Search Results (154)

In the context of globalization and the Belt and Road Initiative, maintaining the stability and security of the China Railway Express network (CRN) is critical for international logistics operations. However, unexpected events can lead to node and edge failures within the CRN, potentially triggering cascading failures that critically compromise network performance. This study introduces a Coupled Map Lattice model that incorporates cargo flow dynamics, distributing cargo based on distance and the residual capacity of neighboring nodes. We analyze cascading failures in the CRN under three scenarios, isolated node failure, isolated edge disruption, and simultaneous node and edge failure, to assess the network’s vulnerability during emergencies. Our findings show that deliberate attacks targeting cities with high node strength result in more significant damage than attacks on cities with a high node degree or betweenness. Additionally, when edges are disrupted by unexpected events, the impact of edge removals on cascading failures depends on their strategic position and connections within the network, not just their betweenness and weight. The study further reveals that removing collinear edges can effectively slow the propagation of cascading failures in response to deliberate attacks. Furthermore, a single-factor cargo flow allocation method significantly enhances the network’s resilience against edge failures compared to node failures. These insights provide practical guidance and strategic support for the CR Express in mitigating the effects of both unforeseen events and intentional attacks. Full article

Nowadays, the world witnesses cyber attacks daily, and these threats are becoming exponentially sophisticated due to advances in Artificial Intelligence (AI). This progress allows adversaries to accelerate malware development and streamline the exploitation process. The motives vary, and so do the consequences. Unlike Information Technology (IT) breaches, Operational Technology (OT)—such as manufacturing plants, electric grids, or water and wastewater facilities—compromises can have life-threatening or environmentally hazardous consequences. For that reason, this article explores a potential cyber attack against an OT environment—firmware downgrade—and proposes a solution for detection and response by implementing Microsoft Defender for IoT (D4IoT), one of the leading products on the market for OT monitoring. To detect the malicious firmware downgrade activity, D4IoT was implemented in a pre-commissioning (non-production) environment. The solution passively monitored the network, identified the deviation, and generated alerts for response actions. Testing showed that D4IoT effectively detected the firmware downgrade attempts based on a protocol analysis and asset behavior profiling. These findings demonstrate that D4IoT provides valuable detection capabilities against an intentional firmware downgrade designed to exploit known vulnerabilities in the older, less secure version, thereby strengthening the cybersecurity posture of OT environments. The explored attack scenario leverages the symmetry between genuine and malicious firmware flows, where the downgrade mimics the upgrade process, aiming to create challenges in detection. The proposed solution discerns adversarial actions from legitimate firmware changes by breaking this functional symmetry through behavioral profiling. Full article

Phishing attacks continue to pose serious challenges to cybersecurity, with attackers constantly refining their methods to bypass detection systems. One particularly evasive technique is Zero-Font phishing, which involves the insertion of invisible or zero-sized characters into email content to deceive both users and traditional email filters. Because these characters are not visible to human readers but still processed by email systems, they can be used to evade detection by traditional email filters, obscuring malicious intent in ways that bypass basic content inspection. This study introduces a proactive phishing detection tool capable of identifying both traditional and Zero-Font phishing attempts. The proposed tool leverages a multi-layered security framework, combining structural inspection and machine learning-based classification to detect both traditional and Zero-Font phishing attempts. At its core, the system incorporates an advanced machine learning model trained on a well-established dataset comprising both phishing and legitimate emails. The model alone achieves an accuracy rate of up to 98.8%, contributing significantly to the overall effectiveness of the tool. This hybrid approach enhances the system’s robustness and detection accuracy across diverse phishing scenarios. The findings underscore the importance of multi-faceted detection mechanisms and contribute to the development of more resilient defenses in the ever-evolving landscape of cybersecurity threats. Full article

As B5G systems are evolving toward 6G, their coordination increasingly relies on AI-driven automation and orchestration actions, a process that is characterized as cognition. Therefore, a 6G system, through this cognitive process, acts as an intent-handling entity that comprehends sophisticated intent semantics from the users/tenants and calculates the ideal goal state for the specific intent, organizing the necessary adaptation actions that are needed for the transition of the system into that state. However, the use of cognitive-driven AI models to coordinate the purposes of a 6G system creates new risks, as a new surface of attack is born, where the whole 6G system operation may be maliciously affected by adversarial attacks within the user-intents. Focusing on this challenge, this paper realizes a prototype cognitive coordinator for 6G trustworthiness provision and investigates its adversarial robustness for different BERT-based quantification models, which are used for realizing the 6G cognitive system. Full article

(1) Background: Traumatic brain injury (TBI) is caused from rapid head acceleration/deceleration, focal blows, blasts, penetrating forces, and/or shearing forces, whereas hypoxic–anoxic injury (HAI) is caused through oxygen deprivation events, including strangulation. Most service-seeking domestic violence (DV) survivors have prior mechanistic exposures that can lead to both injuries. At the time of our study, some evidence existed about the exposure to both injuries over the course of a survivor’s lifetime from abuse sources, yet little was known about their co-occurrence to the same survivor within the same episode of physical intimate partner violence (IPV). To better understand the lived experience of service-seeking DV survivors and the context in which partner-inflicted brain injury (PIBI) is sustained, we sought to understand intentional brain injury (BI) exposures that may need to be addressed and accommodated in services. Our aims were to 1. characterize the lifetime co-occurrence of strangulation and intentional head trauma exposures from all abuse sources to the same survivor and within select physical episodes of IPV and 2. establish the lifetime prevalence of PIBI. (2) Methods: Survivors seeking DV services in the state of Ohio in the United States of America (U.S.) completed interview-administered surveys in 2019 (n = 47). Community-based participatory action approaches guided all aspects of the study development, implementation, and interpretation. (3) Results: The sample was primarily women. Over 40% reported having Medicaid, the government-provided health insurance for the poor. Half had less than a postsecondary education. Over 80% of participants presented to DV services with both intentional head trauma and strangulation exposures across their lifetime from intimate partners and other abuse sources (i.e., child abuse, family violence, peer violence, sexual assault, etc.), though not always experienced at the same time. Nearly 50% reported an experience of concurrent head trauma and strangulation in either the first or last physical IPV episode. Following a partner’s attack, just over 60% reported ever having blacked out or lost consciousness—44% experienced a loss of consciousness (LOC) more than once—indicating a conservative estimate of a probable brain injury by an intimate partner. Over 80% of service-seeking DV survivors reported either a LOC or two or more alterations in consciousness (AICs) following an IPV attack and were classified as ever having a partner-inflicted brain injury. (4) Conclusions: Most service-seeking IPV survivors experience repetitive and concurrent exposures to abusive strangulation and head trauma through the life course and by intimate partners within the same violent event resulting in brain injury. We propose the use of the term partner-inflicted brain injury (PIBI) to describe the physiological disruption of normal brain functions caused by intentional, often concurrent and repeated, traumatic and hypoxic neurologic insults by an intimate partner within the context of ongoing psychological trauma, coercive control, and often past abuse exposures that could also result in chronic brain injury. We discuss CARE (Connect, Acknowledge, Respond, Evaluate), a brain-injury-aware enhancement to service delivery. CARE improved trauma-informed practices at organizations serving DV survivors because staff felt knowledgeable to address and accommodate brain injuries. Survivor behavior was then interpreted by staff as a “can’t” not a “won’t”, and social and functional supports were offered. Full article

Artificial Intelligence (AI) security research is promising and highly valuable in the current decade. In particular, deep neural network (DNN) security is receiving increased attention. Although DNNs have recently emerged as a prominent tool for addressing complex challenges across various machine learning (ML) tasks and DNNs stand out as the most widely employed, as well as holding a significant share in both research and industry, DNNs exhibit vulnerabilities to adversarial attacks where slight but intentional perturbations can deceive DNNs models. Consequently, several studies have proposed that DNNs are exposed to new attacks. Given the increasing prevalence of these attacks, researchers need to explore countermeasures that mitigate the associated risks and enhance the reliability of adapting DNNs to various critical applications. As a result, DNNs have been protected against adversarial attacks using a variety of defense mechanisms. Our primary focus is DNN as a foundational technology across all ML tasks. In this work, we comprehensively survey and present the latest research on DNN security based on various ML tasks, highlighting the adversarial attacks that cause DNNs to fail and the defense strategies that protect the DNNs. We review, explore, and elucidate the operational mechanisms of prevailing adversarial attacks and defense mechanisms applicable to all ML tasks utilizing DNN. Our review presents a detailed taxonomy for attacker and defender problems, providing a comprehensive and robust review of most state-of-the-art attacks and defenses in recent years. Additionally, we thoroughly examine the most recent systematic review concerning the measures used to evaluate the success of attack or defense methods. Finally, we address current challenges and open issues in this field and future research directions. Full article

Large language models (LLMs) have made significant strides in generating coherent and contextually relevant responses across diverse domains. However, these advancements have also led to an increase in adversarial attacks, such as prompt injection, where attackers embed malicious instructions within prompts to bypass security filters and manipulate LLM outputs. Various injection techniques, including masking and encoding sensitive words, have been employed to circumvent security measures. While LLMs continuously enhance their security protocols, they remain vulnerable, particularly in multimodal contexts. This study introduces a novel method for bypassing LLM security policies by embedding malicious instructions within a mind map image. The attack leverages the intentional incompleteness of the mind map structure, specifically the absence of explanatory details. When the LLM processes the image and fills in the missing sections, it inadvertently generates unauthorized outputs, violating its intended security constraints. This approach applies to any LLM capable of extracting and interpreting text from images. Compared to the best-performing baseline method, which achieved an ASR of 30.5%, our method reaches an ASR of 90%, yielding an approximately threefold-higher attack success. Understanding this vulnerability is crucial for strengthening security policies in state-of-the-art LLMs. Full article

A road network is an important spatial carrier for the efficient and reliable operation of urban services and material flows. In recent years, the “high road density, small block size” trend has become a major focus in urban planning practices. However, whether high-density road networks are highly resilient lacks quantitative evidence. This study presents a multi-scale analytical framework for measuring road network resilience from a topological perspective. We abstract 186 ideal orthogonal grid density models from an actual urban road network, quantifying resilience under two disturbance scenarios: random failures and intentional attacks. The results indicate that road network density indeed has a significant impact on resilience, with both scenarios showing a trend where higher densities correlate with greater resilience. However, the increase in resilience value under the intentional attack scenario is significantly higher than that under the random failure scenario. The findings indicate that network density plays a decisive role in determining resilience levels when critical edges fail. This is attributed to the greater presence of loops in denser networks, which helps maintain connectivity even under intentional disruption. In the random failure scenario, network resilience depends on the combined effects of the node degree and density. This study offers quantitative insights into the design of resilient urban forms in the face of disruptive events, establishing reference benchmarks for road network spacing at both meso- and micro-scales. The results provide practical guidance for resilient city planning in both newly developed and existing urban areas, supporting informed decision-making in urban morphology and disaster risk management. Full article

Forests play a vital role in maintaining ecological balance, making accurate forest monitoring technologies essential. Remote sensing point cloud data always capture distinctive geometric features of forests, including the cylindrical symmetry of tree trunks and the radial symmetry of canopies. However, the inherent complexity of point cloud data, combined with their vulnerabilities to adversarial attacks, often disrupts these symmetrical patterns, significantly limiting the practical application of deep learning models in forest monitoring. This research presents a novel approach to enhance the robustness of individual tree segmentation networks by combining data augmentation and adversarial training techniques. Our method employs the FGSM algorithm and Gaussian noise attack to generate adversarial samples while utilizing data denoising and controlled noise injection for data augmentation. A dynamic adversarial training framework can adaptively adjust the proportion of adversarial samples during the network training stage to optimize the model. Using remote sensing point cloud datasets from Wisconsin, the experimental results demonstrate the effectiveness of the individual tree segmentation networks, PointNet++ and DBSCAN, in reducing attack success rates whilst improving the stability and accuracy of segmentation results under various adversarial conditions. This study highlights the potential for more robust forest monitoring systems capable of maintaining accuracy even when faced with data perturbations or intentional interference. Full article

Battlefields contain complex networks of electromagnetic (EM) systems, owned by adversary/allied military forces and civilians, communicating intentionally or unintentionally. Attacker’s strategies may include Intentional EM Interference (IEMI) to adversary target systems, although transmitted signals may additionally degrade/disrupt allied/civilian systems (called victims). To aid decision-making processes relating to IEMI attacks, Risk Assessment (RA) is performed to determine whether interference risks to allied/civilian systems are acceptable. Currently, there is no formalized Quantitative RA Method (QRAM) capable of calculating victim risk distributions, so a novel approach is proposed to address this knowledge gap, utilizing an Electromagnetic Warfare (EW) IEMI RA method modeling scenarios consisting of interacting EM systems within complex, dynamic, diverse, and uncertain environments, using Systems-of-Systems (SoS) theory. This paper aims to address this knowledge gap via critical analysis utilizing a case study which demonstrates the use of an Acknowledged SoS-based model as input to a QRAM capable of calculating victim risk distributions within EW IEMI RA-associated scenarios. Transmitter operators possess only uncertain/fuzzy knowledge of victim systems, so it is proposed that a Moot Acknowledged System-of-Fuzzy-Systems applies to EW IEMI RA scenarios. In summary, a novel SoS description feeding a novel QRAM (supported by a systematic literature review of RA mathematical modeling techniques)is proposed to address the knowledge gap. Full article

The tight integration of cyber and physical networks in integrated energy cyber–physical systems (IECPS) improves system awareness and coordinated control but also heightens susceptibility to targeted attacks. A robust IECPS defense system is crucial for increasing the system’s resilience against deliberate attacks. Reducing the associated risks is essential to ensure the safe and stable operation of IECPS. In order to enhance the defense capability of IECPS against deliberate attacks, this paper discusses cyberattacks, physical attacks, and coordinated cyber physical attacks (CCPAs) in detail. The attack principles and attack models of each type of attack are described, and then the intentional attack threats faced by IECPS are analyzed. Based on this, the paper reviews the current research landscape regarding countermeasures against deliberate attacks, categorizing the findings into three key areas: preemptive prevention, process response, and post–event recovery and summarizing. The theoretical foundations, system planning, optimal scheduling, and cyber security technologies required for existing defense research are further elaborated. The unresolved issues within these key technologies are analyzed and summarized, followed by the presentation of the problems and challenges faced in defending against deliberate IECPS attacks. Full article

As renewable energy sources (RES) continue to expand and the use of power inverters has surged, inverters have become crucial for converting direct current (DC) from RES into alternating current (AC) for the grid, and their security is vital for maintaining stable grid operations. This paper investigates the security vulnerabilities of photovoltaic (PV) inverters, specifically focusing on their internal sensors, which are critical for reliable power conversion. It is found that both current and voltage sensors are susceptible to intentional electromagnetic interference (IEMI) at frequencies of 1 GHz or higher, even with electromagnetic compatibility (EMC) protections in place. These vulnerabilities can lead to incorrect sensor readings, disrupting control algorithms. We propose an IEMI attack that results in three potential outcomes: Denial of Service (DoS), physical damage to the inverter, and power output reduction. These effects were demonstrated on six commercial single-phase and three-phase PV inverters, as well as in a real-world microgrid, by emitting IEMI signals from 100 to 150 cm away with up to 20 W of power. This study highlights the growing security risks of power electronics in RES, which represent an emerging target for cyber-physical attacks in future RES-dominated grids. Finally, to cope with such threats, three detection methods that are adaptable to diverse threat scenarios are proposed and their advantages and disadvantages are discussed. Full article

In the rapidly evolving landscape of telecommunications, the integration of commercial 5G solutions and the rise of edge computing have reshaped service delivery, emphasizing the customization of requirements through network slices. However, the heterogeneity of devices and technologies in 5G and beyond networks poses significant challenges, particularly in terms of security management. Addressing this complexity, our work adopts the Zero-touch network and Service Management (ZSM) reference architecture to enable end-to-end automation of security and service management in Beyond 5G networks. This paper introduces the ZSM-based framework, which harnesses software-defined networking, network function virtualization, end-to-end slicing, and orchestration paradigms to autonomously enforce and preserve security service level agreements (SSLAs) across multiple domains that make up a 5G network. The framework autonomously manages end-to-end security slices through intent-driven closed loops at various logical levels, ensuring compliance with ETSI end-to-end network slice management standards for 5G communication services. The paper elaborates with an SSLA-triggered use case comprising two phases: proactive, wherein the framework deploys and configures an end-to-end security slice tailored to the security service level agreement specifications, and reactive, where machine learning-trained security mechanisms autonomously detect and mitigate novel beyond 5G attacks exploiting open-sourced 5G core threat vectors. Finally, the results of the implementation and validation are presented, demonstrating the practical application of this research. Interestingly, these research results have been integrated into the ETSI ZSM Proof of Concept #6: ’Security SLA Assurance in 5G Network Slices’, highlighting the relevance and impact of the study in the real world. Full article

Objective: Exposure of emergency service personnel to disasters can lead to significant mental health challenges. The psychological impact of intentionally caused disasters, such as terrorist attacks, tends to be more severe than that of natural disasters. While much research has focused on terrorist attacks, little is known about the effects of intentional vehicular assaults (IVAs). This study examines the impact of an IVA on the mental health of firefighters. We hypothesized that firefighters deployed to the scene (deployed group (DG)) would experience more mental health problems compared to those not on duty (comparison group (CG)). Methods: The study included n = 115 firefighters, with 60 in the DG and 55 in the CG from the same units. Validated psychometric tools were used to assess anxiety, panic attacks (PHQ-D), and post-traumatic stress symptoms (PCL-5). Participation was voluntary, and informed consent was obtained. The study received approval from the Charité Berlin Ethics Committee (number: EA4/085/18). Results: A significantly higher prevalence of panic attacks was found in the DG (12.5%) compared to the CG (1.8%), with an odds ratio of 8.0 (95% CI: 1.0–67.3). Correlation analysis revealed a significant positive relationship between non-occupational tasks and hostility (r = 0.312, p = 0.015, n = 60), while parenthood had no significant effect on panic attacks or generalized anxiety. Conclusion: These results highlight the severe mental health impact of intentional disasters like IVAs on firefighters, emphasizing the need for targeted psychological support and interventions. Future research should focus on tailored interventions to address the high prevalence of panic attacks among this population. Full article

This article investigates the performance of the Galileo Open Service Navigation Message Authentication (OSNMA) system in real-life environments prone to RF interference (RFI), jamming, and/or spoofing attacks. Considering the existing data that indicate a relatively high number of RFI- and spoofing-related incidents reported in Eastern Europe, this study details a data-collection campaign along various roads through urban, suburban, and rural settings, mostly in three border counties in East and South-East of Romania, and presents the results based on the data analysis. The key performance indicators are determined from the perspective of an end user relying only on Galileo OSNMA authenticated signals. The Galileo OSNMA signals were captured using one of the few commercially available GNSS receivers that can perform this OSNMA authentication algorithm incorporating the satellite signals. This work includes a presentation of the receiver’s operation and of the authentication results obtained during test runs that experienced an unusually high number of RFI-related incidents, followed by a detailed analysis of instances when such RFI impaired or fully prevented obtaining an authenticated position, velocity, and time (PVT) solution. The results indicate that Galileo OSNMA demonstrates significant robustness against interference in real-life RF-degraded environments, dealing with both accidental and intentional interference. Full article