Journal of Cybersecurity and Privacy

Anti-analysis techniques, also known as evasive techniques, enable Windows malware to detect and evade dynamic inspection environments, undermining the effectiveness of virtual-machine and sandbox-based inspection. Despite extensive prior research, no unified classification has been paired with a large-scale empirical evaluation of static detection capabilities for these behaviors. This paper addresses this gap by presenting a comprehensive classification and detection framework. We consolidate 94 anti-analysis techniques from academic, community, and threat-intelligence sources into nine mechanistic categories and derive corresponding YARA rules for static identification. In total, 82 YARA signatures were authored or refined and evaluated on 459,508 malware and 92,508 goodware samples. After iterative refinement using precision thresholds, 42 rules achieved high accuracy (≥75%), 16 showed moderate precision (50–75%), and 24 were discarded due to unreliability. The results indicate strong static detectability for firmware- and BIOS-based checks, but limited precision for timing-based evasions, which frequently overlap with benign behavior. Although YARA provides broad coverage of observable artifacts, its static nature limits detection under obfuscation or runtime mutation; our measurements therefore represent conservative estimates of technique prevalence. All validated rules are released in an open-source repository to support reproducibility, improve incident-response workflows, and strengthen prevention and mitigation against real-world threats. Future work will explore hybrid validation, container-evasion extensions, and forensic attribution based on signature co-occurrence patterns. Full article

With the widespread adoption of anonymity networks such as Tor, I2P, and JonDonym, reliably classifying darknet traffic remains challenging due to feature redundancy and severe class imbalance in encrypted flows. Existing approaches often rely on static feature-selection strategies and generic oversampling methods, which limit robustness and may distort traffic semantics. This study proposes an adaptive classification framework integrating Adaptive Weighted Feature Aggregation (AWFA) for reliability-aware feature selection and Traffic-Aware SMOTE (TA-SMOTE) for semantically constrained perturbations of packet-size and timing features while preserving flow-level structure. The framework is evaluated on a two-layer hierarchy comprising browser-level (L1) and application-level (L2) classification. At the L2, the proposed AWFA and TA-SMOTE pipeline attains a macro-F1 score of 73.81%, significantly exceeding PCA-based reduction and traditional RF-based selection with SMOTE. At the browser level (L1), macro-F1 rises from 91.58% to 96.09% while reducing the feature space from 84 to 40 attributes, highlighting both performance improvements and structural efficiency gains. Additional semantic validation confirms that the balancing process preserves the statistical and structural characteristics of genuine darknet traffic. These results indicate that reliability-aware feature aggregation and traffic-aware balancing provide a practical, trustworthy approach to modern darknet traffic classification. Full article

Asset discovery is a fundamental but inherently flawed capability in cybersecurity, as current methodologies frequently confuse preliminary discovery observations with definitive asset inventories, thereby obscuring uncertainty, restricting auditability, and eroding trust in security-critical decision-making. This work addresses the issue of inconsistent asset identification in dynamic IT settings by presenting an evidence-based architectural paradigm that clearly distinguishes observation, identity resolution, and inventory representation. The principal research aim is to develop and authenticate an architecture that maintains discovery evidence, facilitates deterministic, verifiable identity resolution, and supports interpretable inventory derivation. In contrast to state-centric and model-driven methodologies, the proposed architecture enhances (i) traceability through the preservation of time-scoped, method-attributed observations, (ii) identity continuity amidst dynamic conditions such as IP reassignment and infrastructure modifications, and (iii) auditability by facilitating the reconstruction of inventory claims from foundational evidence. An examined proof-of-concept implementation in a controlled yet realistic network environment shows superior identity stability, greater discovery traceability, and retention of historical context relative to traditional inventory models. The results validate the practicality and architectural benefits of the strategy; nevertheless, the evaluation is constrained by a lack of formalised performance indicators and adversarial robustness, which are recognised as priorities for further investigation. Full article

Raising cybersecurity awareness (CSA) of employees is crucial for all modern organisations. To meet the organisational need for CSA, activities aimed at increasing CSA have been the focus of both industry and research in the past. There are, subsequently, a plethora of CSA activities for organisations to choose from. Nevertheless, research consistently reports that organisations struggle to raise CSA to an appropriate level, and a core issue lies in their ability to select CSA activities and effectively adopt them. This paper used semi-structured interviews with practitioners working on CSA adoption in public-sector organisations to identify what practitioners perceive as success factors. The interviews were analysed through a socio-technical lens and resulted in a taxonomy that groups success factors for CSA adoption in the three socio-technical dimensions: organisational, user-centric, and technical. The taxonomy outlines ten success factors and demonstrates how the participants see success of CSA activities as not only dependent on technical factors but also, and perhaps even more important, user-adaptability and organisational readiness. The results were validated in a workshop with CSA experts across Europe, who highlighted the practical usefulness of the taxonomy as both a map of potential challenges and a teaching tool for educating new CSA practitioners. Full article

LoRaWAN’s role in global maritime logistics has allowed for efficient monitoring of ships and cargo, but it also comes with critical cybersecurity vulnerabilities. Experimental validation of three attack vectors—replay attacks, narrowband jamming and metadata inference—is conducted using a reproducible digital-twin LoRaWAN dataset reflecting Rotterdam port-like operational patterns (N = 20,000 baseline transmissions). Using controlled simulations and Kolmogorov–Smirnov statistical analysis, we show that: (1) replay attacks are feasible under Activation by Personalization (ABP) configurations lacking enforced frame-counter validation and exhibit no univariate separation from legitimate traffic under Kolmogorov–Smirnov analysis (p > 0.46 for all evaluated radio features); (2) narrowband jamming leads to significant SNR degradation (p = 2.36 × 10⁻⁵) on targeted channels without inducing broad distributional anomalies across other radio features; and (3) metadata-only analysis supports elevated metadata-based re-identification susceptibility (median $R_d=0.834$), indicating high predictability under passive observation which can reveal operationally relevant signals even when AES-128 is employed. Our proposed layered mitigation framework consists of mandatory Over-the-Air Activation (OTAA), cryptographic key rotation, channel diversity incorporating Adaptive Data Rate (ADR), gateway hardening, and protocol-level enforcement considerations, customized for maritime LPWAN scenarios. We provide experiment-backed evidence and actionable recommendations to connect academic LPWAN security research to that of industrial maritime practice. Full article

Detecting denial of service traffic remains challenging when malicious sessions exhibit flow characteristics that closely resemble benign network behavior, particularly in low-rate attack settings. This study examines whether autoencoder-based feature compression can improve flow-based intrusion detection while maintaining a deployment-oriented design. We develop a lightweight pipeline that learns a low-dimensional latent representation of tabular flow features using an autoencoder and performs classification using Random Forest, LightGBM, and a radial basis function neural network. Using the CICIDS 2017 dataset, the best performing configurations achieve 99.43 percent accuracy with autoencoder plus Random Forest and 99.39 percent with autoencoder plus LightGBM, while autoencoder plus radial basis function neural network achieves 98.27 percent, with consistently strong precision, recall, and F1-score. The findings support practice by showing that high detection performance can be achieved using compact learned features that reduce input complexity for downstream models, which is beneficial for operational monitoring environments. The study advances knowledge by providing a reproducible evaluation of representation learning as a feature compression step for tabular intrusion detection, and by linking model performance to measurable computational considerations relevant to real-world deployment. Full article

The convergence of Large Language Models (LLMs) and Internet of Things (IoT) systems has created a new class of intelligent applications across healthcare, industrial automation, smart cities, and connected homes. However, this integration introduces a complex and largely underexplored security landscape. LLMs deployed in IoT contexts face threats spanning both the AI and embedded systems domains, including prompt injection through sensor-driven inputs, model extraction from edge devices, data poisoning of IoT data streams, and privacy leakage through LLM-generated responses grounded in personal data. Simultaneously, LLMs are proving to be powerful tools for IoT security, with LLM-based intrusion detection systems achieving 95–99% accuracy on standard IoT datasets and LLM-driven threat intelligence outperforming traditional machine learning by significant margins. We systematically review 88 papers from IEEE, ACM, MDPI, and arXiv (2020–2025), providing: (1) a structured taxonomy of security threats targeting LLM-IoT systems, (2) a review of LLMs as security enablers for IoT, (3) an evaluation of privacy-preserving architectures including federated learning, differential privacy, homomorphic encryption, and trusted execution environments, (4) domain-specific security analysis across healthcare, industrial, smart home, smart grid, and vehicular IoT, and (5) a literature-based comparative analysis of LLM-based security systems. A central finding is the accuracy–efficiency–privacy trilemma: the model compression techniques needed to deploy LLMs on resource-constrained IoT devices can degrade security and even introduce new vulnerabilities. Our analysis provides researchers and practitioners with a structured understanding of both the risks and opportunities at the frontier of LLM-IoT security. Full article

The protection of personal information has become a defining challenge for higher education institutions, particularly in developing contexts where regulatory frameworks are often strong on paper but weak in practice. This study investigates student perceptions of privacy within Zimbabwe’s higher education system, focusing on three constructs: awareness, expectations, and confidence across nine core privacy components derived from international principles (FIPPs, OECD, GDPR) and the Zimbabwe Data Protection Act (ZDPA). Using survey data from 287 students across diverse programmes and modes of study, descriptive and comparative analyses reveal a striking pattern: students demonstrate high awareness and very strong expectations, yet their confidence in institutional compliance remains significantly lower. The largest deficits were found in privacy education, consent, and notice/openness, suggesting that institutions are perceived as technically competent in data handling but weak in transparency, accountability, and student engagement. The research extends privacy perception models by considering the discrepancy between the students’ expectations and the institutional trust. It also encourages universities to go beyond mere compliance by implementing concrete measures such as privacy training, clear consent, and frequent data audits. The findings contribute to global debates on privacy by offering evidence from the Global South, showing that the key challenge is not student ignorance but institutional trustworthiness. Bridging this awareness-confidence gap is essential for building a privacy-conscious academic environment. Full article

Ensuring ongoing endpoint security compliance across diverse, hybrid IT infrastructures poses a continual operational challenge, especially in enterprise Linux systems, where manual verification methods are difficult to scale and prone to inconsistency. This study offers an empirical assessment of an automated methodology for monitoring endpoint compliance and security, applied within a mid-sized IT consulting firm. The suggested methodology incorporates automated compliance scanning, malware detection, endpoint verification, and remediation utilising open-source technology, all orchestrated through centralised automation and reporting systems. The evaluation follows an observational comparative methodology, contrasting manual compliance operations with automated enforcement across 60 Linux endpoints (30 Fedora and 30 Ubuntu systems) over two equivalent eight-week operational periods. The analysis emphasises operational parameters such as administrative workload, configuration uniformity, and audit preparedness. The findings demonstrate that automation reduced manual compliance-related tasks by roughly 70–80%, enhanced configuration consistency across endpoints through continuous enforcement, and enabled automated production of audit-ready compliance reports. The findings provide concrete evidence that operational security automation can markedly improve endpoint compliance management in business Linux and hybrid IT environments. Full article

Enforcing cybersecurity governance is no longer a choice. It has become essential to protect nations’ safety and economy. In addition to the well-known cybersecurity standards that provide guidelines for implementing security controls, many countries have introduced national cybersecurity frameworks to meet their requirements and needs. These countries also provide assessment tools to check that organizations comply with these frameworks. This research emphasizes the importance of efficient cybersecurity governance practices, highlighting the Jordanian National Cyber Security Framework (JNCSF) that was announced in 2019. We have chosen this framework because, since its launch, it has not been presented or analyzed thoroughly by any of the existing studies. Moreover, the National Cyber Security Center (NCSC) in Jordan has not announced any public self-assessment tools for organizations to evaluate their compliance with the JNCSF. Therefore, the absence of a structured and publicly available self-assessment mechanism for the JNCSF creates a challenge for organizations in objectively measuring their cybersecurity governance readiness. Accordingly, the main contributions of this paper are to provide a detailed breakdown and discussion of the JNCSF, which supports organizations in Jordan and also shares the JNCSF philosophy regionally and internationally. Additionally, this study introduces an efficient self-assessment tool (named JCCT) that can be used both offline and online. JCCT accurately measures the institution’s cybersecurity compliance against JNCSF and international standards (ISO and NIST), reflecting its current state and the potential impact on its risk profile. Moreover, this paper proposes new compliance score equations based on a comprehensive mathematical model that generally benefits any governance system. The JCCT tool offers rich, interactive, customized dashboards and automatically generates reports with recommended action plans for the organization. Full article

Vehicular Ad Hoc Networks (VANETs) have become the foundation for the implementation of intelligent transportation systems and new vistas for road safety and traffic efficiency. However, these networks are still susceptible to Sybil attacks, a form of attack that requires malicious entities to create a series of fake identities in order to have an out-of-proportion influence. The present paper puts forth a new Sybil attack detection framework that combines Verifiable Delay Functions (VDFs) in synergistic cooperation with a hierarchical fog-cloud computing structure. Our method does not rely on any additional properties of VDFs but uses them to prove uniqueness computationally, deploying purposefully placed fog nodes for effective localized detection. We mathematically formulate a multi-layered detection algorithm that processes interactions between vehicles on two fog (and cloud) layers to produce suspicion scores using spatiotemporal consistency and VDF challenge-response patterns. Security analysis proves the system’s ability to resist a range of Sybil attack variants with performance evaluation outperforming at detection above 97.8% and false positives below 2.3%. The incorporation of machine learning techniques also extends detection capabilities, and our hybrid VDF-ML method proves better adaptation to the changing attack patterns. Details of implementation and detailed simulations in various traffic situations prove the feasibility and efficiency of our proposed solution to set a new level playing ground for secure VANET communications. Full article

Cybercriminals use advanced techniques to launch an attack against organizations, which causes disruption of normal business activities. The traditional signature-based malware detection methods are not effective in the detection of ransomware. Therefore, the use of machine learning and deep learning for malware detection is becoming a major area of research. There are two types of malware detection strategies, namely, static and dynamic. This work investigates the task-dependent effectiveness of static PE header-based detection by systematically evaluating three binary classification problems of increasing difficulty: ransomware vs. benign, malware vs. benign, and ransomware vs. other malware families. An end-to-end machine learning pipeline is implemented, including dataset-specific preprocessing, class imbalance handling, model training, and evaluation using imbalance-aware metrics. Random Forest, Support Vector Machine, and XGBoost models are assessed across all tasks, with SHAP used to analyze feature contribution and explain performance degradation. The experimental results demonstrate that tree-based ensemble models, particularly XGBoost, achieve strong detection performance when class boundaries are structurally distinct, but they struggle when ransomware must be distinguished from structurally similar malware. The results indicate that static analysis based on PE header features can be a viable approach for pre-execution triage, but they exhibit clear limitations for fine-grained ransomware discrimination. Full article

The escalating scale and complexity of cybercrime necessitate standardized digital forensic protocols to ensure the integrity and admissibility of digital evidence. This study empirically assesses the use of ISO/IEC 27037 and ISO/IEC 27041 through three real-world digital forensic case studies conducted in organizational settings. A multi-case methodology was employed, encompassing a multinational corporate criminal investigation, an internal employee misbehaviour probe, and an examination into mobile- and cloud-based data leaks. The effect of synchronized standard implementation was evaluated using audit-based and quantitative indicators that measure forensic process quality as a system attribute. The findings demonstrate that the systematic implementation of ISO/IEC 27037 and ISO/IEC 27041 improves investigative traceability, documentation quality, and evidentiary robustness. In the worldwide case study, documentation completeness increased by 18%, and all digital evidence was deemed admissible in judicial proceedings, surpassing the institutional baseline admissibility rate of 82%. In other instances, evidence gathered within the same framework was acknowledged in organizational or disciplinary review processes, resulting in similar enhancements in documentation quality and procedural consistency, notwithstanding technological and organizational limitations. The paper develops and empirically substantiates an integrated procedural validation model that connects evidence-handling practices with method and instrument validation. The results indicate that the synchronized implementation of ISO/IEC forensic standards improves the transparency, dependability, and auditability of digital forensic investigations. Full article

Virtualization is the building block of modern cloud computing infrastructure. However, it remains vulnerable to a range of security threats, including malicious co-located tenants, hypervisor vulnerabilities, and side-channel attacks. These threats are generally mitigated by developing and deploying advanced and complex security solutions that incur significant performance overhead. Prior work on virtual machines (VMs) and containers has mainly evaluated basic security solutions, such as firewalls, using narrow performance metrics and synthetic models within limited evaluation frameworks. These studies often overlook advanced security modules in both user and kernel space, lack the flexibility to incorporate emerging features, and fail to capture detailed system-level impacts. We address these gaps with HyperShield, an open-source framework for unified security evaluation across VMs and containers that mimics a realistic cloud infrastructure. HyperShield supports advanced security modules in both user and kernel space, providing rich system-level performance metrics for comprehensive evaluation. Our performance evaluation shows that containers generally outperform VMs due to their lower virtualization overhead, achieving a throughput of 9.38 Gb/s compared to 1.98 Gb/s for VMs for our benchmarks. However, VMs’ performance is comparable for kernel-space deployments, as Docker uses the shared kernel space of the Docker bridge, which can result in packet congestion. In latency-sensitive workloads, VM access latency of 14.91 ms is comparable to Docker’s 12.86 ms. In storage benchmarks, FIO, however, VMs outperform Docker due to the overhead of Docker’s layered, copy-on-write file system, whereas VMs leverage optimized virtual block devices with near-native I/O performance. These results highlight performance dependencies on benchmark choice, trade-offs in deploying security workloads between user and kernel space, and the choice of containers and virtual machines as virtualization environments. Therefore, HyperShield provides a comprehensive evaluation toolkit for exploring an optimal security-module deployment strategy. Full article

Embedded devices in modern power systems offer increased connectivity and remote reprogrammability/reconfigurability. These features along with interconnections between Information Technology (IT) and Operational Technology (OT) networks enable greater agility, reduced operator workload, and enhanced power system performance and capabilities, as well as expanding the cyber-attack surface. This increased cyber-attack surface, as well as increasingly complex, diverse, and potentially untrustworthy software/hardware supply chains, increases the need for robust real-time monitoring in power systems, and more generally in cyber–physical systems (CPS). We propose a novel framework for real-time monitoring and anomaly detection in CPS, specifically smart grid substations and SCADA systems. The proposed framework enables real-time signal temporal logic condition-based anomaly monitoring by processing raw captured packets from the communication network through a hierarchical semantic extraction and tag processing pipeline into a time series of semantic events and observations, that are then evaluated against expected temporal properties to detect and localize anomalies. We demonstrate the efficacy of our methodology on a hardware in the loop (HITL) testbed under several attack scenarios. The HITL testbed includes multiple physical power system devices (real-time automation controllers and relays) and simulated devices (Phasor Measurement Units—PMUs, relays, Phasor Data Concentrators—PDCs), all interfaced to a dynamic power system simulator. Full article

In increasingly interconnected tactical environments, cybersecurity, trust, and interoperability must evolve in tandem. Federated Coalition Networks (FCNs) enable multinational cooperation while preserving national sovereignty; however, the secure management of identities, policies, and configurations across coalition domains remains a critical challenge, particularly under adversarial and resource-constrained conditions. This paper proposes a blockchain-enabled management framework aligned with the defense-in-depth paradigm, focusing on management-plane functions such as policy enforcement, public key infrastructure (PKI) management, and auditable governance, rather than time-critical tactical communications. The solution relies on a permissioned blockchain architecture with Byzantine Fault Tolerant consensus, avoiding energy-intensive Proof-of-Work mechanisms and supporting operation under Disconnected, Intermittent, and Low-bandwidth (DIL) conditions. A coalition-level trust-and-governance model is introduced to prevent unilateral control while preserving national autonomy. A realistic use case and a proof-of-concept implementation demonstrate the feasibility of the approach, showing bounded latency, limited energy overhead, and sufficient throughput for FCN management. These results indicate that appropriately tailored blockchain solutions can effectively enhance resilience, trust, and compliance in federated defense networks. Full article

Small-and-medium-sized enterprises (SMEs) increasingly depend on business partnerships to access markets and scale operations, yet they often face trust barriers during contract formation due to the complexity of the verification of their cybersecurity posture and compliance status by their partners. This problem is intensified by rising regulatory expectations, notably the EU Cyber Resilience Act (CRA), which many SMEs struggle to interpret and operationalize under constraints of budget, skills, and fragmented responsibilities. This study adopts a Design Science Research approach to blueprint and evaluate a lightweight mapping framework that links commonly implemented security controls to CRA requirements and to widely recognized benchmarks (ISO/IEC 27001 and CIS). Grounded in Institutional Theory and Socio-Technical Systems Theory, the artefact translates regulatory obligations into actionable, evidence-backed controls and produces partner-facing outputs that support transparency in negotiations and service level agreements. The framework is iteratively co-created with a multidisciplinary expert community. Expected contributions include a practical mechanism for making cybersecurity maturity visible, accelerating partnership formation, and enabling sustainable interorganizational relationships while remaining feasible for resource-constrained SMEs. Full article

The IEC 62443 standard defines that, based on risk assessment, different parts of an Industrial Automation and Control System (IACS) may have different security levels, and that parts with the same security level can be designated as separate zones. Furthermore, communication between different zones, both intra-IACS and inter-IACS, can be done via conduits. In this article, we argue that zones and particularly conduits can benefit from more detailed discussions of their architecture and implementation. Consequently, as novel contributions we (1) describe detailed principles for implementing conduits; (2) outline a process for connecting zones with potentially different Security Levels (SLs), expressed in the form of a flow chart; and (3) discuss challenges related to the application of zones and conduits in practice. Full article

In the evolving cybersecurity landscape, detecting Thai-English code-mixed malicious scripts within high-trust domains such as governmental and academic portals presents a significant defensive challenge. While Transformer-based architectures excel in semantic parsing, they often exhibit ‘Structural Bias,’ misinterpreting the high-entropy syntax of benign legacy HyperText Markup Language (HTML) as malicious obfuscation due to inherent ‘Attention Deficit’ in token-limited models. To address this, we propose an Explainable AI (XAI)-Driven Hybrid Architecture grounded in a ‘Selective Integration’ strategy. Unlike traditional hybrid models, our framework mathematically formalizes the fusion process by synergizing context-aware WangChanBERTa embeddings with orthogonal structural statistics through Dempster-Shafer Theory and Conditional Mutual Information (CMI). The proposed model was validated on a high-fidelity corpus, achieving a state-of-the-art F1-score of 0.9908, significantly outperforming standalone Transformers, Random Forest, and unsupervised baselines. XAI diagnostics revealed a ‘Dual-Validation’ mechanism where structural features act as an epistemic anchor. This mechanism effectively triggers a ‘Semantic Veto’ to filter hallucinations caused by benign complexity, achieving a remarkably low False Positive Rate (FPR) of 0.0116. Our findings demonstrate that hybridization is most effective when engineered features provide mathematical orthogonality to semantic embeddings. This work offers a robust, theoretically grounded framework for securing critical digital infrastructures in low-resource linguistic environments. Full article