The rapid proliferation of Internet of Things (IoT) devices and distributed computing platforms has accelerated the adoption of the edge–cloud continuum, an architectural paradigm that integrates edge devices, fog nodes, and centralized cloud infrastructures to support real-time data processing and latency-sensitive applications. While this architecture enhances scalability, responsiveness, and intelligent service delivery, it simultaneously expands the cyber-attack surface due to the presence of heterogeneous, resource-constrained, and geographically distributed devices. Traditional perimeter-based security mechanisms are increasingly inadequate for protecting such dynamic environments, while many existing Zero Trust Architecture (ZTA) implementations rely on static access control policies and centralized decision mechanisms that limit scalability and real-time responsiveness. This study proposes a Machine Learning-Driven Self-Healing Zero Trust Architecture (SH-ZTA) designed to enable autonomous cyber resilience across the edge–cloud continuum. The framework integrates Graph Neural Networks (GNNs) for relational anomaly detection and Deep Reinforcement Learning (DRL) for adaptive security policy orchestration. Network telemetry data collected from IoT devices and edge gateways are represented as communication graphs, enabling the detection of abnormal interactions, compromised nodes, and potential lateral movement attacks. The reinforcement learning agent dynamically enforces micro-segmentation policies, isolates malicious entities, and reconfigures network pathways to maintain operational continuity without human intervention. Experimental evaluation conducted in a simulated edge computing environment demonstrates that the proposed SH-ZTA framework significantly improves threat mitigation efficiency while maintaining low computational overhead suitable for resource-constrained devices. The results show improved detection accuracy, faster response latency, and enhanced network resilience compared to conventional security approaches.

The rapid evolution of cyber threats poses significant challenges for security analysts, who must continuously process large volumes of unstructured information from threat reports, security blogs, vulnerability databases, and dark-web discussions. Manual analysis of these sources is time-consuming, inconsistent, and unable to keep pace with the increasing frequency and sophistication of emerging attacks. This work presents an automated framework for emerging cyber-threat identification and profiling using Natural Language Processing (NLP). The proposed system collects real-time textual data from multiple cybersecurity intelligence sources and applies advanced NLP techniques—such as entity extraction, topic modeling, semantic similarity, and threat classification—to detect newly emerging vulnerabilities, exploits, malware families, and attack trends. Using machine-learning–based clustering and profiling mechanisms, the system generates structured threat intelligence reports that summarize threat attributes, severity levels, affected platforms, and potential attack vectors. The automated pipeline reduces analyst workload, minimizes detection delays, and provides an adaptive, scalable solution for enterprise threat intelligence. Experimental results show that the NLP-based approach significantly enhances the accuracy and speed of early threat discovery when compared to traditional manual analysis.

Synthetic data generation is increasingly proposed as an alternative to classical anonymization for sharing health data. We compared concrete applications of both approaches on a small, high-dimensional health claims dataset, assessing their impact on fidelity, reproducibility of study outcomes, and privacy risks. To reflect different sharing contexts, we considered a context-independent, higher-risk scenario with no assumptions about potential attacks, and a context-dependent, lower-risk scenario informed by threat modeling. Analyses on anonymized and synthetic data yielded results similar to those from the original study data, but came at the cost of higher uncertainty when estimating hazard ratios. As expected, higher data utility and fidelity were related to higher privacy risks. Our findings provide a reusable workflow and comparative insights into anonymization and synthetization and show that both methods are valuable means to lower privacy risks in data sharing scenarios but verifying results on the original data should be done whenever possible.

In a Cyber-Physical System (CPS), multiple embedded subsystems interact with the external environment while operating semi-independently, creating complex contextual relationships, exposure to adversarial conditions, and inherent uncertainty. Ensuring secure communication across CPS infrastructure requires the application of fundamental security principles, supported by a combination of approaches such as social engineering awareness, implementation of security standards, vendor-specific controls, and effective network management. Trust emerges as a critical factor in maintaining the security and reliability of CPS communications. This paper reviews key usability challenges and associated risks in CPS environments, analyzes potential attack vectors across different system layers, and discusses strategies for effective trust management to enhance overall system resilience.

This article investigates the problem of secure state estimation for multitarget tracking systems based on Kalman consensus filtering. In the existing distributed Kalman filters, the filter gain and consensus structure rely on the independence of tracked targets, which cannot maintain the estimation performance when encountering coupled measurements across multiple targets. Moreover, the existing researches mainly focus on the security in single-channel systems, whereas such efforts fail to consider potential attacks in multichannel scenarios. In this case, by establishing a target-dependent augmented system and a link-unreliable composite directed graph, the coupling features and multichannel attacks are depicted. Then, a modified Kalman consensus filter is proposed by specifically designing consensus structure and gain terms to account for the impacts of coupled measurements and attacks. Furthermore, by scaling the Lyapunov function through the Riccati difference equation and matrix inequalities, sufficient conditions are established to ensure the boundedness of estimation errors. Numerical simulations are conducted to demonstrate the effectiveness of the filter.

Purpose – Tourists are in a dilemma when it comes to visiting conflict zones: How much will they risk to go to a place with the promise of the experience of a lifetime? As the study area, the study will use Kashmir region of India to examine how tourists manoeuvre through the risk perceptions and make decisions to visit the region despite various possible risks. In this study, the authors explored the association between the destination attractiveness and the behaviour intentions and the perceived risk of terror among the tourist taking into consideration the mediating effects of the perceived risk and the risk prevention effort. In addition, it also examines the moderating role of knowledge of risk-preventive strategies in these relationships. Methodology/Design/Approach – The research will utilize a quantitative research approach, using Smart PLS-SEM, in order to realize the objectives of the research. Findings -The findings show that although destination attractiveness has a positive effect on future travel plans, the greater the perceived risk the lower the effect. There is a decrease in the level of confidence of the tourists in their precautionary actions in case of potential terrorist attacks. The conclusions indicate that there is a need to build a good relationship between locals and the tourists by promoting the community-based tourism projects to aid trust and toughness in the conflicted zones. Findings – This research study suggests that tourism authorities can improve visitation and repeat travel through investing in community-based tourism projects. The destinations should incorporate the transparent safety measures and enhanced destination services that will create value and satisfaction to the travellers. These strategies reduce risk perceptions of tourists, as well as enhance resilience and global image of destinations affected by conflicts. Originality of the research – The research provides important information to tourism authorities and service providers in general because it demonstrates how investing in safety measures, community-based tourism, and destination services are able to lower perceptions of risks and encourage repeat customers to visit, in spite of the fact that the study is limited only to Kashmir.

Modulated oxidation pathways enabled by CoFe bimetallic alloy catalysts for effective elimination of antibiotics.

For vehicle steer-by-wire (SbW) systems, intelligent connected vehicle technology has improved their performance while making them vulnerable to denial-of-service (DoS) attacks. Since the sampling and control signals share the same CAN bus, both sensor-to-controller and controller-to-actuator channels are prone to simultaneous signal interruptions under DoS attacks, significantly reducing the accuracy of steering angle tracking. This article innovatively proposes a secure dual-channel joint defense strategy that enhances tracking accuracy under DoS attacks. For secure observation, the estimation error system is established to consider uncertainties caused by external disturbances and measurement noise. By introducing constraints that characterize the convergence speed and bounds of estimation errors, it ensures high accuracy of state observation in uncertain SbW systems under DoS attacks. For robust control, the designed tube-based model predictive control introduces an auxiliary control input to constrain future states that may be affected by system disturbances caused by potential DoS attacks, to a robust positive invariant set, suppressing the jumping phenomenon in switching control caused by uncertainty, leading to prediction performance degradation and achieving smooth actuator outputs. Hardware-in-the-loop experiment results demonstrate that the proposed strategy ensures stability of the SbW system under DoS attacks and effectively improves the transient response performance and tracking accuracy for the reference steering angle.

A three-tier microsegmentation framework for enterprise networks under Zero Trust Architecture

ABSTRACT The smart water distribution system is an essential and widely encompassing component of modern smart cities. While connecting to networks to improve infrastructure efficiency, it also exposes itself to the cyber environment, making it vulnerable to various cyber‐physical attacks, such as malicious manipulation of sensor data and unauthorized interference with communication between system components. These attacks could result in severe consequences. Moreover, attackers may employ network attack techniques, such as replay attacks, during the process of cyber‐physical attacks to disguise their malicious actions. This not only increases the harm caused by the attacks but also raises the difficulty of detecting such attacks. Numerous recent studies have tackled this issue using model‐based or data‐driven approaches to analyze attack characteristics. However, model‐based methods often rely on complex hydraulic models, while data‐driven approaches, although promising, still leave room for improvement in terms of detection accuracy and generalization ability. Additionally, both types of methods lack the capability to localize the affected components within the system, which is crucial for prompt response and mitigation of cyber‐physical attacks. This paper proposes a data‐driven time series analytical deep learning framework for detecting cyber‐physical attacks on water distribution systems, building a deep learning model based on an encoder‐decoder architecture and LSTM networks, and designing a reconstruction error calculation method specifically for time‐series data, which measures the discrepancy between the original and reconstructed sequences to detect potential attacks and localize the affected components. The framework was developed and tested on an open‐source dataset. Our method performed well in terms of accuracy, precision, recall, F1 score, and detection latency, thereby successfully detecting all attacks and localizing the affected components of the water distribution system while demonstrating stronger generalization capabilities and potential for further development.

Securing data storage and sharing has become a primary concern in today’s application domains, prompting the exploration of Attribute-Based Encryption (ABE) protocols as a practical solution. ABE allows data owners to build access structures and share data with users based on specific attributes. However, existing ABE protocols typically require pairing operations, which leads to significant computational overhead. In addition, the inherent dependence of ABE systems on key generation centers raises security concerns. To address these challenges, we propose a novel data sharing protocol. Our protocol empowers data owners to retain control over data access, thereby reducing the risks associated with compromised key generation centers. This is achieved by fusing Shamir’s secret sharing and attribute-based encryption schemes. In addition, we reduce the computational burden on the data owner by offloading part of the encryption overhead to the cloud. Furthermore, we confirm the robustness of our protocol against potential cryptographic attacks through formal proofs. These properties highlight the versatility of our solution to securely store and share data in various real-world scenarios.

Gun violence in U.S. schools continues to be a persistent concern. A promising line of scholarship focuses on potential school gun attacks that were stopped before they could occur, but this work is limited to a small number of studies. This study investigates how potential school gun attacks were exposed by analyzing 124 publicly reported cases from 2018 to 2023. For the analysis, we generated descriptive data on the school contexts, individuals, and processes associated with exposing potential school gun attacks both on and off school grounds. Findings indicated that in most cases, suspects communicated their intentions, which created opportunities for exposing potential attacks. Students were the most common source for exposure, reporting 42% of cases, while teachers, parents, and community members played smaller but important roles. To illustrate the interacting factors behind exposing a potential attack, we further describe four recurring scenarios: public signaling of intent, private disclosures to confidants, discovery of written plans or private communications, and detection through safety measures. These pathways to exposing a threat suggest that positive relationships in schools, open lines of communication, and high expectations for reporting serious threats may be central to averting school gun attacks.

The rapid advancement of underwater imaging technology has generated massive amounts of image data. How to securely store and reliably transmit such data under stringent resource constraints have become a key challenge in the field of marine information. Existing underwater image encryption schemes primarily rely on digital encryption, which typically produces noise-like encrypted images that are prone to attracting attention from potential attackers during transmission and storage. Moreover, current underwater image encryption schemes often lack systematic validation of robustness and reliability in practical communication scenarios. To address these issues, this paper proposes an optical encryption and lossless carrier hiding of underwater images based on Fourier holography and a 5D chaotic system. The proposed scheme employs a five-dimensional chaotic system to generate encryption keys, offering a large key space of up to 10210, enabling single-channel optical encryption of color underwater images and thereby reducing optical hardware requirements. Furthermore, the encrypted optical images achieve lossless carrier hiding, effectively mitigating the issue of drawing attention from potential attackers during transmission and storage. Finally, the robustness and reliability are validated through communication simulations using orthogonal frequency-division multiplexing (OFDM) and generalized frequency-division multiplexing (GFDM), addressing the lack of systematic validation in existing underwater image encryption schemes. Experimental results demonstrate that the proposed scheme not only inherits the advantages of high initial-value sensitivity from chaotic systems and the physical security inherent to Fourier holographic optical encryption but also achieves lossless carrier-based concealment of optical images. This offers a novel solution, to our knowledge, for underwater image transmission characterized by high security, strong robustness, and enhanced stealth.

The paper proposes a formal model for the adaptive selection of cryptographic parameters for protecting communication channels in corporate computer networks based on dynamic trust assessment and integrated risk. The relevance of the study stems from the fact that common practices of static configuration of encryption algorithms, modes of operation, and cryptographic strength parameters do not account for changes in access context and the behavior of interacting entities, which leads either to excessive computational overhead or to the emergence of vulnerability windows during threat escalation. The scientific novelty lies in interpreting the cryptographic profile as a controllable dynamic state of the security system, where trust acts as a direct control parameter of the cryptographic configuration rather than merely a factor in access decision-making. A protected channel is formalized as a state tuple combining the subject, resource, context, trust level, risk, and cryptographic profile, while adaptive parameter selection is described by a mapping that establishes a correspondence between (resource criticality, context) and a set of cryptographic characteristics (algorithm, mode, strength parameter, session lifetime). An optimization formulation for profile selection is developed that accounts for the trade-off between cryptographic strength and operational costs, along with an event-driven mechanism for updating the cryptographic state (Rekey/Upgrade/Revoke) in response to trust degradation, risk increase, or critical security events. Scenario analysis (normal operation, contextual/behavioral anomaly, critical event) demonstrates the model’s ability to coherently enhance strength and reduce cryptographic session lifetimes in high-risk situations, thereby reducing the potential attack window while maintaining acceptable performance under low-risk conditions. The obtained results provide a theoretical foundation for deploying adaptive cryptographic profiles in TLS/VPN and Zero Trust–oriented corporate environments.

Sliced inverse regression (SIR), which includes linear discriminant analysis (LDA) as a special case, is a popular and powerful dimension reduction tool. In this article, we extend SIR to address the challenges of decentralized data, prioritizing privacy and communication efficiency. Our approach, termed as federated sliced inverse regression (FSIR), facilitates distributed computing of the sufficient dimension reduction subspace among multiple clients, solely sharing local estimates to protect sensitive datasets from exposure. To guard against potential adversary attacks, FSIR employs diverse perturbation strategies, including a novel vectorized Gaussian mechanism that guarantees ( ε , δ ) -differential privacy at a low cost of statistical accuracy. Additionally, FSIR achieves a tight composition of various privacy mechanisms by adopting a hypothesis testing perspective on differential privacy. It also incorporates a collaborative feature screening procedure, enabling effective handling of high-dimensional client data with varying feature sets. Theoretical properties of FSIR are established for both low-dimensional and high-dimensional settings, supported by extensive numerical experiments and real data analysis. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Federated learning aims to protect user data privacy by training locally and exchanging only model updates. However, the gradient of the exchange itself can reveal information that allows malicious actors to launch member-inference attacks to infer whether a particular data sample is present in a client’s training set, posing a serious privacy threat. In this paper, a complete detection and quantification scheme is proposed for the member inference attack in federated learning gradient exchange. First, we design an attack detection framework based on gradient feature analysis, which uses ensemble learning model to identify potential malicious attacks by extracting and aggregating high-dimensional statistical features and distribution anomalies of client-uploaded gradients. Secondly, in order to evaluate the severity of privacy leakage when the attack succeeds, we propose a privacy leakage quantification method. By measuring the sensitivity difference of gradient to member and non-member samples, we construct a computable leakage scoring system, and classify different leakage risk levels accordingly. Experimental results show that the detection method proposed in this paper can effectively identify member inference attacks in various federated learning scenarios, and the quantitative disclosure index can accurately reflect the privacy risk level under different attack configurations and model states. This study provides theoretical and technical references for privacy security assessment and dynamic protection of federated learning systems.

The main objective of this research is to develop a deep learning-based method that enhances the accuracy and efficiency of detecting network intrusions from known malicious sources. Unlike conventional intrusion detection systems (IDS), which often struggle with identifying complex or novel attack patterns, this hybrid approach leverages the capabilities of Recurrent Neural Networks (RNNs) to model sequential data and temporal behaviors of network traffic. The system is designed to provide detailed information about the type, intensity, and target of potential attacks, enabling organizations to implement more strategic cyber security defenses. The study involves the analysis of existing deep learning algorithms for intrusion detection, collection of real-world network traffic data, and the development of a secure, robust, and accurate IDS framework. The proposed hybrid technique focuses on improving system integrity and minimizing false positives to ensure effective detection and mitigation of network threats. To study and analyze the existing algorithm of deep learning and technology of intrusion detection system Data collection regarding network attacks from molecules sources To determine how to improve security parameters To implements secure system in terms of integrity and accuracy

In contemporary communication systems, digital images occupy an irreplaceable role; however, the privacy-related risks attendant to their prevalent application have grown increasingly salient. This paper presents an image encryption scheme integrating a novel two-dimensional Ackley-Sine chaotic map (2D-ASM) with dynamic DNA operations. First, a two-dimensional Ackley-Sine chaotic map, constructed based on the Ackley function and sine function, is designed and validated through a series of chaotic indicators. Results demonstrate that 2D-ASM exhibits superior chaotic properties compared to several existing state-of-the-art chaotic maps, with its maximum Lyapunov exponent (LE) exceeding 23, Permutation Entropy (PE) close to 1 in the full parameter range, and correlation dimension (CD) significantly higher than comparative chaotic systems. The proposed 2D-ASM-based image encryption scheme leverages the SHA-256 hash value of the plaintext image and four external keys to jointly generate the initial conditions and parameters of the 2D-ASM chaotic system, thereby ensuring a sufficiently large key space of 2256. Subsequently, chaotic sequences generated by 2D-ASM are employed to permute and diffuse the plaintext image, followed by dynamic DNA coding, operations, and decoding to obtain the encrypted image. Security analyses and comparisons with several existing representative algorithms confirm that the proposed encryption scheme achieves excellent encryption performance: the Number of Pixels Change Rate (NPCR) is above 99.6%, the Unified Average Changing Intensity (UACI) approaches 33.4%, and the information entropy of ciphertext images reaches 7.999 or higher. The scheme can effectively resist various potential attacks, including statistical and differential attacks, and outperforms representative algorithms in pixel correlation reduction and anti-interference performance.

Background. Hereditary angioedema (HAE) is a rare genetic disorder with variable prevalence, characterized by recurrent swelling in various parts of the body, including potential laryngeal attacks, significantly affecting patients' quality of life. Methods. A nationwide, cross-sectional survey study was conducted between December 2023 and June 2024, targeting adults (aged 18 and older). The patients filled out different HAE-related questionnaires. Descriptive statistics were used to analyze and summarize the data. Results. The prevalence of HAE in Croatia is estimated to be 3.10 per 100,000 people. The majority were females, patients with positive family history, and type 1 HAE. The median diagnostic delay was 13 years, with initial attacks typically occurring in adolescence, but diagnosis was often not established until young to middle adulthood. Regarding quality of life, approximately 51% reported a significant impact. Fatigue was prevalent, with 46.9% of patients experiencing mild to moderate levels, and 22.4% suffering from severe fatigue. Most patients reported minimal depression, and 37.7% presented with moderate to severe anxiety. Among employed individuals, a median presenteeism of 20% indicated productivity loss while at work, in contrast to generally minimal absenteeism. Conclusions. Recent more substantial diagnostic efforts and increased awareness are contributing factors to the higher observed prevalence of HAE in Croatia, mainly due to the sustained work of a dedicated patient organization and a well-developed network of national HAE experts. Patients still experience a high disease burden, impaired quality of life, and difficulties with daily activities, which trends also observed in other HAE cohorts worldwide.

Cybersecurity of linear accelerators in radiation oncology beyond ransomware.