AI-Powered Metaheuristic Algorithms: Enhancing Detection and Defense for Consumer Technology

Artificial intelligence (AI) systems have been increasingly faced with significant challenges and opportunities, particularly in the realm of security for consumer electronics technology. The adversarial defense (ADV) mechanism has more potential for addressing those issues. Some pertinent AI-enabled or metaheuristic schemes, such as MOPSO, MOGWO, and NSGA-II algorithms, have proven specific advantages but are lacking in providing significant accuracy prediction and robustness. By combining AI, machine learning (ML), and metaheuristic techniques, ADV enhances the detection and mitigation of dynamic security attacks such as intrusion detection or real-time malware detection, including adversarial attacks in consumer electronics appliances. This research emphasizes the combined use of AI/ML and metaheuristic algorithms, illustrating how ADV methods can employ various techniques to safeguard AI from adversarial attacks. Through detailed simulations and comparative analyses, this research highlights the effectiveness of advanced ADV methods in strengthening the resilience of consumer-based AI systems against new threats, thereby ensuring robust protection for electronic technologies and their data.