This collection of major projects on cyber security provides innovative ideas for final year BTech, MTech, and PhD students. It offers a comprehensive range of topics to enhance understanding and address current challenges in the field of cyber security.
This paper explores attack and defense methods in cyber-physical power systems (CPPS), detailing security strategies, analyzing attack modes, and discussing future advancements to ensure robust, safe, and reliable energy systems.
This study proposes a deep learning-based model for detecting cyber-attacks in smart power systems using data from phasor measurement units. The model achieved a 93.6% detection rate and 93.91% accuracy, outperforming existing methods.
This paper assesses the impact of cyberattacks on wind farm energy management systems, modeling attack scenarios and their effect on power system reliability, showing that successful cyberattacks reduce overall system dependability.
This paper analyzes the reliability of electric power systems, focusing on cybersecurity threats to SCADA/EMS systems. It uses Bayesian Networks and simulation methods to assess vulnerabilities, attack paths, and the impact on system dependability.
This study proposes a deep learning model to detect zero-day controller hijacking attacks on power grids, achieving over 99.9% accuracy in identifying anomalies with minimal false positives using regular operational data.