Cyber Hacking Breaches Prediction Using Machine Learning Techniques
Keywords:
Cyber Hacking, Machine Learning, breach, deception attackAbstract
Cyber-physical systems have achieved important advancements in various active tenders, thanks to the seamless addition of physical methods, computational properties, and communication skills. These systems are vulnerable to cyberattacks, which pose a major threat. Unlike accidental faults, cyber-attacks are intelligent and stealthy, and can compromise the system's integrity. Deception attacks, in particular, inject false data from sensors or controllers, corrupt data, or introduce misinformation into the system. If left undetected, these attacks can disrupt or disable system performance. Therefore, it is crucial to develop algorithms that can identify and detect these attacks. Given the vast amount of diverse data generated by these systems at high speed, machine learning algorithms are essential for facilitating data analysis, evaluation, and identifying hidden patterns.
References
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Articles in this journal are licensed under the Creative Commons Attribution-NonCommercial 4.0 International License. This license permits others to copy, distribute, and adapt the work, provided it is for non-commercial purposes, and the original author and source are properly credited.
