The development of the Internet of Things (IoT) has been impressive, fostering connectivity for applications while exposing new and growing security issues such as scalability issues. Such concerns are not considered hard by traditional security models since devices overload the current systems, and most technologies are only reactive rather than proactively changing results among IoT devices. The framework suggested in this paper is composed of a three-layer solution concept based on AI-IDS, Blockchain, and Edge Computing. Threats are countered with machine learning triggers, while blockchain is implemented to improve data flow accuracy in IoT systems. The edge computing issue considers that data processing surrounds IoT devices, which can enhance resources and effectiveness in poor network scenarios. The study, therefore, uses optimization strategies for enhancing system performance, game theory in representing the relationship between IoT objects and security personnel and differential equations for simulating dynamic behaviors of the IoT system under attack. It is then compared to conventional passive models where the improvements in scale, speed, and response time are demonstrated. The proposed approach can be considered a self-contained, self-learning, fundamentally scalable approach to addressing emerging and continually metamorphosing IoT cyber threats by improving the security and steadiness of IoT networks, particularly in restrained contexts.

This article is focused on the analysis of the increasing risks of hostile cyber operations targeting AI and their relevance to cybersecurity. The study proposes a novel classifying system of adversarial ML attacks based on their aims, the knowledge level of the attacker, the degree of specialization concerning the attack target, and the frequency of the attack. It shows that the same kind of attacks are possible during both the training and inference phases and explains how small changes to data can affect ML systems. Examples like healthcare model theft and data poisoning during the recent U.S. elections highlight the serious threats to public confidence and data integrity. Nonetheless, it also suggests a strong defense framework that incorporates defense architecture with the new ideas of adversarial training, game-theoretic models, robust learning, and adversarial detection. The effectiveness of adversarial training was demonstrated by the notable 75% performance retention it attained against gradient-based assaults. To improve the system's reliability and protect it from adversarial threats, the research employs the findings of AI and cybersecurity domains. It is highly valuable for the progress of defense techniques for AI in life-critical applications and provides essential contributions to the protection of essential structures.  

The rapid expansion of Internet of Things (IoT) ecosystems has revolutionized industries by enabling real-time data exchange and interconnected operations. However, this growth presents significant security challenges, including scalability, resource constraints, and the need for real-time adaptability to evolving cyber threats. To address these issues, this study proposes an innovative, AI-driven framework that integrates lightweight intrusion detection systems (IDS), blockchain-based authentication, and edge computing. This qualitative research synthesizes peer-reviewed literature to identify existing gaps and design a multi-layered security solution tailored for resource-constrained IoT environments. The proposed framework enhances scalability by leveraging decentralized blockchain systems and edge computing for distributed data processing. Lightweight AI algorithms are employed to optimize resource efficiency and ensure real-time adaptability. Analytical comparisons with traditional security models demonstrate the framework's superiority in mitigating IoT vulnerabilities while maintaining computational feasibility. This study contributes to the theoretical and practical understanding of IoT security, offering a scalable, resource-efficient, and adaptive solution for emerging threats.

This work proposes an ethical artificial intelligence optimization framework for cybersecurity tasks aiming at maximizing robustness, detection efficiency, minimizing computational complexity, and being compliant with ethics principles (fairness, transparency, etc.). Focusing on fast convergence on the one hand and a diverse solution search on the other, the framework employs metaheuristic optimization approaches like PSO and GA. We test this framework with real-world cyberattacks, for example, CICIDS and UNSW-NB15 datasets, to get results signifying adaptability to various attacks, with average F1-scores of 0.92 due to PSO and a high R(w) = 0.18 robustness metric via GA. Its explainability mechanism with SHAP and LIME explains the model predictions showing that features like packet size and connection durations were pivotal in impacting the model output. The learned adaptive configurations of optimization parameters (α, β, γ) make the framework customizable for diverse cybersecurity applications and accommodate tradeoffs between detection performance and adversarial robustness. Additionally, the framework is lightweight, allowing scalability to large datasets and high-dimensional use cases. Privacy-preserving approaches such as Federated Learning can also be incorporated into this framework, enabling its application to real-time cybersecurity attacks alongside hybrid optimization techniques. Such a framework will enable the development of secure, efficient, and ethically robust AI systems in sectors where AI must address the 21st-century security challenges.

Today, the challenges and opportunities that are now inherent in the development of generative artificial intelligence (GAI) for cybersecurity are evolving rapidly. As such, this article examines the proactive capabilities of GAI, with a focus on how it has revolutionized the concept of cyber threats and the approaches to threat intelligence. It investigates the predictive and adaptive potential of GAI to combat sophisticated attack vectors, zero-day vulnerabilities, and automated threat modeling. According to studies, this research highlights that GAI can predict attack patterns with 87% accuracy and detect zero-day vulnerabilities with 80% precision. In addition, GAI-based intrusion detection systems (IDS) exhibit high detection rates (98% for known threats and 92% for unknown threats) at low false positive rates. This data shows that integrating GAI into cybersecurity enables organizations to discover vulnerabilities prior to exploitation, simulate realistic attack scenarios, and automate threat reactions. However, as with GAI in cybersecurity, there are significant. ethical and operational risks of GAI in cybersecurity, including the capacity to enable adversaries and magnify existing vulnerabilities that need close regulatory oversight. Organizations leverage their predictive power to anticipate and proactively secure their digital infrastructure from emerging threats, moving from an earlier reactive to a more anticipatory security posture. In the end, the findings from this research point to the need to strategically leverage GAI to strengthen cybersecurity strategies by creating fortified defenses in an ever more complicated digital environment.