Abstract
: Deepfake technology is still on the rise, bringing serious risks to cybersecurity; detection and prevention are the next critical levels. This paper examines how AI and ML are critical components of the deepfake approach to combating cyber threats within the cybersecurity system. The first goals are to assess the efficiency of AI/ML in deepfake detection, to survey today's methods, and to discuss possible improvements regarding their future employment. In a quantitative method supplemented by perceptions and impressions from cybersecurity professionals, this cross-sectional study utilizes a mixed-method questionnaire. Principal findings show that pre-trained AI and ML enhance the accuracy and efficiency of deepfake identification with some limitations in identifying highly elaborate and real-time deepfake content. The study concludes that AI/ML-based solutions are needed to enhance anti-deepfake cybersecurity tools as they provide tailored and reliable frameworks for addressing the issue. They prove the importance of further implementing AI and ML technologies in improving the resistance of today's cybersecurity systems.
Published Version
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