Enhancing Cybersecurity: A Deep Dive into Augmented Intelligence Through Machine Learning and Image Processing

Abstract

In cybersecurity, the potential of Machine Learning (ML) is widely acknowledged, yet the evolving landscape of cyber threats necessitates more dynamic and responsive strategies. This paper explores the integration of augmented intelligence with machine learning and image processing to elevate threat detection capabilities and foster effective human-machine collaboration in cybersecurity. We aim to investigate the synergistic potential of combining augmented intelligence with machine learning and image processing. Our approach involves a fusion of human expertise and Artificial Intelligence (AI) capabilities for providing AI driven insights under human oversight. Additionally, we delve into the role of image processing as a core component of augmented intelligence in broadening the cybersecurity domain. The integration of image processing facilitates the analysis of network traffic through visualizations, the detection of image based threats, and improved situational awareness. Coupled with machine learning, this methodology empowers cybersecurity systems to effectively detect anomalies, interpret complex network structures, and rapidly identify potential threats. The paper presents empirical evidence supporting the efficacy of this integrated approach.