Cybercrime Detection and Prevention

Cybercrime has emerged as a serious and growing threat to individuals, organizations, and governments worldwide. It includes a wide range of malicious activities such as hacking, phishing, ransomware attacks, and financial fraud, all of which contribute to substantial financial losses each year. This paper provides a comprehensive overview of cybercrime, discussing its various forms and the techniques used for its detection, including tripwires, honeypots, anomaly detection systems, and operating system commands. It also explores preventive strategies and highlights relevant legal frameworks, particularly the Information Technology Act, 2000.

In addition, the paper introduces a hybrid detection model that integrates Random Forest, Long Short-Term Memory (LSTM), and XGBoost algorithms with Apache Spark to enable real-time threat detection. The proposed framework combines traditional cybersecurity practices with modern artificial intelligence techniques for improved cyber defence.

By blending conventional security methods with advanced AI-driven approaches, the proposed system enhances detection accuracy, scalability, and adaptability, making it more effective in addressing the continuously evolving landscape of cyber threats.

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