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        <title>Journal of Information Assurance and Security Feed</title>
        <link>https://reference-global.com/journal/IAS</link>
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        <copyright>All rights reserved 2026, Cerebration Science Publishing Co., Limited</copyright>
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        <item>
            <title><![CDATA[BlockFedIDS: A Blockchain-Enabled Federated Learning Framework for Privacy-Preserving Intrusion Detection in Heterogeneous IoT Networks]]></title>
            <link>https://reference-global.com/article/10.2478/ias-2026-0022</link>
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            <pubDate>Mon, 28 Sep 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[The rapid growth of IoT networks demands privacy-preserving intrusion detection systems that operate on distributed data without exposing sensitive traffic information. This paper presents BlockFedIDS, a blockchain-enabled federated learning framework that eliminates the centralised aggregator using smart contracts. The framework integrates Differentially Private Stochastic Gradient Descent (DP-SGD) for differential privacy, Multi-Krum for Byzantine-resilient aggregation, and Pedersen commitments for secure model update verification. Experimental evaluation on the NSL-KDD and CIC-IDS2017 datasets with 20 IoT clients achieves 97.14% detection accuracy and 0.984 F1-score, while reducing poisoning-induced performance degradation by 74.3% compared with FedAvg and requiring only 1.4 KB of on-chain storage per training round.]]></description>
            <category>ARTICLE</category>
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            <title><![CDATA[Internet of things and Blockchain for Smart Machine Tools in Industry 4.0, A Review]]></title>
            <link>https://reference-global.com/article/10.2478/ias-2026-0021</link>
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            <pubDate>Mon, 21 Sep 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[In order to increase transparency, traceability and security of data for smart machine tools within industry 4.0, blockchain technology and Internet of Things (IoTs) are applied. This review examines applications of IoT and blockchain technologies in smart machine tools within Industry 4.0 era. Thus, the paper systematically examines blockchain application in real-time data collection and communication between interconnected devices to provide data integrity, traceability, and decentralized security. Thus, to improve the security and efficiency of smart manufacturing within the industry 4.0 era, applications of blockchain and IoT in smart machine tools are studied.]]></description>
            <category>ARTICLE</category>
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        <item>
            <title><![CDATA[A Hybrid Z-Isomorphic GNN Framework for Robust DDoS Attack Detection in Software-Defined Networks]]></title>
            <link>https://reference-global.com/article/10.2478/ias-2026-0019</link>
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            <pubDate>Thu, 20 Aug 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[Although SDN provides a programmable, centrally managed framework for modern networks, that same centralization leaves it exposed to attacks such as Distributed Denial of Service (DDoS). This paper proposes an intrusion detection framework that couples Z-Isomorphic Sigmoid Graph Neural Networks (ZIS-GNN) with Bonobo-Optimization-based (EKPC-BOA) feature selection. The sigmoid-based activation strengthens the graph representation relative to conventional GNNs, capturing complex traffic patterns more faithfully, while the hybrid selector – combining the Bonobo Optimization Algorithm with an entropy score and Pearson correlation – distils the most informative features from the traffic data and thereby improves both efficiency and accuracy. Experiments demonstrate that the proposed ZIS-GNN+EKPC-BOA model attains an accuracy of 97.36%, a precision of 97.37%, and an F1-score of 97.58%, outperforming baseline models such as DNN (89.60%), LSTM (91.68%), BiLSTM (93.77%), and GNN (95.86%), as well as the standard graph baselines GCN (96.18%) and the attention-based GAT (96.58%). The results show the effectiveness of combining graph-based learning with hybrid feature selection for intrusion detection in SDN.]]></description>
            <category>ARTICLE</category>
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        <item>
            <title><![CDATA[Trust-Aware Federated Learning for Robust and Secure Social Media Analysis]]></title>
            <link>https://reference-global.com/article/10.2478/ias-2026-0020</link>
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            <pubDate>Thu, 20 Aug 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[The rapid growth of social media requires privacy-preserving approaches for detecting toxic, fraudulent, misleading, and inappropriate content while protecting sensitive user information. This paper proposes a trust-aware federated learning framework that enables collaborative model training without sharing raw user data. An attention-based Long Short-Term Memory (LSTM) network captures important semantic relationships in textual content, while a trust scoring mechanism identifies unreliable or potentially malicious clients. A blockchain layer provides secure verification of model updates and enhances transparency throughout the federated training process. Experimental results demonstrate strong classification performance with limited blockchain overhead, highlighting the framework’s potential as a scalable, secure, and efficient solution for privacy-preserving social media content analysis.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[AegisSentinel: Secure Medical Image Transmission with HFQE-S Encryption and COA Optimized HAPCNN Attack Detection]]></title>
            <link>https://reference-global.com/article/10.2478/ias-2026-0017</link>
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            <pubDate>Fri, 07 Aug 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[Medical images such as computed tomography (CT), magnetic resonance imaging (MRI), and X-ray scans are commonly sent through health care networks for remote medical diagnostics and consultations. While sending, the said images become susceptible to being exposed to noise, tampered, and attacked, which might not seem visually perceptible yet could cause significant medical errors. In this study, we present AegisSentinel, an end-to-end solution that uses Hybrid Fibonacci Q-Matrix Encryption with SHA Key (HFQE-S) to secure medical images, a Hierarchical Auto-Associative Polynomial CNN (HAPCNN) to detect transmission attacks, and Crayfish Optimization Algorithm (COA) to tune the latter model. The algorithm was tested using 8,550 labeled CT scan images from the TCGA-LUAD dataset across six classes (clean and five types of attacks). The proposed encryption scheme yielded an entropy value of 7.9973 bits owing to a lossless decryption process with infinite PSNR. COA-tuned HAPCNN yielded 96.52% accuracy with 96.49% F1 score in classification while performing encryption and decryption within 2.87 ms and 2.35 ms, respectively.]]></description>
            <category>ARTICLE</category>
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        <item>
            <title><![CDATA[Design and Evaluation of a DeSci-Driven Lightweight Hybrid Blockchain Framework for Privacy-Preserving and Incentive-Aware Decentralized Healthcare Research Infrastructure]]></title>
            <link>https://reference-global.com/article/10.2478/ias-2026-0018</link>
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            <pubDate>Fri, 07 Aug 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[The rapid digitization of healthcare has brought Electronic Health Records (EHRs) to the forefront of clinical data management; however, persistent challenges of centralized control, privacy breaches, absence of patient data ownership, and the inability to support decentralized scientific collaboration continue to impede scalable healthcare research ecosystems. Recent advances in Decentralized Science (DeSci) introduce a paradigm shift by leveraging blockchain, cryptographic primitives, and decentralized governance to enable transparent, trust-minimized, and collaborative biomedical research. This paper proposes a DeSci-driven lightweight hybrid blockchain framework designed to support privacy-preserving and incentive-aware decentralized healthcare research infrastructure. The framework integrates a permissioned blockchain with a lightweight hybrid PBFT–PoA consensus protocol, off-chain storage, and Zero-Knowledge Proof (ZKP)-based authentication to enable secure, privacy preserving data access without disclosing user identity. A tokenomics-based DAO governance layer is incorporated to support decentralized engagement, transparent policy enforcement, and incentive-driven research participation. The proposed system is evaluated through simulation under varying network conditions, with key performance metrics — latency, throughput, and computational cost — assessed across network sizes from 10 to 50 nodes. Simulation-based projections suggest that the proposed framework may achieve lower latency, higher throughput, and improved computational efficiency relative to literature-reported values for MedRec, FHIRChain, and HealthChain under the stated modeling assumptions; these comparisons are model-based and illustrative rather than measurements obtained from a controlled, identical-environment deployment. Beyond data management, the framework enables a DeSci-oriented research lifecycle encompassing decentralized data contribution, validation, and provenance tracking. The simulation-only nature of the current evaluation is explicitly acknowledged as a limitation, with a clear roadmap toward prototype-level implementation on Hyperledger Fabric or Ethereum as immediate future work.]]></description>
            <category>ARTICLE</category>
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        <item>
            <title><![CDATA[From YOLO Models to Embedded Deployment: A Blockchain-Enabled ANPR System on Jetson and Raspberry Pi]]></title>
            <link>https://reference-global.com/article/10.2478/ias-2026-0016</link>
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            <pubDate>Fri, 07 Aug 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[This paper presents a secure blockchain-enabled Automatic Number Plate Recognition (ANPR) framework for Intelligent Transportation Systems (ITS), combining lightweight YOLO-based license plate detection, OCR-based character recognition, and Ethereum smart contracts for tamper-resistant vehicle data management. The study investigates the inference performance of recent object detection architectures, namely YOLOv11, YOLOv12, and YOLO26, on Raspberry Pi 5 and NVIDIA Jetson Nano edge devices. Furthermore, optimizations for ONNX and TensorRT are explored to enhance inference efficiency on embedded hardware. Experimental results demonstrate complementary strengths among the evaluated models. YOLOv11n achieved the best overall detection performance, obtaining the highest recall (0.945), F1-score (0.9645), and mAP@0.5:0.95 (0.692), while YOLOv11n and YOLOv12n achieved identical precision (0.985) and mAP@0.5 (0.963). YOLO26 demonstrates the best deployment efficiency, reducing inference latency to 33.4 ms on Raspberry Pi (ONNX) and 38.9 ms on Jetson Nano, making it suitable for edge applications. Model optimization significantly accelerates inference, reducing YOLOv11 latency on Raspberry Pi from 98.2 ms to 38.3 ms after ONNX conversion. The results demonstrate that the proposed framework effectively balances detection accuracy, computational efficiency, and data security, making it a promising solution for smart parking and next-generation ITS applications.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Graph Attention-Based Engineering Framework for Aspect-Level Sentiment-Driven Recommendation Using BiLSTM–CRF Hybrid Model]]></title>
            <link>https://reference-global.com/article/10.2478/ias-2026-0013</link>
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            <pubDate>Thu, 30 Jul 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[The rapid expansion of user-generated content in the form of reviews and ratings on e-commerce sites calls for sophisticated recommendation algorithms that can comprehend both the sentiments expressed in text and numeric evaluations. Existing recommendation strategies based on collaborative filtering and content-based filtering have been shown to neglect the meaning conveyed in user reviews, resulting in poor performance and interpretability. In this study, we introduce a novel recommendation algorithm that combines Aspect-Based Sentiment Analysis (ABSA) and a Graph Attention Network (GAT) to improve the recommendation quality. Initially, user reviews are processed by a Bidirectional Long Short-Term Memory (BiLSTM) model supplemented with an attention mechanism to obtain detailed sentiments toward aspects and generate an implied rating score. The difference between the implicit and explicit rating scores is then used to estimate sentiment-ratings consistency. Finally, a GAT is introduced to explore intricate relationships among users, items, and sentiment dimensions by weighting useful relations and ignoring noises through attention coefficients. The proposed approach is based on the aggregation of sentiments, scores, and embeddings. Experimental results on multiple benchmark data sets show that the proposed method for hybrid recommendations outperforms other approaches in terms of precision, and recall. Moreover, the proposed framework also allows for improved interpretability because of the aspect-level sentiment representation.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Performance Evaluation of Mobile RPL-Based IoT Networks under Sinkhole Attacks]]></title>
            <link>https://reference-global.com/article/10.2478/ias-2026-0015</link>
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            <pubDate>Thu, 30 Jul 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[The rapid expansion of Internet of Things (IoT) deployments has increased reliance on Low-power and Lossy Networks (LLNs), where the Routing Protocol for LLNs (RPL) is widely adopted. However, RPL networks are vulnerable to sinkhole attacks that manipulate routing information to disrupt communication. This study evaluates the performance of mobile RPL-based IoT networks under such attacks, considering both mobility and attacker presence. Key metrics, including packet delivery ratio, inter-packet time, power consumption, and isolated nodes, are analyzed using Cooja simulations. Results indicate that mobility intensifies attack impact, causing instability and performance degradation, emphasizing the need for mobility-aware security mechanisms.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Attention-Based Fusion of EEG Spectrograms and Scalograms for Schizophrenia Detection Using Vision Transformers]]></title>
            <link>https://reference-global.com/article/10.2478/ias-2026-0014</link>
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            <pubDate>Thu, 30 Jul 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[Schizophrenia is a neuropsychiatric disorder that affects brain activity, making timely and reliable diagnosis challenging. Although electroencephalography (EEG) provides a non-invasive and cost-effective diagnostic modality, its non-stationary nature and complex temporal–spectral characteristics limit the effectiveness of conventional machine learning and deep learning approaches, which often rely on a single time–frequency representation or simple feature fusion strategies. To address these limitations, this study proposes an attention-based fusion framework that integrates complementary time–frequency representations for EEG-based schizophrenia detection. Spectrograms and scalograms generated using the Short-Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT) capture global spectral information and localized temporal–frequency characteristics, respectively. These representations are processed by a Vision Transformer (ViT), while a bidirectional cross-attention module enables effective interaction and fusion of complementary features before global feature learning. The proposed framework achieved average classification accuracies of 98.34 ± 0.21% and 98.68 ± 0.15% on Dataset 1 and Dataset 2, respectively. Comparative experiments demonstrate that the proposed framework consistently improves classification performance over methods based on individual time–frequency representations and conventional feature fusion approaches, indicating that the proposed attention-guided fusion strategy effectively exploits complementary EEG information for schizophrenia detection.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Role of System Readiness Level (SRL) in Integration, Interoperability, and Standardization for System Security]]></title>
            <link>https://reference-global.com/article/10.2478/ias-2026-0010</link>
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            <pubDate>Wed, 22 Jul 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[This abstract summarizes the application of System Readiness Level (SRL) as a governing metric for evaluating the maturity of System Security through the lenses of integration, interoperability, and standardization. In the development of complex, multi-domain systems, individual component maturity (Technology Readiness Level) often fails to account for the security vulnerabilities that emerge during subsystem convergence. This paper/discussion examines the System Readiness Level (SRL)—a composite index derived from the interaction between Technology Readiness Levels (TRL) and Integration Readiness Levels (IRL)—as a critical framework for System Security. By quantifying the maturity of secure interfaces, SRL provides a systematic method for ensuring that security controls are not only technically sound but also integrated into the system architecture without creating new attack vectors. Furthermore, the framework enhances interoperability by standardizing the criteria for secure data exchange across disparate platforms. Through the adoption of SRL, organizations can transition from fragmented security assessments to a standardized, holistic maturity model, ultimately reducing acquisition risk and ensuring that the final system is operationally ready to defend against evolving cyber threats.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Detection of False Data Injection Attacks (FDIA) Targeting Distribution Transformers in Smart Grids Using Hybrid Artificial Intelligence Methods]]></title>
            <link>https://reference-global.com/article/10.2478/ias-2026-0011</link>
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            <pubDate>Wed, 22 Jul 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[Communication technologies in modern power systems have rendered energy grids vulnerable to False Data Injection Attacks (FDIA). This study evaluates FDIA detection on the IEEE 118-bus test system using pandapower. A synthetic dataset of 2000 samples was partitioned into 75%/25% training/test splits. Three models — LSTM networks, Isolation Forest and One-Class SVM — were comparatively assessed. LSTM achieved the highest performance with 97.0% recall, F1-score of 0.965 and AUC of 0.987, outperforming Isolation Forest (93.0%) and One-Class SVM (90.0%). These results confirm that deep learning approaches are highly effective for smart grid cybersecurity and compatible with IEC 61850-based infrastructures.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Zero Trust Frameworks for Misinformation Detection in Industry 5.0]]></title>
            <link>https://reference-global.com/article/10.2478/ias-2026-0012</link>
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            <pubDate>Wed, 22 Jul 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[This paper examines the application of the Generative Pre-trained Transformer (GPT) model for misinformation detection as part of Zero-Trust security models in Industry 5.0. As the transition to Industry 5.0 gains momentum, characterized by heightened interconnectedness and human-machine interaction, the necessity for rigorous security measures becomes critical. Disinformation poses significant threats, potentially undermining operational efficiency and safety, while causing substantial financial and reputational damage. To address these risks, we propose leveraging GPT, a natural language processing tool, to develop an effective misinformation detection system that enhances data integrity and trust in human-machine interactions within Industry 5.0. This paper discusses integrating a GPT-based misinformation detection system with existing security measures in Industry 5.0, showcasing its potential to strengthen a Zero-Trust security framework. We provide a detailed architectural specification of how the GPT-3 classifier integrates as a Policy Decision Point (PDP) within a Zero Trust Network Access (ZTNA) framework. Through case studies and simulations, we demonstrate the value of this machine learning model in protecting Industry 5.0 environments from misinformation-related threats. We also present inference latency analysis across GPT-3 variants to evaluate suitability for real-time industrial deployment. The research emphasizes the need for continuous vigilance and adaptable security strategies, highlighting the crucial role of GPT-based misinformation detection in maintaining a secure and reliable environment for human-machine interactions within the rapidly evolving context of Industry 5.0.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[From Awareness to Practice: Domain-Specific Information Security Awareness as a Predictor of Self-Reported Cybersecurity Practices among University Personnel]]></title>
            <link>https://reference-global.com/article/10.2478/ias-2026-0009</link>
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            <pubDate>Wed, 08 Jul 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[This study examined information security awareness as a predictor of self-reported cybersecurity practices among 160 personnel of a state university in the Philippines. Awareness was measured across six domains: password security, phishing, device security, data protection, security policy, and social engineering. Results showed moderate to high awareness and reported practices, with uneven domain patterns. Multiple regression indicated that awareness domains explained 55.8% of the variance in reported practices. Password security, phishing, and social engineering awareness were significant predictors, while security policy, data protection, and device security were not. However, these findings should be understood in the context of self-reported cybersecurity practices, which may differ from actual behavior.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Deep Learning for Blockchain Security: A Concise Survey, Taxonomy, and Open Problems]]></title>
            <link>https://reference-global.com/article/10.2478/ias-2026-0008</link>
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            <pubDate>Wed, 08 Jul 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[Blockchain is increasingly used in finance, healthcare, supply chains, and IoT, but it also faces growing security threats such as fraud, smart contract vulnerabilities, intrusions, and malware. Deep learning (DL) has emerged as a powerful tool to detect and mitigate these risks. This paper presents a structured survey of DL applications in blockchain security and proposes a taxonomy linking threat types, integration approaches (on-chain, off-chain, hybrid), DL models, data modalities, and deployment settings. We review recent advances across major security tasks and discuss key limitations and challenges. Finally, we highlight future research directions to improve robustness, efficiency, and practical deployment.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Towards Automated Identification of Block Cipher Structures Using Machine Learning]]></title>
            <link>https://reference-global.com/article/10.2478/ias-2026-0007</link>
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            <pubDate>Wed, 08 Jul 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[This paper focuses on the classification of block ciphers based on their structure, which is a crucial step in automated cryptanalysis. For this purpose, five machine learning algorithms, including Logistic Regression, Naive Bayes, k-Nearest Neighbour, Random Forest, and XGBoost, are applied to ciphertext data to differentiate between Substitution–Permutation Networks (SPN) and Feistel block cypher structures. A comprehensive experimental framework is developed using 16 block ciphers, including AES, DES, Blowfish, Twofish, and Camelia, etc evaluated across two encryption modes (ECB and CBC) with 5-fold cross validation under four different scenarios. To improve generalization and eliminate dataset bias, ciphertext datasets are generated using fixed key and multiple random keys and diverse plaintext sources, including text, source code, and binary data. A feature extraction pipeline based on byte-level statistics, compression characteristics, and higher-order histogram features is employed, followed by classification using five machine learning and one deep learning models. NIST SP800-22 suite tests were conducted on ciphertext dataset. Results confirmed that despite ciphertexts exhibit near-ideal entropy and pass standard randomness tests; however, small higher-order statistical deviations remain observable. Experimental results demonstrate that these weak statistical signals can be leveraged to achieve classification accuracies exceeding 90% for ciphertext, with performance increasing as ciphertext size increases. The classification accuracy is highest for ECB and decreases for CBC, consistent with increasing diffusion and inter-block dependency. Feature importance and ablation studies further reveal that statistical features provide the dominant discriminative signal. These findings highlight the presence of finite-sample statistical artifacts that can be detected using machine learning, and provide new insights into the intersection of statistical cryptanalysis and machine learning, with implications for automated cryptographic analysis.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Artificial Intelligence Enabled Digital Identity Systems for Secure and Transparent Governance: A Conceptual Framework]]></title>
            <link>https://reference-global.com/article/10.2478/ias-2026-0006</link>
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            <pubDate>Wed, 08 Jul 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[Digital identity systems are a foundational component of contemporary e-governance, enabling secure authentication, controlled access to public services, and digital interaction between citizens and government institutions. However, traditional identity and access management systems predominantly rely on static credentials and centralized architectures that are increasingly vulnerable to cyber threats, identity fraud, and data misuse. Recent advances in artificial intelligence (AI) present new opportunities to enhance digital identity systems through adaptive authentication, continuous monitoring, and real-time risk assessment. At the same time, the integration of AI into identity governance raises important concerns related to privacy, accountability, transparency, and public trust. This paper develops a comprehensive conceptual framework for AI-enabled digital identity systems aimed at supporting secure and transparent governance. Using an integrative synthesis of literature from digital identity management, artificial intelligence, cybersecurity, and public governance, the study proposes a four- dimensional governance-oriented framework encompassing technological, security and privacy, institutional and regulatory, and ethical and trust-related components. While these dimensions have been examined independently in prior research, this study contributes by systematically integrating them into a unified framework tailored to public-sector governance contexts. The proposed framework is intended to guide policymakers, system designers, and researchers in designing and evaluating trustworthy AI-enabled digital identity infrastructures. It also provides a theoretical foundation for future empirical validation across diverse regulatory and institutional environments. By emphasizing the interdependence of technological capability, governance mechanisms, and ethical safeguards, this study contributes to ongoing discussions on the responsible adoption of artificial intelligence in digital public-sector governance.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Adversarial Robust Reinforcement Learning for Secure and Regulation-Aware Internet of Things Systems]]></title>
            <link>https://reference-global.com/article/10.2478/ias-2026-0005</link>
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            <pubDate>Wed, 08 Jul 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[Reinforcement learning has emerged as a promising paradigm for adaptive Internet of Things (IoT) security due to its ability to optimize sequential defense decisions in dynamic environments. Yet, the vast majority of reinforcement learning–based IoT security solutions take benign learning conditions and impose regulatory imperatives as static post-processing constraints. You are aware that these assumptions allow adversarial exploitation of the learning loop with respect to state observations, reward feedback, and policy updates while still adhering to the rules. To address this limitation, in this paper, we propose an adversarially robust and regulation-aware reinforcement learning framework that explicitly models the learning process itself as a possible attack surface. By integrating bounded adversarial threat modeling and dynamic, compliance-aware policy optimization, the approach leverages uncertainty-aware reward shaping and constrained robust optimization to maintain policy stability and regulatory compliance. Experimental assessment over benchmark IoT intrusion detection datasets shows that the framework achieves up to 35% more stability in policy, reduces security degradation by more than 30% under adversarial conditions, and dramatically lowers compliance violation rates compared to classical architectures relying solely on reinforcement learning. These results emphasize how the security of the learning process itself is essential to the establishment of trust-based and regulation-compliant IoT security systems.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[An Overview of EDR Serviceability for Security Information and Event Management (SIEM)]]></title>
            <link>https://reference-global.com/article/10.2478/ias-2026-0004</link>
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            <pubDate>Wed, 08 Jul 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[This review paper focuses on and addresses Endpoint Detection and Response (EDR) tools, providing an overview of their purpose, functions, operations, services, and benefits within the cybersecurity landscape. Specifically, EDR solutions are designed to detect, prevent, investigate, and respond to advanced cyber threats that often bypass traditional antivirus programs. To achieve this, these tools continuously collect and analyze data in real time using behavioral analytics, artificial intelligence (AI), and machine learning (ML). This enables the identification of anomalous activities and sophisticated attack patterns, such as zero-day exploits and fileless malware. Furthermore, the integration of open-source tools strengthens an organization's security posture by enhancing service capabilities, scalability, reliability, and availability. The paper also discusses the evolution of EDR from standalone tools to integrated, interoperable, automated, and intelligence-driven platforms that utilize behavioral and predictive analysis to counter increasingly sophisticated threats. Such integration, in turn, enables faster decision-making while reducing code complexity, operational costs, and response times. Ultimately, the sustainability of open-source tools contributes to higher quality, improved performance, effective cost management, better decision-making, and reduced risk. In summary, this review synthesizes key developments and innovations in the EDR-SIEM domain, drawing from academic research, industry analysis, and real-world applications.]]></description>
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        <item>
            <title><![CDATA[Towards Trustworthy Procurement: A Lightweight and Secure Bidding System]]></title>
            <link>https://reference-global.com/article/10.2478/ias-2026-0002</link>
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            <pubDate>Mon, 15 Jun 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[A fair tendering system is essential in public administration to ensure the proper use of taxpayers’ money. The sealed-bid quotation method remains preferred for minor purchases due to its simplicity. However, it has more geographical restrictions and is more vulnerable to fraud than the e-tendering system. E-tendering methods are more technology-dependent and are used for major purchases. However, both approaches require quotation submission to the procurement host. Therefore, this research proposes a lightweight and secure bidding system that requires no quotation submission at the time of participation. Quotation disclosure is required at the time of bid opening. Until the bid opening event, the quotation remains in the respective bidder’s possession, and there is no risk of data leakage. Therefore, this system can be used to secure bidding in real-time.]]></description>
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