed signal serves as the input for the network. CNN is used to extract time-series features from the vibration signal and LSTM is employed to classify the reconstructed signal. Experimental results demonstrate that this method effectively identifies three different types of vibration signals collected from a real-world environment, achieving a recognition accuracy of 97.3% for intrusion signals. This method successfully addresses the challenge of φ-OTDR pattern recognition and provides valuable insights for the development of practical engineering products. Full article

In Burkina Faso, small-scale, community-managed irrigation systems play a crucial role in stabilizing agricultural production and improving food security. Over the past three decades, the state has transferred the management of these irrigation systems to local farmer organizations in the hope of improving efficiency and sustainability. This study assesses the long-term performance of six irrigation perimeters Dakiri, Gorgo, Itenga, Mogtedo, Savili, and Wedbila through an in-depth analysis of governance models, infrastructure conditions, and financial sustainability. Performance indicators such as relative water supply (RWS), gross production per unit of irrigation water (PbIr), and water charge recovery rates were used to assess the effectiveness of farmer-led irrigation management. The results reveal persistent governance and financial challenges as well as issues such as water wastage and low yield persisting, despite decades of implementation of farmer-led management. The degradation of irrigation infrastructure, coupled with declining water fee collection rates, threatens the sustainability of these systems. A comparative analysis of international cases suggests that a hybrid governance model, in which the state provides technical and financial support while strengthening accountability mechanisms, could improve the performance of these irrigation systems. This study recommends a shift towards greater state intervention, improved financial mechanisms, and the adoption of digital monitoring tools to ensure a more efficient and sustainable management framework. Full article

Edge processing is a trend in developing new technologies that leverage Artificial Intelligence (AI) without transmitting large volumes of data to centralized processing services. This technique is particularly relevant for security applications where there is a need to reduce the probability of intrusion or data breaches and to decentralize alert systems. Although drone detection has received great research attention, the ability to identify helicopters expands the spectrum of aerial threats that can be detected. In this work, we present the development of a perimeter alert system that integrates AI and multiple sensors processed at the edge. The proposed system can be integrated into a LoRa or LoRaWAN network powered by solar energy. The system incorporates a PDM microphone based on an Arduino Nano 33 BLE with a trained model to identify a drone or a UH-60 from an audio spectrogram to demonstrate its functionality. It is complemented by two PIR motion sensors and a microwave sensor with a range of up to 11 m. Additionally, the DC magnetic field is measured to identify possible sensor movements or changes caused by large bodies, and a configurable RGB light signal visually indicates motion or sound detection. The monitoring system communicates with a second MCU integrated with a LoRa or LoRaWAN communication module, enabling information transmission over distances of up to several kilometers. The system is powered by a LiPo battery, which is recharged using solar energy. The perimeter alert system offers numerous advantages, including edge processing for enhanced data privacy and reduced latency, integrating multiple sensors for increased accuracy, and a decentralized approach to improving security. Its compatibility with LoRa or LoRaWAN networks enables long-range communication, while solar-powered operation reduces environmental impact. These features position the perimeter alert system as a versatile and powerful solution for various applications, including border control, private property protection, and critical infrastructure monitoring. The evaluation results show notable progress in the acoustic detection of helicopters and drones under controlled conditions. Finally, all the original data presented in the study are openly available in an OSF repository. Full article

The number of vulnerabilities identified annually has increased substantially, thereby raising the risks associated with online services. The implementation of cybersecurity management measures in accordance with the European NIS2 Directive is optional at the local authority level. This study analyzes the external perimeter of nearly 7000 municipalities and proposes a simplified security framework that provides a comprehensive view of security across regions. A complete data set was assembled on the Technological and Competence profiles of all municipalities in Spain over a two-year period. The data were gathered from the external perimeter in relation to security, availability and SEO posture areas. A survey was conducted to determine the level of concern among citizens regarding cybersecurity issues in online municipal services, with 188 respondents. Some regions were identified as exhibiting particularly high and homogeneous levels of security. In contrast, other regions were found to be below the expected level. The presence of supra-local entities, such as the “Diputaciones”, has been demonstrated to facilitate the harmonization of regional security, while simultaneously reducing technological fragmentation and operational expenditure. Full article

Mobile monitoring systems are currently used in many applications related to environmental applications or the monitoring of health status. However, security monitoring systems are usually chosen for a specific object, area or perimeter. The main goal of our article is to present the mechanical design of mobile monitoring units. These units create the basis of a developed mobile monitoring security system, which can be applied to monitor any area of interest, even in demanding weather conditions, involving, for example, windiness or wide operational temperature ranges. Therefore, this article is focused on the mechanical design of mobile monitoring units, which are constructed not only so that they can withstand challenging environmental conditions, but also with regard to their simple transportation, manufacturing process and, if necessary, possible repairs. During the design, emphasis was also placed on the vibrations of the mobile monitoring units and their temperature dependence, because vibrations can significantly affect the correct functioning of the mobile monitoring security system and cause false alarm situations. To confirm the correctness of the simulation models, experiments were performed on the mobile monitoring unit prototypes. Full article

Security grids consisting of High-Definition (HD) Internet of Things (IoT) cameras are gaining popularity for organizational perimeter surveillance and security monitoring. Transmitting HD video data to cloud infrastructure requires high bandwidth and more storage space than text, audio, and image data. It becomes more challenging for large-scale organizations with massive security grids to minimize cloud network bandwidth and storage costs. This paper presents an application of Machine Vision at the IoT Edge (Mez) technology in association with a novel Grid Sensing (GRS) algorithm to optimize cloud Infrastructure as a Service (IaaS) resource allocation, leading to cost minimization. Experimental results demonstrated a 31.29% reduction in bandwidth and a 22.43% reduction in storage requirements. The Mez technology offers a network latency feedback module with knobs for transforming video frames to adjust to the latency sensitivity. The association of the GRS algorithm introduces its compatibility in the IoT camera-driven security grid by automatically ranking the existing bandwidth requirements by different IoT nodes. As a result, the proposed system minimizes the entire grid’s throughput, contributing to significant cloud resource optimization. Full article