Cyber Hacking Breaches Prediction and Detection using Machine Learning

Author(s)	T.Sathya, Theyjakshaya.DS, Vishnu Vardhini.AM
Country	India
Abstract	Predicting cyber-hacking breaches through ML (random forest classifier) is one of the newest technologies, and utilizing computer algorithms to identify and anticipate breaches have shown to be a difficult challenge. The primary focus of employing machine learning for breach detection and prediction is to make malware detection more rapid, scalable, and efficient than traditional systems that require human input. Websites that have the potential to launch a cyberattack can provide the information. Data breaches might end in identity theft, fraud, and other damages. According to the data, 70% of breaches have an impact on many firms. The analysis shows the probability of a breach in data. Security breaches are becoming more likely as a result of increasing use of computer applications and host and network security.
Keywords	Random Forest Classifier ,Html,cyber attack,ML
Field	Engineering
Published In	Volume 6, Issue 3, May-June 2024
Published On	2024-06-17
Cite This	Cyber Hacking Breaches Prediction and Detection using Machine Learning - T.Sathya, Theyjakshaya.DS, Vishnu Vardhini.AM - IJFMR Volume 6, Issue 3, May-June 2024. DOI 10.36948/ijfmr.2024.v06i03.22653
DOI	https://doi.org/10.36948/ijfmr.2024.v06i03.22653
Short DOI	https://doi.org/gt2b9k

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