“The Role of AI in Cybersecurity A Case Study of Malware and Threat Detection”

Artificial intelligence (AI) techniques like machine learning and deep learning are increasinglybeing leveraged for malware detection and cyber threat analysis. This case study provides acomprehensive assessment of the current capabilities and limitations of using AI for malwaredetection. Quantitative experiments compare deep neural networks to traditional methods on amalware dataset, evaluating accuracy, robustness to evasion attacks, and explainability.Qualitative examinations of commercial solutions and interviews with experts elucidate real-worldpractices and challenges. Results indicate deep learning can improve malware detection oversignature-based approaches. However, adversarial evasions, interpretability constraints, and datadiversity issues remain as challenges. Ensuring rigorous validation, human-machine teaming, andcontinuous adaption will be critical for successful AI malware detection deployment. This researchaims to equip cybersecurity leaders with practical guidance and identify high-potential areasneeding future progress.

کلیدواژه ها:

malware detection ، artificial intelligence ، machine learning ، deep learning ، cybersecurity ، adversarial evasion ، explainable AI