ASDS: Adaptive Stackelberg Defense Scheme for Cyber-Physical Intrusion Detection in Cloudified Transportation Systems

A GenAI-Driven Adaptive Cybersecurity Mesh for Real-Time Threat Detection in Intelligent Communication Systems

Intelligent communication systems integrating Internet of Things (IoT), cyber-physical infrastructures, edge computing, and next-generation communication protocols have significantly expanded the attack surface of modern digital ecosystems. Traditional intrusion detection systems (IDS) remain predominantly centralised, signature-based, and insufficiently adaptive to evolving multi-vector and zero-day threats. This paper proposes a GenAI-driven adaptive cybersecurity mesh architecture designed for real-time threat detection in distributed intelligent communication environments. The proposed framework integrates zero-trust security principles with a distributed mesh of edge security nodes coordinated through a policy orchestration layer. A generative AI-based adaptive threat modelling engine continuously synthesises contextual attack patterns and enhances anomaly detection across network, application, and behavioural layers. A formal cross-layer risk-scoring model fuses heterogeneous security signals to generate dynamic threat-confidence indices. The system is evaluated in a simulated intelligent communication environment comprising heterogeneous nodes, mixed legitimate traffic, and multiple attack scenarios, including DDoS, man-in-the-middle, and protocol-exploitation attacks. Experimental results demonstrate improved detection accuracy, reduced false positives, and lower response latency compared to baseline signature-based and centralised ML-based IDS models. The proposed architecture offers a scalable and adaptive security paradigm suitable for next-generation intelligent communication infrastructures.

Resilient control and Markov-enhanced hybrid multi-feature intrusion detection for cyber-physical wind farms under SCADA delays and coordinated cyber-attacks

A Deception-Based Intrusion Prevention Framework for Proactive Network Security Using Behavioral Threat Analysis

The rapid expansion of networked systems has led to an increase in sophisticated cyber threats that frequently bypass traditional security mechanisms. Conventional defenses largely rely on signature-based or rule-based techniques, which are limited in their ability to detect unknown or advanced attacks. To address these challenges, this paper proposes a Deceptive Intrusion Prevention System (DIPS) that transitions network security from a reactive model to a proactive, intelligence-driven approach. The proposed architecture employs strategically deployed decoy resources and deceptive information to divert attackers away from critical assets while monitoring their behavior within a controlled environment. The framework combines deception, behavioral analysis, and automated mitigation within a unified intrusion prevention architecture. By analyzing attacker interactions with deceptive components, the system accurately identifies malicious activity and enables real-time response actions such as isolation and blocking. Experimental evaluation conducted in a controlled network environment demonstrates that the proposed approach improves detection accuracy, reduces false positives, and enhances overall system resilience. The results further show that deception-based intrusion prevention effectively delays attackers and generates actionable threat intelligence, strengthening proactive network defense.

Intrusion detection systems for the internet of vehicles: a systematic survey of techniques and challenges across CAN bus and vehicular networks

This paper presents a comprehensive and systematic review of deep learning techniques applied to cyber intrusion detection within IoV systems, conducted in accordance with the PRISMA framework across 83 selected studies published between 2020 and 2025.

AI-Driven Cyber Defense Systems Using Real-Time Analytics

The rapid digitization of critical infrastructure, businesses, and government services has expanded the cyber-attack surface, making traditional security mechanisms increasingly ineffective. AI-based cyber defense systems powered by real-time analytics provide a proactive and adaptive approach to cybersecurity by integrating machine learning, deep learning, and intelligent threat detection techniques. This study examines the architecture, analytical frameworks, and operational processes of AI-driven cyber defense solutions capable of detecting known and unknown threats, including zero-day attacks and advanced persistent threats (APTs). The proposed framework incorporates continuous monitoring, streaming analytics, anomaly detection, behavioral analysis, and automated response mechanisms. Performance is evaluated using metrics such as detection accuracy, false positive rate, response time, and scalability. The findings indicate that AI-powered cyber defense significantly enhances threat detection, reduces response time, and improves overall cyber resilience compared to traditional security models, highlighting its critical role in next-generation cybersecurity infrastructures.

Enhancing Enterprise Cloud Security with Intelligent Intrusion Detection Systems: A Machine Learning Approach

With the widespread adoption of cloud computing, securing enterprise networks against cyber threats has become increasingly important. Cloud environments are highly dynamic and constantly changing, making them susceptible to sophisticated cyberattacks that traditional Intrusion Detection Systems (IDS) often fail to detect. This study focuses on Intelligent Intrusion Detection Systems (IIDS) and their critical role in strengthening cloud security. Unlike conventional signature-based IDS that rely on fixed attack patterns, IIDS employ advanced Machine Learning (ML) and Artificial Intelligence (AI) techniques including deep learning, decision trees, and ensemble models to identify both known and emerging threats with greater accuracy. The paper proposes an integrated framework that combines real-time anomaly detection with automated response capabilities for cloud networks. Key architectural elements of IIDS are examined, alongside major deployment challenges such as scalability, false-positive rates, and computational requirements. Additionally, practical case studies and performance evaluations illustrate how IIDS enhance threat detection by improving accuracy, adaptability, and efficiency. Finally, the paper outlines future research directions to further advance IIDS capabilities and address the evolving security needs of modern cloud infrastructures.