EAI Endorsed Transactions on Security and Safety
Growing threats and increasingly also failures due to complexity may compromise the security and resilience of network and service infrastructures. Applications and services require the security of data handling and we need new security architectures and scalable and interoperable security policies for this. There is a need to guarantee end-to-end security in data communications and storage, including identity management and authentication. INDEXING: CrossRef, Google Scholar, ProQuest, EBSCO, CNKI, Dimensions
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Enhancing Information Security in Industry 5.0: A Human-Centric LL-ISMS Framework to Bridge PDCA Gaps
Industry 5.0 is transforming the industrial paradigm by placing humans at the center of manufacturing and service processes. This approach, which emphasizes the synergistic collaboration between humans and cyber-physical systems (CPS), goes beyond the purely productivity-oriented goals of Industry 4.0 and prioritizes sustainability, resilience, and human well-being. However, the evolving nature of human-machine interactions poses complex security challenges. Traditional information security management systems (ISMS), which are based on the PDCA cycle and top-down approaches, face limitations in adapting to operational dynamics and receiving qualitative feedback from frontline workers. This paper introduces a complementary human-centric framework, “Low-Level ISMS” (LL-ISMS), while identifying emerging security risks in these complex ecosystems. The framework operates as a bottom-up feedback loop and focuses on four key stages: purposeful preparation (Do’), practical perception (Feel), critical analysis (Think), and active participation (Help). By connecting operational insights from employees to management decisions, LL-ISMS significantly increases the effectiveness of security management in Industry 5.0 environments and fosters a shared and dynamic security culture.
Copyright (c) 2025 EAI Endorsed Transactions on Security and Safety https://creativecommons.org/licenses/by/4.0/
Resonant Risk: A Sociotechnical Model for Understanding Cyber Threat Perception and Amplification in the Age of Artificial Intelligence (AI)
INTRODUCTION: Cyber incidents are increasingly shaped not only by technical severity but also by how risk signals are amplified through AI-mediated information ecosystems. Deepfakes, synthetic media, algorithmic amplification, and AI-generated misinformation can intensify public fear, distort trust, and trigger disproportionate societal responses. OBJECTIVES: This paper develops the Resonant Risk Model as a sociotechnical extension of the Social Amplification of Risk Framework for AI-era cybersecurity. It also proposes the Resonant Risk Management Framework to support perception-aware cyber risk governance. METHODS: The study uses theory-building, interdisciplinary literature synthesis, structured case selection, and comparative case analysis. Six cases are assessed using dimensions including technical severity, amplification channels, resonance factors, public perception, societal ripple effects, and feedback loops. RESULTS: The analysis shows that high technical severity does not always produce high public resonance. SolarWinds showed very high technical severity but moderate public resonance, while AI-driven misinformation and deepfake cases produced high trust erosion despite lower direct technical impact. CONCLUSION: The paper argues that cyber resilience must include perception monitoring, rapid communication, misinformation correction, and trust recovery alongside technical controls.
A Hybrid Differential Privacy and k-Anonymity Framework for Enhancing Location Privacy in Location-Based Services
The growing reliance on Location-Based Services (LBS) has intensified privacy risks, as the continuous collection of sensitive user location data exposes individuals to potential re-identification and unauthorised tracking. This paper presents a hybrid privacy-preserving framework that combines the Diameter-Bounded DBSCAN clustering algorithm for spatial k-anonymity with an adaptive Laplace mechanism for ε-differential privacy. This integration ensures the formation of compact anonymity groups while maintaining high data utility. Experimental evaluation on the real-world GeoLife dataset demonstrates 85.1% query accuracy, 0.14 trajectory distortion (EDR), and average query latency below 100 milliseconds for 20,000 users, outperforming DPPS and AdaptiveGrid baselines. Comprehensive sensitivity analysis of the diameter threshold (dmax) and evaluation of suppression bias confirm the framework’s robustness, scalability, and practical suitability for real-time LBS deployment.
Breaking the Loop: Adversarial Attacks on Cognitive-AI Feedback via Neural Signal Manipulation
INTRODUCTION: Brain-Computer Interfaces (BCIs) embedded with Artificial Intelligence (AI) have created powerful closed-loop cognitive systems in the fields of neurorehabilitation, robotics, and assistive technologies. However, these tightly bound systems of human-AI integration expose the system to new security vulnerabilities and adversarial distortions of neural signals. OBJECTIVES: The paper seeks to formally develop and assess neuro-adversarial attacks, a new class of attack vector that targets AI cognitive feedback systems through attacks on electroencephalographic (EEG) signals. The goal of the research was to simulate such attacks, measure the effects, and propose countermeasures. METHODS: Adversarial machine learning (AML) techniques, including Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD), were applied to open EEG datasets using Long Short Term Memory (LSTM), Convolutional Neural Networks (CNN), and Transformer-based models. Closed-loop simulations of BCI-AI systems, including real-time feedback, were conducted, and both the attack vectors and the attacks countermeasure approaches (e.g., VAEs, wavelet denoising, adversarial detectors) were tested. RESULTS: Neuro-adversarial perturbations yielded up to 30% reduction in classification accuracy and over 35% user intent misalignment. Transformer-based models performed relatively better, but overall performance degradation was significant. Defense strategies such as variational autoencoders and real-time adversarial detectors returned classification accuracy to over 80% and reduced successful attacks to below 10%. CONCLUSION: The threat model presented in this paper is a significant addition to the world of neuroscience and AI security. Neuro-adversarial attacks represent a real risk to cognitive-AI systems by misaligning human intent and action with machine response. Mobile layer signal sanitation and detection.
Brain-Computer Interfaces (BCI) Security
Adaptive Deception: Real-Time, AI-Powered Cybersecurity for Modern Threat Landscapes
INTRODUCTION: The current volume and sophistication of cyber threats are beyond overshadowing the security capabilities of traditional reactive security approaches. Herein, we present a new cybersecurity framework that incorporates real-time threat intelligence with adaptive deception technologies for the proactive defense of digital infrastructures. OBJECTIVES: The objectives of this research include: (1) develop an AI-driven cybersecurity framework, (2) incorporate real-time threat intelligence and deception-based active defense approaches, and (3) assess performance in simulated and real-world cyber-attack scenarios. METHODS: The proposed cyber-defense framework uses machine learning approaches, automated deception technologies (e.g., honeypots, moving target defense), and real-time threat intelligence feeds. The framework is constructed in a modular architecture and tested in simulation environments with real-time attack emulation. RESULTS: The framework performed with over 93% of threats visible, an adaptive response time < 2 seconds, and < 12% overhead imposed on the system. The framework achieved > 85% threat prevention, measured long recovery time, and measured system integrity improvements. CONCLUSION: The conclusion of this work illustrates that a proactive cybersecurity framework can be achieved through the integration of AI-enabled adaptive response with real-time threat intelligence. This work represents an advancement toward intelligent, self-learning systems capable of anticipating and responding to developing cyber threats with minimal human intervention.
This paper presents a systematic literature review on the psychology of insider threats—security risks originating from individuals within organizations. While this is a well-established research area, psychological perspectives remain underdeveloped. The extended version adds background to better contextualize the role of personality traits, psychological states, and situational factors in insider threats. The paper also highlights research gaps and the need for stronger theoretical foundations in this domain.
WebTracker: Real Webbrowsing Behaviors -- is Website Fingerprinting now Realistic?
The increasing demand for privacy has driven the adoption of privacy-enhancing tools such as VPNs, but website fingerprinting – the analysis of packet metadata like packet size and number of packets – still poses a substantial risk. Website fingerprinting allows adversaries to predict a victim’s web usage based on their browsing patterns, effectively creating a “fingerprint”. Recent studies have largely focused on laboratory settings and have assumed a simplified model: a victim visits a single website at a time and that all network packets can be observed. However, a new private browser extension, WebTracker, deployed with real users, shows that observed browsing patterns are significantly different from those previously assumed. Users’ behavior frequently exhibits defensive strategies, such as multiple websites overlapping and downloading simultaneously, which can interfere with website fingerprinting. A study of international users demonstrated that over 15% of websites overlap with at least another, with an average overlap time of 66 seconds, while a US-based study showed only 0.72% of websites overlap. Moreover, these overlaps typically occur shortly after the initial website download. These findings suggest that the beginning of a website is more crucial than the end for website fingerprinting attacks, highlighting the need for more analysis of webbrowsing behavior.
SeFS: A Secure and Efficient File Sharing Framework based on the Trusted Execution Environment
As the cloud-based file sharing becomes increasingly popular, it is crucial to protect the outsourced data against unauthorized access. Existing cryptography-based approach suffers from expensive re-encryption upon permission revocation. Other solutions that utilize Trusted Execution Environment (TEE) to enforce access control either expose the plaintext keys to users or turn out incapable of handling concurrent requests. In this paper, we propose SeFS, a secure and practical file sharing framework that leverages cooperation of server-side and client-side enclaves to enforce access control, with the former responsible for registration, authentication and access control enforcement and the latter performing file decryption. Such design significantly reduces the computation workload of server-side enclave, thus capable of handling concurrent requests. Meanwhile, it also supports immediate permission revocation, since the file decryption keys inside the client-side enclaves are destroyed immediately after use. We implement a prototype of SeFS and the evaluation demonstrates it enforces access control securely with high throughput and low latency.
Mitigating Vulnerabilities in Closed Source Software
Many techniques have been proposed to harden programs with protection mechanisms to defend against vulnerability exploits. Unfortunately the vast majority of them cannot be applied to closed source software because they require access to program source code. This paper presents our work on automatically hardening binary code with security workarounds, a protection mechanism that prevents vulnerabilities from being triggered by disabling vulnerable code. By working solely with binary code, our approach is applicable to closed source software. To automatically synthesize security workarounds, we develop binary program analysis techniques to identify existing error handling code in binary code, synthesize security workarounds in the form of binary code, and instrument security workarounds into binary programs. We designed and implemented a prototype or our approach for Windows and Linux binary programs. Our evaluation shows that our approach can apply security workarounds to an average of 69.3% of program code and the security workarounds successfully prevents exploits to trigger real-world vulnerabilities.
Copyright (c) 2022 EAI Endorsed Transactions on Security and Safety https://creativecommons.org/licenses/by/3.0/
A Systemic Security and Privacy Review: Attacks and Prevention Mechanisms over IOT Layers
In this contemporary era internet of things are used in every realm of life. Recent software’s (e.g., vehicle networking, smart grid, and wearable) are established in result of its use: furthermore, as development, consolidation, and revolution of varied ancient areas (e.g., medical and automotive). The number of devices connected in conjunction with the ad-hoc nature of the system any exacerbates the case. Therefore, security and privacy has emerged as a big challenge for the IoT. This paper provides an outline of IoT security attacks on Three-Layer Architecture: Three-layer such as application layer, network layer, perception layer/physical layer and attacks that are associated with these layers will be discussed. Moreover, this paper will provide some possible solution mechanisms for such attacks. The aim is to produce a radical survey associated with the privacy and security challenges of the IoT. This paper addresses these challenges from the attitude of technologies and design used. The objective of this paper is to rendering possible solution for various attacks on different layers of IoT architecture. It also presents comparison based on reviewing multiple solutions and defines the best one solution for a specific attack on particular layer.
A human-in-the-loop approach to understanding situation awareness in cyber defence analysis
In this paper we argue for a human-in-the-loop approach to the study of situation awareness in computer defence analysis (CDA). The cognitive phenomenon of situation awareness (SA) has received significant attention in cybersecurity/CDA research. Yet little of this work has attended to the cognitive aspects of situation awareness in the CDA context; instead, the human operator has been treated as an abstraction within the larger human-technology system. A more human-centric approach that seeks to understand the socio-cognitive work of human operators as they perform CDA will yield greater insights into the design of tools and interfaces for CDA. As support for this argument, we present our own work employing the Living Lab Framework through which we ground our experimental findings in contextual knowledge of real-world practice.
Cyber analysis is a complex task that requires the coordination of a large sociotechnical system of human analysts working together with technology. Adequate situation awareness of such a complex system requires more than aggregate situation awareness of individuals. Teamwork in the form of communication and information coordination is at the heart of team-level situation awareness. In this position paper, we report observations from previously conducted cognitive task analyses that suggest that teamwork is lacking in many cyber analysis organizations. Communication is ineffective, team roles are inconsistent across organizations, reward structures and selection may thwart collaboration, and the environment is conducive to individual work. Suggestions for improving teamwork in the cyber domain are offered.
Training organizational supervisors to detect and prevent cyber insider threats: two approaches
Cyber insider threat is intentional theft from, or sabotage of, a cyber system by someone within the organization. This article explores the use of advanced cognitive and instructional principles to accelerate learning in organizational supervisors to mitigate the cyber threat. It examines the potential advantage of using serious games to engage supervisors. It also posits two systematic instructional approaches for this training challenge – optimal path modelling and a competency-based approach. The paper concludes by discussing challenges of evaluating training for seldom occurring real world phenomena, like detecting a cyber-insider threat.
Decision-making and emotions in the contested information environment
Future conflicts will necessitate the ability to conduct effective military operations in a contested information environment. The building and maintaining of robust situational awareness, protection of decision-making effectiveness of individuals and teams, fighting through information attacks from both in, and through, the cyberspace domain, will be essential. Increasing the knowledge of the mechanisms involved in degrading task performance and decision-making during cyber attacks will enable the development of advanced human-centered defensive techniques that aid fight-through capability. In this position paper, the development and evaluation of software that simulates real-time and persistent manipulation of the information environment is discussed. Results of the evaluation indicated that the task performance of a team of decision-makers performing collaborative tasks could be degraded through real-time manipulation of cyberspace content and operation. The paper concludes with a discussion of focus and direction for future research and development. It is suggested that the building of a deeper understanding of the perceptual and cognitive factors that are significant in the relationship between information environment manipulation and reduction in task performance is required. This understanding will aid in the defence of cyberspace attacks, will aid in fight through and mission assurance, and will aid the Information Operations community.
Framing the Human Dimension in Cybersecurity
The advent of technologies that can seamlessly operate in different environments with differing levels of security present new challenges to the cybersecurity specialist seeking to ensure the safety of data, process or output of a complex system. This paper reviews the human dimension of cybersecurity. The Human Factors Integration (HFI) framework is employed as a structure with which to consider the many ways in which the human can differentially affect the security of a system both positively and negatively. We conclude that when the human factors element is analysed when specifying and designing secure and safe systems, it is far more likely that the human can assist and increase the overall level of security. As in other high technology sectors such as aviation or petrochemical, if not considered, the human can often ‘bulldoze’ through the most carefully considered and designed security or safety barriers
Preface to special issue on the cognitive science of cyber defence analysis
An introduction to the key topics in the cognitive science of cyber defence analysis including an overview of the challenges that exist and need to be faced in the field.
Trust in social computing. The case of peer-to-peer file sharing networks
Social computing and online communities are changing the fundamental way people share information and communicate with each other. Social computing focuses on how users may have more autonomy to express their ideas and participate in social exchanges in various ways, one of which may be peer-to-peer (P2P) file sharing. Given the greater risk of opportunistic behavior by malicious or criminal communities in P2P networks, it is crucial to understand the factors that affect individual’s use of P2P file sharing software. In this paper, we develop and empirically test a research model that includes trust beliefs and perceived risks as two major antecedent beliefs to the usage intention. Six trust antecedents are assessed including knowledge-based trust, cognitive trust, and both organizational and peer-network factors of institutional trust. Our preliminary results show general support for the model and offer some important implications for software vendors in P2P sharing industry and regulatory bodies.
A framework for usable end-user privacy control in social software systems
Recent studies have shown that many users struggle to properly manage selective sharing of the diverse information artefacts they deposit in social software tools. Most tools define privacy based on the ‘network of friends’ model, in which all ‘friends’ are created equal and all relationships are reciprocal. This model fails to support the privacy expectations that non-technical users bring from their real-life experiences, such as enabling different degrees of intimacy within one’s network and providing flexible, natural means of managing the volatile social relationships that social software systems confront. Furthermore, the model suffers from lack of empirical grounding and systematic evaluation. This paper presents a framework for building privacy management mechanisms for social software systems that is intuitive and easy to use for the average, non-technical user population of these systems. The framework is based on a grounded theory study of users’ information sharing behaviour in a social software tool. Results inform the design of OpnTag, a social software prototype that facilitates personal and social information management and sharing. Preliminary empirical data suggest that our proposed privacy framework is flexible enough to meet users’ varying information sharing needs in different contexts while maintaining adequate support for usability.
How did you know that about me? Protecting users against unwanted inferences
The widespread adoption of social computing applications is transforming our world. It has changed the way we routinely communicate and navigate our environment and enabled political revolutions. However, despite these applications’ ability to support social action, their use puts individual privacy at considerable risk. This is in large part due to the fact that the public sharing of personal information through social computing applications enables potentially unwanted inferences about users’ identity, location, or other related personal information. This paper provides a systematic overview of the social inference problem. It highlights the public’s and research community’s general lack of awareness of the problem and associated risks to user privacy. A social inference risk prediction framework is presented and associated empirical studies that attest to its validity. This framework is then used to outline the major research and practical challenges that need to be addressed if we are to deploy effective social inference protection systems. Challenges examined include how to address the computational complexity of social inference risk modeling and designing user interfaces that inform users about social inference opportunities.
Introduction to special issue on information privacy and trust in social media
Welcome message from the Editor-in-Chief
Security Analysis of Quantization Schemes for Channel-based Key Extraction
The use of reciprocal and random properties of wireless channels for the generation of secret keys is a highly attractive option for many applications that operate in a mobile environment. In recent years, several practice-oriented protocols have been proposed, but unfortunately without a sufficient and consistent security analysis and without a fair comparison between each other. This can be attributed to the fact that until now neither a common evaluation basis, nor a security metric in an on-line scenario (e.g., with changing channel properties) was proposed. We attempt to close this gap by presenting test vectors based on a large measurement campaign, an extensive comparative evaluation framework (including ten protocols as well as new on-line entropy estimators), and a rigorous experimental security analysis. Further, we answer for the first time a variety of security and performance related questions about the behavior of 10 channel-based key establishment schemes from the literature.
Reversible and Embedded Watermarking of Medical Images for Telemedicine
In this paper, we propose a new reversible watermarking of medical images for the applications in telemedicine. By using a bit-stream insertion scheme, the patient’s information can be treated as a watermark and can be embedded into the bit-stream of a cover image for remote transmission. The proposed method simplifies the design of traditional image coding after a reversible watermarking. Experimental results show that the compression ratio can be achieved up to 3.025. The watermarking capacity can be obtained over 0.75 bpp for some common images. In addition, the watermark can be extracted exactly and the cover image can be reconstructed either in a lossless way or a lossy way. The obtained results also show the improvement with respect to previous works.
Redundancy Elimination in DTN via ACK Mechanism
The traditional routing protocols for delay tolerant networks (DTN) usually take the strategy of spreading multiple copies of one message to the networks. When one copy reaches destination, the transmission of other copies not only waste the bandwidth but also deprive other messages of the opportunities for transmission. This paper brings up a mechanism to eliminate the redundant copies. By adding an acknowledge field to the packet header to delete redundant copies, it can degrade the network overhead while improve the delivery ratio. Simulation results confirm that the proposed method can improve the performance of epidemic and Spray and Wait routing protocol.
A Hierarchical of Security Situation Element Acquisition Mechanism in Wireless Sensor Network
In wireless sensor network, the processing ability of the sensor nodes is poor. And the security situational element acquisition is also a serious problem. Thus, this paper proposes a hierarchical framework of security situational elements acquisition mechanism. In this framework, support vector machine hyper sphere multi class algorithm is introduced as basic classifier. The method of attribute reduction uses non negative matrix factorization algorithm. The fuzzy classification algorithm used to initialize non negative matrix factorization, in order to avoid the local optimal which is caused by non negative matrix factorization random initialization. In the sink node classification rules and attribute reduction rules are formed by learning. The classification analyses respectively focus on the cluster head and sink node, which can reduce the requirement of the sensor node properties. Attribute reduction before the data transmission, which reduces communication consumption data transmission, improves the performance of classifiers. By simulation analysis, the scheme has preferably accuracy in the situation elements acquisiton, and smaller communication overhead in the process of information transmission.
Advanced Audio Coding (AAC), a standardized lossy compression scheme for digital audio, which was designed to be the successor of the MP3 format, generally achieves better sound quality than MP3 at similar bit rates. While AAC is also the default or standard audio format for many devices and AAC audio files may be presented as important digital evidences, the authentication of the audio files is highly needed but relatively missing. In this paper, we propose a scheme to expose tampered AAC audio streams that are encoded at the same encoding bit-rate. Specifically, we design a shift-recompression based method to retrieve the differential features between the re-encoded audio stream at each shifting and original audio stream, learning classifier is employed to recognize different patterns of differential features of the doctored forgery files and original (untouched) audio files. Experimental results show that our approach is very promising and effective to detect the forgery of the same encoding bit-rate on AAC audio streams. Our study also shows that shift recompression-based differential analysis is very effective for detection of the MP3 forgery at the same bit rate.
A improved Network Security Situation Awareness Model
In order to reflect the situation of network security assessment performance fully and accurately, a new network security situation awareness model based on information fusion was proposed. Network security situation is the result of fusion three aspects evaluation. In terms of attack, to improve the accuracy of evaluation, a situation assessment method of DDoS attack based on the information of data packet was proposed. In terms of vulnerability, a improved Common Vulnerability Scoring System (CVSS) was raised and maked the assessment more comprehensive. In terms of node weights, the method of calculating the combined weights and optimizing the result by Sequence Quadratic Program (SQP) algorithm which reduced the uncertainty of fusion was raised. To verify the validity and necessity of the method, a testing platform was built and used to test through evaluating 2000 DAPRA data sets. Experiments show that the method can improve the accuracy of evaluation results.
Network Security Risk Assessment Based on Item Response Theory
Owing to the traditional risk assessment method has one-sidedness and is difficult to reflect the real network situation, a risk assessment method based on Item Response Theory (IRT) is put forward in network security. First of all, the novel algorithms of calculating the threat of attack and the successful probability of attack are proposed by the combination of IRT model and Service Security Level. Secondly, the service weight of importance is calculated by the three-demarcation analytic hierarchy process. Finally, the risk situation graph of service, host and network logic layer could be generated by the improved method. The simulation results show that this method can be more comprehensive consideration of factors which are affecting network security, and a more realistic network risk situation graph in real-time will be obtained.
An Improved MOEA/D for QoS Oriented Multimedia Multicasting with Network Coding
Recent years witness a significant growth in multimedia applications. Among them, a stream of applications is real-time and requires one-to-many fast data transmission with stringent quality-of-service (QoS) requirements, where multicast is an important supporting technology. In particular, with more and more mobile end users requesting real-time broadband multimedia applications, it is of vital importance to provide them with satisfied quality of experience. As network coding can offer higher bandwidth to users and accommodate more flows for networks than traditional routing, this paper studies the multicast routing problem with network coding and formulates it as a multi-objective optimization problem. As delay and packet loss ratio (PLR) are two important performance indicators for QoS, we consider them as the two objectives for minimization. To address the problem above, we present a multi-objective evolutionary algorithm based on decomposition (MOEA/D), where an all population updating rule is devised to address the problem of lacking feasible solutions in the search space. Experimental results demonstrate the effectiveness of the proposed algorithm and it outperforms a number of state-of-the-art algorithms.
Mobile Watermarking against Geometrical Distortions
Mobile watermarking robust to geometrical distortions is still a great challenge. In mobile watermarking, efficient computation is necessary because mobile devices have very limited resources due to power consumption. In this paper, we propose a low-complexity geometrically resilient watermarking approach based on the optimal tradeoff circular harmonic function (OTCHF) correlation filter and the minimum average correlation energy Mellin radial harmonic (MACE-MRH) correlation filter. By the rotation, translation and scale tolerance properties of the two kinds of filter, the proposed watermark detector can be robust to geometrical attacks. The embedded watermark is weighted by a perceptual mask which matches very well with the properties of the human visual system. Before correlation, a whitening process is utilized to improve watermark detection reliability. Experimental results demonstrate that the proposed watermarking approach is computationally efficient and robust to geometrical distortions.