Augmented Intelligence in Machine Learning for Cybersecurity: Enhancing Threat Detection and Human-Machine Collaboration

Analysts can combat sophisticated cyber-attacks thanks to enhanced intelligence in cyber security, which blends human experience with developments in machine learning and image processing. This cooperative strategy acknowledges the importance of both human discretion and technological intelligence in the safeguarding of digital assets. To improve threat detection and foster human-machine collaboration, the project investigates the application of augmented intelligence with machine learning and image processing techniques in cyber security. Machine learning approaches, such as threat and anomaly identification and feature extraction from images, automate analysis and offer insightful information. Additionally, human experts can employ augmented intelligence to evaluate and change automated results by drawing on their knowledge of threat detection and decision-making. To determine the impact of augmented intelligence on threat detection and response, this study studies real-world datasets, evaluating results in terms of accuracy, efficiency, and handling of complex threats. According to preliminary findings, augmented intelligence improves precision through human judgement and promotes productive collaboration through interactive interfaces. It focuses on the benefits of combining human involvement and machine-driven analysis in the case of complex threats. By focusing on human-machine collaboration and its potential to enhance hazard detection, this study also helps us understand augmented intelligence. Future cyber security systems that employ augmented intelligence to successfully fend off new attacks can benefit from the findings.