MACHINE LEARNING IN HACKING ATTEMPTS
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Abstract
Hacking cases were common, especially in the modern world. People continue to be victims of burglary. There has been extensive research into various types of hacking techniques. However, there are a few types of research done to show how to use machine learning to accomplish different types of illegal attacks. Experts have been able to deceive machines into all sorts of illegal attacks. This paper takes the research perspective of how machine learning provides success in illegal hacking efforts. The methods used in this study are mainly reviews of previous studies conducted for the same. Details are also collected based on the imagination present in the field. Various types of attack methods that use machine learning, such as simulation, service rejection, and data collection are discussed. The paper also includes defense strategies and recommendations on how to deal with hacking attempts involving machine learning.
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