International Journal of Digital Technology and Network Security System (e-ISSN: 3108-3307)

Contemporary Cybersecurity Challenges: Threat Landscape, Attack Vectors, and Mitigation Strategies

2026-09-24T07:26:10+00:00

Cybersecurity has become a core requirement for organisations and individuals that depend on interconnected information systems, cloud services, mobile devices, and Internet of Things (IoT) platforms. This review examines the contemporary cybersecurity landscape by organising major attack vectors, affected technologies, and practical mitigation measures into a unified discussion. The paper covers malware, phishing and social engineering, denial-of-service attacks, zero-day exploitation, man-in-the-middle attacks, ransomware, IoT risks, mobile-banking malware, and the growing dual-use implications of artificial intelligence. It also considers how cloud computing, web applications, mobile networks, encryption, and IPv6 alter the security boundary of modern digital environments. Recent standards and threat reports show that vulnerability exploitation, credential abuse, ransomware, availability attacks, supply-chain exposure, and identity compromise remain significant concerns, while AI is increasing both defensive capability and adversarial scale. The review therefore emphasises layered protection rather than reliance on a single security product. Recommended measures include strong identity controls, multifactor authentication, secure configuration, timely patching, network segmentation, encryption, continuous monitoring, resilient backups, incident-response planning, user awareness, and secure-by-design development. The paper concludes that effective cybersecurity requires coordinated governance, technology, people, and recovery capabilities, supported by continuous risk assessment and adaptation to emerging threats.

A User Priority Determination Method and A Load-control Algorithm Based on Web Access History During Network Load

2026-08-10T11:15:37+00:00

Today, web services are rapidly growing and are being used by many users. This leads to network overload. To avoid overload, techniques for efficient load balancing and server clustering have been studied, and they are being widely used. While a common web service is running, it can occasionally be seen that network overload occurs due to access from many users. To avoid this occasional network overload, expanding computer resources to use normal network services results in the waste of computer resources and operational costs. User priority is determined according to the importance of web service, and there is a problem of limiting the access of low-priority users and providing stable web service to high-priority users at the time of the occasional network overload. Hence, a method was proposed to determine user priority based on web history during network overload under limited computer resources and to stabilize network services for high-priority users by blocking the access of low-priority users.

Optimized Security and Performance for Misinformation Detection in Distributed Caching Systems

2026-09-29T10:41:17+00:00

Distributed caching reduces content-delivery latency and network traffic, but cached objects can also accelerate the dissemination of manipulated or misleading information. This paper proposes a lightweight, multi-layer framework that combines probabilistic pre-screening, cryptographic integrity verification, and trust-aware cache retention. A Bloom filter is first used at the cache edge to rapidly identify objects associated with known misinformation indicators. Candidate objects then undergo SHA-256 verification against trusted reference hashes, separating fast screening from stronger integrity validation. Finally, a reputation-based policy assigns a time-to-live (TTL) value according to the reputation score of the content source, allowing trusted content to remain cached longer while reducing the persistence of content from less trusted sources. A simulation study uses 1,000 fixed-size digital objects with misinformation loads of 5%, 10%, and 15% and varies cache size, Bloom filter size, number of hash functions, and source reputation. Across the evaluated configurations, the analytical model reports detection accuracy from 99.672% to 99.949%, a modeled SHA-256 processing latency of 10.24 ms for a 1,024-byte object, and cache hit rates from 78% to 86%. The results indicate that combining complementary lightweight mechanisms can provide a practical security-performance trade-off for latency-sensitive distributed caching. The study also identifies real-time cache invalidation and adaptive reputation learning as important directions for future validation.

Generative AI for Automated Medical Report Generation Using Multimodal Clinical Data

2026-09-09T09:28:45+00:00

The fast rate of Electronic Health Records (EHRs), medical imaging, and clinical documentation have greatly led to the administrative burden on healthcare professionals, which leaves little time to attend to patients directly. Automated creation of medical reports based on Generative Artificial Intelligence (GenAI) has become one of the potential solutions that can enhance the efficiency and consistency of documentation. Nevertheless, the current methods have the shortcomings of only single-modality processing, the absence of domain-specific knowledge, and the possibility of producing clinically inaccurate or hallucinated information. The paper proposes a new multimodal Generative AI architecture that combines structured clinical data, medical images, and physician notes to produce accurate, context-specific medical reports. The suggested system uses a modality-specific encoder, such as a Vision Transformer, to analyze medical images, a ClinicalBERT-based model to interpret text, and an attention-based fusion mechanism to capture a global picture of the patient. A domain-adapted large language model is additionally boosted with clinical knowledge grounded in standardized medical ontologies. To enhance stability, a reinforcement-oriented medical validation layer is proposed that can be used to assess the factual consistency and punish clinically incorrect predictions, which would decrease hallucinations and enhance diagnostic consistency. The proposed approach has proven superior to the currently available baseline methods in linguistic quality, clinical accuracy, and report completeness, while requiring much less time during the documentation phase. Explainability is also used in the system, which connects the generated content with supporting clinical evidence. The suggested framework is a scalable, trustworthy, and privacy-conscious approach to smart clinical documentation that can benefit radiology reporting, discharge summary creation, and real-time clinical decision support. This study is relevant to the creation of reliable and human-friendly Generative AI systems to be used in the next-generation healthcare app.