• Abhishek R Joshi
• Aniruddh Deshpande
• Veeresh H M
• Vinuta H
• V.K.Parvati

The ubiquitous spread of digital technologies makes cyber-attacks one of the major risks in terms of individuals, organizations and even countries. An ability to predict cyberattacks before they occur is an important factor for strengthening cybersecurity systems and minimizing possible negative effects. Machine learning models have lately been recognized as a promising tool for forecasting of cyberattacks because of their ability to break down large volumes of data and reveal patterns that signify harmful activities. This paper gives an overall view of the most recent approaches in cyberattack prediction based on machine learning algorithms. Initially, we go through types of cyberattacks and their significance, indicating that prevention should be the key factor. In addition, we present supervised, unsupervised, and semi-supervised approaches as the algorithms frequently applied in cyberattack prediction. We dive into the characteristics and datasets that are used during the training of these models and elaborate on the need for feature selection and data improvement.

Cyber Security, Machine learning, Data analysis, Cyberattack, Anomaly detection

"CYBER ATTACK PREDICTION USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 3, page no.k402-k405, March-2024, Available :http://www.jetir.org/papers/JETIR2403A51.pdf

"CYBER ATTACK PREDICTION USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 3, page no. ppk402-k405, March-2024, Available at : http://www.jetir.org/papers/JETIR2403A51.pdf