Robust and Secure AI in Cybersecurity: Detecting and Defending Against Adversarial Attacks

EasyChair Preprint no. 13463

Abstract

As artificial intelligence (AI) continues to play an increasingly vital role in cybersecurity, ensuring the robustness and security of AI models becomes paramount. Adversarial attacks, which exploit vulnerabilities in AI systems, pose a significant threat to the integrity and reliability of these models. This abstract explores the challenges associated with adversarial attacks and highlights the importance of developing robust and secure AI systems capable of detecting and defending against such attacks.

Adversarial attacks manipulate input data to deceive AI models, causing them to misclassify or make incorrect decisions. These attacks exploit inherent weaknesses in AI algorithms, such as deep neural networks, by introducing imperceptible perturbations to input data. Consequently, the AI system's decision-making process can be compromised, leading to potentially devastating consequences in cybersecurity applications.

To address these challenges, researchers are focusing on developing techniques to enhance the robustness of AI models against adversarial attacks. This involves leveraging various approaches, including adversarial training, defensive distillation, and ensemble methods, to improve the model's ability to accurately classify both legitimate and adversarial inputs. Additionally, advancements in explainable AI and interpretable machine learning contribute to the understanding and identification of potential vulnerabilities.

Defending against adversarial attacks also requires continuous monitoring and detection mechanisms. Techniques such as anomaly detection, behavior analysis, and real-time monitoring can aid in identifying and mitigating adversarial activity. Moreover, the integration of AI with other cybersecurity tools, such as intrusion detection systems and threat intelligence platforms, strengthens defense strategies by combining the strengths of different technologies.

Keyphrases: adversarial attacks, adversarial training, AI vulnerabilities, Cybersecurity, Robust AI, Secure AI

@Booklet{EasyChair:13463,
  author = {Edwin Frank and Harold Jonathan},
  title = {Robust and Secure AI in Cybersecurity: Detecting and Defending Against Adversarial Attacks},
  howpublished = {EasyChair Preprint no. 13463},

  year = {EasyChair, 2024}}