www.ijecs.in International Journal Of Engineering And Computer Science Volume 11 Issue 8 August 2022, Page No. 25585-25600 ISSN: 2319-7242 DOI: 10.18535/ijecs/v11i08.4694 Phani Durga Nanda Kishore Kommisetty, IJECS Volume 10 Issue 08 August, 2022 Page No.25574-25584 Page 25585 Transforming Cyber Defense: Anomaly Detection and Predictive Analytics for Automated Threat Response Phani Durga Nanda Kishore Kommisetty 1 , Bala Maruthi Subba Rao Kuppala 2 , Hussain Vali Buvvaji 3 1 Director of Information Technology, phanidurgakommisetty@yahoo.com 2 Support Escalation Engineer, balamaruthikuppala@yahoo.com 3 Sr Infrastructure Engineer, hussainvalibuvvaji@yahoo.com Abstract Currently, cyber defense remains a pre-eminently human-driven endeavor, lacking fundamental capabilities for comprehensive and timely detection, response, and prediction. Here, we present transformative concepts to mature cyber defense toward automated anomaly detection, prediction, and response. Our concepts treat the underlying problem at its most basic and essential level: violation of the predictability of correct actions and correct system and service performance, representing unintended relationships and change. We mathematically generalize prediction to explore relationships between dependencies, predict correct action sets, discern and anticipate both intended and unintended change, and mitigate the effects of correlated nested risk to enhance defense capabilities within and across organizations. These general attributes can also provide the principal knowledge and mechanisms essential for new generations of cyber defense and information assurance. Our concepts directly address immediate and long- term, broad and fundamental needs in defense and, we believe, will be studied indefinitely. The fundamental nature of these concepts leads to their broad applicability across scientific, engineering, and human endeavors, including social, economic, and political systems, where incomplete knowledge-supported decisions steadily increase untenable manipulation and control. These general attributes can also provide the principal knowledge and mechanisms essential for new generations of cyber defense and information assurance. Keywords:Transforming Cyber Defense, Industry 4.0, Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), Smart Manufacturing (SM),Computer Science, Data Science,Vehicle, Vehicle Reliability 1. Introduction The cyber defense game is currently human-centric, which requires considerable involvement of highly skilled security analysts and incident handlers. This condition creates a sizable manpower problem with increasing numbers of cybersecurity incidents and apparent high failure rates with today's point-in- time information sharing, network defense, and detection approaches. To address the evolving and broadening nature of cyber threats, there is a straightforward solution: towards fully autonomous cyber defense. The acquisition of cybersecurity tools is growing, enabled by machine learning and the rapid increase in the application of artificial intelligence. We characterize the methodological, practical, and cybersecurity challenges that, if overcome, will be needed to realize any net of AI.The AI-based tools possess a variety of techniques designed to handle current cyber threats, including anomaly (i.e., behavior-based) detection and predictive analytics to support automated threat response systems. Given suitable models, prediction capabilities are generally compatible with anomaly detection approaches, which can be considered