Resilient AI Systems: Robustness and Adversarial Defense in the Face of Cyber Threats - Navita, Navita; Srinivasan, S.; Nitin, Nitin (International Journal of Intelligent Systems and Applications in Engineering, 2024)
These days, artificial intelligence (AI) systems are used in many important areas. Because of this, it is very important to make these systems more resistant to online dangers. This paper looks at the complicated process of making AI systems stronger from two different angles: making them more resilient and adding good defenses against attacks from other AI systems. The first part of our research is focused on making AI systems more resilient, since these systems have to be able to handle many obstacles from both inside and outside the company. In this case, "robustness" means the system’s ability to keep working and being useful in a variety of difficult situations. The proposed method look into data enhancement, model diversity, and anomaly spotting to make AI models stronger in case something unexpected happens. We look at cutting edge methods to find, weaken, and stop hostile attacks on AI systems because we know that cyber dangers against them are getting smarter. Researchers are looking into whether adversarial training, ensemble methods, and anomaly detection algorithms can help protect AI systems from both known and unknown dangers. The goal of our study is to help make AI systems that are strong enough to handle the complex world of cyber dangers by combining two important factors: stability and hostile defense. As AI remains a key part of technological progress, protecting the integrity and dependability of these systems becomes not only a technological but also a social necessity. This is to protect against the risks and weaknesses that could appear in the complex digital environment.