Abstract: This study explores how machine learning can be used to anticipate cybercrimes, with an emphasis on detecting attack techniques and possible offenders. The dataset used includes comprehensive records of criminal activity, including the characteristics of criminals and the methods used in attacks. The study compares these algorithms' performance in order to ascertain how accurate they are at forecasting the kind of cyberattack as well as the characteristics of the attacker.

The study also looks at the potential effects of a number of variables on the forecasts, including gender, income level, work position, and the seriousness of the crime. Additionally, it looks into how feature selection and preprocessing methods can improve model performance. This work's ultimate objective is to assist law enforcement organizations in improving their capacity to foresee and stop cyberattacks.

Keywords: Cyber Attack Prediction using Machine Learning involves cybersecurity concepts such as phishing, malware detection, data breaches, and intrusion detection systems (IDS). It utilizes machine learning techniques like logistic regression, random forests, SVM, and deep neural networks to analyze network traffic and detect anomalies. Key processes include feature extraction, dimensionality reduction (PCA, t-SNE), and data augmentation to enhance model accuracy. Performance evaluation metrics such as precision, recall, F1- score, and cross-validation are crucial for ensuring reliable threat detection. The project also involves tools and techniques like supervised learning, hyperparameter tuning, and behavior-based threat intelligence to improve predictive capabilities.

| DOI: 10.17148/IARJSET.2025.12211