5G-Advanced (3GPP Release 18) architectural changes include multi-access edge computing (MEC) architectural changes, network automation, and non-public networks (NPNs). It is important to note that even though these advancements provide substantial performance advantages, they destroy fixed-perimeter security models, providing a distributed attack surface. The use of current security assessment strategies, which are usually non-fluid and isolated, is inadequate to offer the required runtime security health assurance needed in such fluid environments. This study presents a new security assurance framework (SAF) that would be used to provide ongoing evidence-based protection on core, edge, and private network domains. This framework employs a four-layer architecture, including monitoring, analytics (LM), policy engine, and enforcement, to convert security periodically audited to a dynamic threat-control-metric evidence chain. A 96% attack detection rate and a 99.8% reduction in response time (with a mean of 20.1 s) are proven by validation on an emulated 5G-Advanced testbed (approximating Release 18 features using Open5GS (v2.7.2 Rel-17, community developed, Seoul, Republic of Korea and custom extensions) based on a design science research (DSR) paradigm. Although the overhead (13% CPU, 21.4% memory) is manageable, the findings prove that all-time, multi-domain assurance is crucial to the healthy functioning of 5G-Advanced and is a key roadmap to autonomous 6G security.
The increasing connectivity of automotive systems through Vehicle-to-Everything (V2X) communication and Mobile Ad Hoc Networks (MANETs) has created new vulnerabilities to Distributed Denial of Service (DDoS) attacks, threatening the availability of safety-critical vehicle communications and infrastructure services. This research addresses the challenge of protecting MANET-based automotive infrastructure by developing an integrated security architecture combining Network Detection and Response (NDR), Security Information and Event Management (SIEM), and Security Orchestration, Automation, and Response (SOAR) capabilities. Risk analysis was conducted using the NIST Cybersecurity Framework 2.0, mapping security controls across its six core functions. A laboratory proof of concept validated the architecture using CYBERQUEST (SIEM) and NETALERT (NDR) platforms to detect and automatically mitigate a simulated volumetric DDoS attack against a static network node. The integrated detection chain successfully identified abnormal connection volumes, correlated alerts across multiple sources, and executed automated blacklisting responses without human intervention. The results demonstrate that commercially available security platforms can be effectively adapted for MANET environments when properly integrated, providing rapid automated response capabilities aligned with European regulatory requirements including the NIS2 Directive and UNECE Regulation No. 155.
C. Ene, Marius Minea· European Conference on Artif...· 0 citations
Results prove the combination of adaptive intelligence, secure virtualization, and dynamic policy enforcement boosts cybersecurity defenses in unique ways for programmable SDN and DCN infrastructures.
Hasan Alkahtani· JOIV: International Journal...· 0 citations
An AI-assisted, cross-layer security orchestration framework that integrates epoch-wise telemetry with ML-based risk estimation and formalizes mitigation as a Constrained Markov Decision Process (CMDP), and empirical evidence that adaptive mitigation can reduce security risk without sacrificing service guarantees is provided.
F. Philip-Kpae, A. Imoize, K. C. Okafor et al.· E3S Web of Conferences· 0 citations
Working baseline levels of capability are provided with respect to current LLM-based solutions in 6G mission-critical and public safety contexts, and specific research directions to advance LLM-driven cybersecurity toward robust, adaptable, explainable, and life-safety-aware solutions are mapped out.
Siva Sai, Bhuvan Arora, Vineet Suri et al.· IEEE Open Journal of the Com...· 1 citation
The proposed maturity model comprising Fragmented, Instrumented, Correlated, Automated, Automated, and Adaptive stages provides organizations with a practical roadmap for assessing current capabilities and systematically advancing toward intelligent, self-optimizing security operations.
Lakshmi Kiran Meesala· International Journal of Art...· 0 citations
Computer-based testing (CBT) platforms have transformed education and certification by enabling scalable, efficient, and accessible examinations. However, these systems face significant cybersecurity risks, including unauthorized access, denial-of-service (DoS) attacks, and digital cheating, which threaten fairness and reliability. This study proposes a network-based security information system (NBSIS) designed specifically for CBT environments. The framework integrates layered defense, including pfSense firewalls (FW), Snort intrusion detection, Splunk security information and event management (SIEM), and artificial intelligence (AI)-powered analytics, into a unified architecture. A human-centered dashboard ensures usability for non-technical exam administrators, providing real-time alerts and intuitive controls. Validation through simulated attack scenarios demonstrated strong resilience, with high detection accuracy, reduced false positives, and rapid response times. Comparative analysis against intrusion detection system (IDS)-only and SIEM-only systems confirmed superior performance. The findings highlight NBSIS as a robust, scalable, and adaptive solution that safeguards exam integrity while remaining practical for diverse organizational contexts. This research contributes to computer science by advancing secure architecture, applying AI-driven anomaly detection, and integrating human-computer interaction principles into cybersecurity for education.