Target Of Attacks Research Articles

The Impact of Terrorism on Tourism Development in the Republic of Kosovo

In the last twenty years, tourism has faced many terrorist attacks or similar crises, so we can ask the question of what is the connection between terrorism and tourism, how does all this affect global tourism. The targets of terrorist attacks in a certain tourist destination can vary, starting from small objects to complex tourist infrastructure, and the basis of the attacks can be political, economic, religious, or other reasons. This research has special theoretical and practical importance for the development of tourism in Kosovo, but from the aspect of the impact of terrorism on tourism, with special reference to the Republic of Kosovo as a tourist destination. Research related to the impact of terrorism on tourism in the Republic of Kosovo will help us to understand, in essence, the negative effects that terrorism can have on the development of tourism in this country. From this research, we are expected to get to know the most important aspects of terrorism and tourism in modern conditions, but also with the legislation in this field and how it can help prevent terrorist acts in tourist facilities in Kosovo.

Leveraging Universal Adversarial Perturbation and Frequency Band Filters Against Face Recognition

Universal adversarial perturbation (UAP) exhibits universality as it is independent of specific images. Although previous investigations have shown that the classification of natural images is susceptible to universal adversarial attacks, the impact of UAP on face recognition has not been fully investigated. Thus, in this paper we assess the vulnerability of face recognition for UAP. We propose FaUAP-FBF, which exploits the frequency domain by learning high, middle, and low band filters as an additional dimension of refining facial UAP. The facial UAP and filters are alternately and repeatedly learned from a training set. Furthermore, we convert non-target attacks to target attacks by customizing a target example, which is an out-of-distribution sample for a training set. Accordingly, non-target and target attacks form a uniform target attack. Finally, the variance of cosine similarity is incorporated into the adversarial loss, thereby enhancing the attacking capability. Extensive experiments on LFW and CASIA-WebFace datasets show that FaUAP-FBF has a higher fooling rate and better objective stealthiness metrics across the evaluated network structures compared to existing universal adversarial attacks, which confirms the effectiveness of the proposed FaUAP-FBF. Our results also imply that UAP poses a real threat for face recognition systems and should be taken seriously when face recognition systems are being designed.

Mechanism of formation and significance of antimitochondrial autoantibodies in the pathogenesis of primary biliary cholangitis

Primary biliary cholangitis (PBC) is a chronic cholestatic progressive liver disease associated with cholangiopathies. The detection of antimitochondrial autoantibodies (AMAs) plays an important role in the diagnosis of classical PBC. AMAs are formed against the antigenic component associated with the dihydrolipoyl transacetylase of pyruvate dehydrogenase complex (E2 PDC) localized on the inner membrane of mitochondria. The loss of immune tolerance of E2 PDC in PBC is thought to be the cause of the mechanism of AMA formation and immune-mediated destruction of biliary epithelial cells (BECs) of the small- and medium-sized intrahepatic bile ducts. E2 PDC is not only present in BECs, but is also abundant in the mitochondria of all nucleated cells. The question remains as to why E2 PDC of only small BECs is the target of autoimmune attack. There is no evidence that AMAs have a deleterious effect on BECs. New scientific data has emerged that explains the damage to BECs in PBC by the defect of the biliary bicarbonate (HCO3–) “umbrella” that protects BECs from the detergent action of bile acids under physiological conditions. Disruption of HCO3– production by BECs in PBC leads to changes in the pH of hepatic bile, accompanied by accumulation of bile acids in the small BECs. The detergent action of bile acids leads to damage of membrane structures of BECs and their apoptosis, development of ductulopenia, and intrahepatic cholestasis. For the first time, it has been suggested that under the influence of bile acids, the E2 PDC antigen may undergo conformational changes that alter its immunological properties. E2 PDC becomes a neoantigen that is recognized by the normal (“healthy”) immune system as a foreign antigen, leading to the production of AMAs. For the first time, the authors of this review provide an explanation for why only small BECs are damaged in PBC.

Emergency evacuation risk assessment for toxic gas attacks in airport terminals: Model, algorithm, and application

Transportation infrastructure has often been the target of terrorist attacks, and mitigation of the risk of toxic gas attacks is a challenging task in the design of indoor emergency evacuation systems. Considering multiple emergency response modes, we propose an agent-based risk assessment model and its algorithm to integrate gas diffusion and pedestrian movement data for emergency response, quickly assessing average individual exposure risk. We assessed the exposure status of individuals with respect to their emergency response actions following a toxic gas attack in an airport terminal. The results indicate that in the event of a general gas attack on an airport terminal, ventilation must be immediately ceased along with early evacuation. In areas with a shelter-in-place environment, the ventilation mode and shelter-in-place time should be determined based on the concentration of indoor and outdoor gases. In areas with nerve gas exposure and high population density, a new exit must be established at evacuation bottlenecks, and pedestrians must be guided to evacuate while promptly closing ventilation. These results offer suggestions and strategies for emergency response and decision-making in airport terminals during such incidents.

Automated Repair of Smart Contract Vulnerabilities: A Systematic Literature Review

The substantial value held by smart contracts (SCs) makes them an enticing target for malicious attacks. The process of fixing vulnerabilities in SCs is intricate, primarily due to the immutability of blockchain technology. This research paper introduces a systematic literature review (SLR) that evaluates rectification systems designed to patch vulnerabilities in SCs. Following the guidelines set forth by the PRISMA statement, this SLR meticulously reviews a total of 31 papers. In this context, we classify recently published SC automated repair frameworks based on their methodologies for automatic program repair (APR), rewriting strategies, and tools for vulnerability detection. We argue that automated patching enhances the reliability and adoption of SCs, thereby allowing developers to promptly address identified vulnerabilities. Furthermore, existing automated repair tools are capable of addressing only a restricted range of vulnerabilities, and in some cases, patches may not be effective in preventing the targeted vulnerabilities. Another key point that should be taken into account is the simplicity of the patch and the gas consumption of the modified program. Alternatively, large language models (LLMs) have opened new avenues for automatic patch generation, and their performance can be improved by innovative methodologies.

Ethnocentric Universalism: Its Nature, Epistemic Harm, and Emancipatory Prospects

ABSTRACT This paper does three interrelated things. First, it argues that the universalism that forms the target of criticism and attack by decolonial theorists from the Global South is a debased form of universalism, what might be termed ‘ethnocentric universalism’. Second, equipped with a conceptual grip on ethnocentric universalism, it shows that the picture on which ethnocentric universalism confers some innocuous epistemic privilege to members of dominant groups is not quite accurate – ethnocentric universalism is incompatible with the epistemic flourishing of members of dominant groups. And third, based on that claim, and the additional consideration that ethnocentric universalism equally undermines the epistemic flourishing of members of historically marginalized groups, it proposes an emancipatory framework for engaging with it.

Cluster partition-fuzzy broad learning-based fast detection and localization framework for false data injection attack in smart distribution networks

The distributed renewable energy generations, as accessible and easily targets for attackers, introduce an extra false data injection attack (FDIA) threat in the smart distribution networks. Scattered attack points and complex attack features hinder the elimination of potential threats. In this context, an FDIA fast detection and pinpoint localization framework is proposed. This framework identifies abnormal signals and attacked nodes from the unique topology structure and status contiguity of smart distribution networks, namely, spatial-temporal correlations of power grids, by using a cluster partition-fuzzy broad learning system (CP-FBLS). Unlike most existing FDIA detection methods, which are dedicated to high accuracy but neglect the urgent need for rapid detection in smart distribution networks, the proposed CP-FBLS framework maintains the fast computational nature of a fuzzy broad learning system (FBLS), while avoiding the accuracy degradation caused by high-dimension of data in large-scale smart distribution networks. Moreover, the multi-layer structure of the proposed framework recognizes the location of FDIA, bridging the research gap of attack localization. To comprehensively evaluate the proposed strategy, datasets containing various FDIA types are constructed. Numerical simulations based on the above datasets in modified IEEE 34-bus and 123-bus distribution systems are implemented. The results of the case studies showed that the proposed method can achieve 98.43 % accuracy with 0.34 ms detection time, realizing rapid detection and localization of various FDIAs with satisfactory accuracy.

Data Poisoning Attack against Neural Network-Based On-Device Learning Anomaly Detector by Physical Attacks on Sensors.

In this paper, we introduce a security approach for on-device learning Edge AIs designed to detect abnormal conditions in factory machines. Since Edge AIs are easily accessible by an attacker physically, there are security risks due to physical attacks. In particular, there is a concern that the attacker may tamper with the training data of the on-device learning Edge AIs to degrade the task accuracy. Few risk assessments have been reported. It is important to understand these security risks before considering countermeasures. In this paper, we demonstrate a data poisoning attack against an on-device learning Edge AI. Our attack target is an on-device learning anomaly detection system. The system adopts MEMS accelerometers to measure the vibration of factory machines and detect anomalies. The anomaly detector also adopts a concept drift detection algorithm and multiple models to accommodate multiple normal patterns. For the attack, we used a method in which measurements are tampered with by exposing the MEMS accelerometer to acoustic waves of a specific frequency. The acceleration data falsified by this method were trained on an anomaly detector, and the result was that the abnormal state could not be detected.

Targeting attack activity-driven networks.

Real-world complex systems demonstrated temporal features, i.e., the network topology varies with time and should be described as temporal networks since the traditional static networks cannot accurately characterize. To describe the deliberate attack events in the temporal networks, we propose an activity-based targeted attack on the activity-driven network to investigate temporal networks' temporal percolation properties and resilience. Based on the node activity and network mapping framework, the giant component and temporal percolation threshold are solved according to percolation theory and generating function. The theoretical results coincide with the simulation results near the thresholds. We find that targeted attacks can affect the temporal network, while random attacks cannot. As the probability of a highly active node being deleted increases, the temporal percolation threshold increases, and the giant component increases, thus enhancing robustness. When the network's activity distribution is extremely heterogeneous, network robustness decreases consequently. These findings help us to analyze and understand real-world temporal networks.

Identification of Security Scenarios in offshore Oil&Gas production facilities based on past incident analysis

Offshore Oil&Gas installations may be attractive targets of intentional attacks due to their presence in socio-political sensitive areas, their economic significance, and the high impact of scenarios that can be triggered by the mismanagement of the hazardous substances handled (e.g., crude oil, natural gas, etc.). In the present study, a new dataset including 112 past security-related incidents that affected the offshore Oil&Gas sector in the last decades was developed and analyzed using Exploratory Data Analysis (EDA), Root Cause Analysis (RCA), and Attack Tree Analysis (ATA). Significant differences were found in the types of threats and attack modes, with terrorism identified as the primary threat, followed by robbery and kidnapping. The identified scenarios caused fatalities, injuries, and asset damages at the targeted facilities, underscoring their potential to cause severe impacts, comparable to the outcomes of safety-related accidents. Tables linking identified attack modes, scenarios, and consequences specific contribution to the SVA workflow proposed by the API Recommended Practices 70 and 70I were obtained. Overall, the results obtained support the application of suitable Security Vulnerability/Risk Assessment (SVA/SRA) methodologies concerning the identification and characterization of adversaries, the definition of attack modes, the evaluation of effectiveness of existing security barriers, and the assessment of consequences.

A context-aware zero trust-based hybrid approach to IoT-based self-driving vehicles security

With the speedy progression and adoption of IoT devices in modern self-driving vehicles (SDVs), autonomous driving industry is gradually reforming its capabilities to provide better transportation services. However, this domain faces enormous security and privacy challenges and thus has become an attractive target for attackers due to its rapid growth and market worth. Furthermore, the rapid transformation in technological tools in transport industry and speedy evolution of cyber-attacks paved the way for designing efficient IDSs. Motivated by these challenges, we put forward a new secure and efficient IDS approach for the security of SDVs. The propose approach utilizes an emerging strategy to mitigate security vulnerabilities and cyber attacks detection using zero trust (ZT) model. Through this work, we put forward a context-aware zero trust security framework for IoT-based SDVs. The proposed framework utilizes a context-aware design to evaluate the trustworthiness of the devices using multi-source trust and reputation model. Then, to make the framework more effective and reliable, we introduce crawler system into the context of the IoT-devices in SDVs to make the system unbiased. Additionally, an observer module is developed that employs state-of-the-art machine learning algorithm to detect malicious actions. Empirical results on two standard benchmark datasets (i.e., Car Hacking and ToN_IoT) validate the practicality and robustness of propose framework in real-world transport systems with enhanced security and trust management against evolving cyber-threats. Detection results demonstrate that the proposed framework secured the best performance by achieving 99.43% and 99.52% accuracy for Car Hacking and ToN_IoT, respectively. The findings of this study will help the security professionals and researchers to comprehend the importance of ZT architecture in developing effective and robust security solutions for modern IoT-based SDVs.

Dual Attention Adversarial Attacks With Limited Perturbations.

The construction of undetectable adversarial examples with few perturbances remains a difficult problem in adversarial attacks. At present, most solutions use the standard gradient optimization algorithm to build adversarial examples by applying global perturbations to benign samples and then launch attacks on the targets (e.g., face recognition systems). However, when the perturbance size is limited, the performance of these approaches suffers substantially. The content of crucial places in an image, on the other hand, will impact the final prediction; if these areas can be investigated and limited perturbances introduced, an acceptable adversarial example will be constructed. Based on the foregoing research, this article offers a dual attention adversarial network (DAAN) to produce adversarial examples with limited perturbations. DAAN initially searches for effective areas in an input image using the spatial attention network and channel attention network, and then creates space and channel weights. Following that, these weights direct an encoder and a decoder to generate effective perturbation, which is then combined with the input to produce an adversarial example. Finally, the discriminator determines if the created adversarial examples are true or false, and the attacked model is utilized to determine whether the generated samples fit the attack targets. Extensive studies on various datasets show that DAAN not only delivers the best attack performance across all comparison algorithms with few perturbations, but it can also significantly improve the defensiveness of the attacked models.

DApps ecosystems: mapping the network structure of smart contract interactions

Decentralized applications (DApps) built on blockchain platforms such as Ethereum and coded in languages such as Solidity, have recently gained attention for their potential to disrupt traditional centralized systems. Despite their rapid adoption, limited research has been conducted to understand the underlying code structure of these applications. In particular, each DApp is composed of multiple smart contracts, each containing a number of functions that can be called to trigger a specific event, e.g., a token transfer. In this paper, we reconstruct and analyse the network of contracts and functions calls within the DApp, which is helpful to unveil vulnerabilities that can be exploited by malicious attackers. We show how decentralization is architecturally implemented, identifying common development patterns and anomalies that could influence the system’s robustness and efficiency. We find a consistent network structure characterized by modular, self-sufficient contracts and a complex web of function interactions, indicating common coding practices across the blockchain community. Critically, a small number of key functions within each DApp play a central role in maintaining network connectivity, making them potential targets for cyber attacks and highlighting the need for robust security measures.

Deception mechanisms of FDA‒AWACS against passive monopulse angle measurements

AbstractAirborne warning and control systems (AWACS) serve as critical command and control centres in air combat operations, making them prime targets for strategic attacks. These enemy attacks typically rely on the accurate determination of AWACS combat positions using different direction‐finding devices. In particular, passive monopulse angle measurement systems locate AWACS by measuring the angles of signals emitted by AWACS radiation sources, thus rendering them vulnerable to attacks. Given the criticality of the airborne radars of AWACS in battle command and control operations, they must function continuously to monitor air and sea targets. Hence, AWACS cannot effectively evade electronic reconnaissance systems through tactics such as radar shutdown. To explore alternative measures, the authors investigate the deception mechanisms of an integrated frequency diverse array AWACS (FDA‒AWACS) against passive monopulse angle measurements. Using an established FDA signal model, the principles underlying two common monopulse angle measurement methods are first outlined. Subsequently, the angle estimation formulae typically used by these monopulse angle measurement systems to interpret received FDA radiation signals are derived. Additionally, the transmit beampatterns, amplitude patterns, and angle measurement deception capabilities of several typical FDAs are examined. Simulation results indicate that the FDA‒AWACS can theoretically deceive passive monopulse angle measurement systems to a certain extent. However, one‐dimensional uniform linear FDAs and other arrays using sinusoidal frequency offsets exhibit limited deception abilities. In contrast, arrays utilising cubic and quartic frequency offset achieve angle measurement errors exceeding 2° in far‐field scenarios.

Botanical Bioflavonoid Composition from Scutellaria baicalensis- and Acacia catechu-Protected Mice against D-Galactose-Induced Immunosenescence, and Cyclophosphamide Induced Immune Suppression.

Oxidative stress and chronic inflammation create a perpetual cycle in the elderly, where impaired immune function amplifies susceptibility to oxidative damage, and oxidative stress further weakens the immune response. This cycle is particularly detrimental to the respiratory system of the elderly, which is an easy target for constant exogenous harmful attacks during cold/flu season or under heavy air pollution. Herbal medicines that protect respiratory function are seen as safer alternatives to conventional therapies; however, there is limited availability of scientifically validated, safe, and effective natural supplements for these conditions. In this study, we evaluated a standardized bioflavonoid composition, UP446, that contains bioactives from the roots of Scutellaria baicalensis and the heartwoods of Acacia catechu as a natural and nutritional supplement for its antioxidative and immunoregulatory effects in oxidative stress-accelerated aging and chemically induced immune suppression mouse models. Immunosenescence was induced through the repeated subcutaneous inoculation of D-galactose (D-Gal) at a dose of 500 mg/kg/day in CD-1 mice. UP446 was administered orally at doses of 100 mg/kg and 200 mg/kg starting in the fifth week of immunosenescence induction. This study lasted a total of ten weeks. All mice received a quadrivalent influenza vaccine 2 weeks before termination. Whole blood, serum, spleen homogenate, and thymus tissues were processed for analysis. Cyclophosphamide (Cy)-induced immunosuppression was triggered by three consecutive injections of cyclophosphamide at 80 mg/kg/day, followed by the oral administration of UP446 for 18 days at doses of 100 mg/kg and 200 mg/kg. Blood was collected from each animal at necropsy, and serum was isolated for IgA and IgG ELISA analysis. UP446 was found to improve immune response, as evidenced by the stimulation of innate (NK cells) and adaptive immune responses (T cells and cytotoxic T cells), an increase in antioxidant capacity (glutathione peroxidase), the preservation of vital immune organs (the thymus), and a reduction in NFκB. UP446 also increased serum levels of IgA and IgG. The findings presented in this report demonstrate the antioxidative, anti-inflammatory, and immune-regulatory activities of UP446, suggesting its potential use in respiratory conditions involving immune stress due to aging, oxidative stress, and/or pathogenic challenges.

Trust-Based Detection and Mitigation of Cyber Attacks in Distributed Cooperative Control of Islanded AC Microgrids

In this study, we address the challenge of detecting and mitigating cyber attacks in the distributed cooperative control of islanded AC microgrids, with a particular focus on detecting False Data Injection Attacks (FDIAs), a significant threat to the Smart Grid (SG). The SG integrates traditional power systems with communication networks, creating a complex system with numerous vulnerable links, making it a prime target for cyber attacks. These attacks can lead to the disclosure of private data, control network failures, and even blackouts. Unlike machine learning-based approaches that require extensive datasets and mathematical models dependent on accurate system modeling, our method is free from such dependencies. To enhance the microgrid’s resilience against these threats, we propose a resilient control algorithm by introducing a novel trustworthiness parameter into the traditional cooperative control algorithm. Our method evaluates the trustworthiness of distributed energy resources (DERs) based on their voltage measurements and exchanged information, using Kullback-Leibler (KL) divergence to dynamically adjust control actions. We validated our approach through simulations on both the IEEE-34 bus feeder system with eight DERs and a larger microgrid with twenty-two DERs. The results demonstrated a detection accuracy of around 100%, with millisecond range mitigation time, ensuring rapid system recovery. Additionally, our method improved system stability by up to almost 100% under attack scenarios, showcasing its effectiveness in promptly detecting attacks and maintaining system resilience. These findings highlight the potential of our approach to enhance the security and stability of microgrid systems in the face of cyber threats.

Comprehensive Review of Security Problems in Mobile Robotic Assistant Systems: Issues, Solutions, and Challenges

Nowadays, robots in the modern world are playing an important and increasingly popular role. MRA (Mobile Robotic Assistant) is a type of mobile robot designed to support humans in many different fields, helping to improve efficiency and safety in daily activities, work, or medical treatment. The number of MRAs is increasing and diverse in function, in addition to the ability to collect and process data, MRAs also have the ability to physically interact with users. Therefore, security is one of the important issues to improve the safety and effective operation of MRA. In this paper, through a comprehensive literature review and detailed analysis of the prominent MRA security attacks in recent years (based on criteria such as: attack targets, technologies used, impact level, feasibility, and contribution to addressing overall MRA security issues), a systematic classification by MRA activity fields is conducted. Security attacks, threats, and vulnerabilities are examined from various perspectives, such as hardware attacks or network/system-level attacks, operating systems/application software. Additionally, corresponding security solutions are proposed, compared, and evaluated to enhance MRA security. The paper also addresses challenges and suggests open research directions for the future.

Perceived identity threat and brand advocacy responses to different types of brand-related attacks

PurposeUsers frequently target rival brands through direct criticism or indirect customer insults, yet the impact of such attacks on brand advocacy remains unexplored. The purpose of this study is to classify online attacks into brand-targeted attacks and consumer-targeted attacks and further investigate their differential impacts on brand advocacy and the underlying mechanism and a boundary condition of those impacts.Design/methodology/approachThree experimental studies using different types of brands (electronics, universities and sports footwear) are conducted to examine the effects of brand-related attack targets on brand advocacy.FindingsThis research shows that consumer-targeted attacks trigger higher brand advocacy through increasing perceived identity threat than brand-targeted attacks. Moreover, the effect of consumer-targeted attacks (versus brand-targeted attacks) on brand advocacy is mediated by perceived identity threat and mitigated when consumers’ identification with the attacked brand is strong.Practical implicationsThe study’s findings yield practical applications for marketers and brand managers, assisting them in understanding consumers’ reactions to brand attacks. This study serves as a reference for firms to consider leveraging the association between brand identification and brand-related attack targets and uniting with loyal brand fans to manage online brand conflicts.Originality/valueThe present study extends prior literature on customer-brand relationships in the context of online attacks. Through investigating the impacts of brand-targeted and consumer-targeted attacks on brand advocacy, this research offers theoretical insights into consumers’ responses to online attacks with different targets.

An efficient intrusion detection system for IoT security using CNN decision forest.

The adoption and integration of the Internet of Things (IoT) have become essential for the advancement of many industries, unlocking purposeful connections between objects. However, the surge in IoT adoption and integration has also made it a prime target for malicious attacks. Consequently, ensuring the security of IoT systems and ecosystems has emerged as a crucial research area. Notably, advancements in addressing these security threats include the implementation of intrusion detection systems (IDS), garnering considerable attention within the research community. In this study, and in aim to enhance network anomaly detection, we present a novel intrusion detection approach: the Deep Neural Decision Forest-based IDS (DNDF-IDS). The DNDF-IDS incorporates an improved decision forest model coupled with neural networks to achieve heightened accuracy (ACC). Employing four distinct feature selection methods separately, namely principal component analysis (PCA), LASSO regression (LR), SelectKBest, and Random Forest Feature Importance (RFFI), our objective is to streamline training and prediction processes, enhance overall performance, and identify the most correlated features. Evaluation of our model on three diverse datasets (NSL-KDD, CICIDS2017, and UNSW-NB15) reveals impressive ACC values ranging from 94.09% to 98.84%, depending on the dataset and the feature selection method. Notably, our model achieves a remarkable prediction time of 0.1 ms per record. Comparative analyses with other recent random forest and Convolutional Neural Networks (CNN) based models indicate that our DNDF-IDS performs similarly or even outperforms them in certain instances, particularly when utilizing the top 10 features. One key advantage of our novel model lies in its ability to make accurate predictions with only a few features, showcasing an efficient utilization of computational resources.

Analysis of the ballistic impact on sandwich panel: influence of attack angle and target location in structure–bullet interaction

Analysis of the ballistic impact on sandwich panel: influence of attack angle and target location in structure–bullet interaction