Enhancing Cybersecurity Through AI-Driven Threat Detection and Classification

The digital revolution has transformed the way organizations, institutions, and individuals communicate, share information, and conduct business. However, this growth in technology has also increased the frequency and sophistication of cyberattacks. Traditional security mechanisms based on signatures, rules, and manual monitoring are no longer sufficient to detect modern threats such as zero-day exploits, ransomware, phishing, and advanced persistent threats. In response, Artificial Intelligence (AI) and Machine Learning (ML) have emerged as powerful enablers for developing intelligent, adaptive, and automated cybersecurity systems. This research paper explores the role of AI in enhancing cybersecurity with a special focus on intelligent threat detection. AI systems can learn from large volumes of data, identify hidden patterns, detect anomalies, and make real-time decisions with minimal human intervention. Machine Learning algorithms such as Random Forest, Support Vector Machine, Decision Tree, and Deep Learning models like Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) are increasingly used to detect malicious activities in network traffic, system logs, emails, and endpoint behavior. The study presents a comprehensive review of existing literature, threat landscape, datasets, preprocessing techniques, and machine learning algorithms used for threat detection. A novel intelligent threat detection framework is also proposed, which integrates data collection, preprocessing, feature extraction, machine learning classification, and automated response into a unified pipeline. The framework is evaluated using benchmark datasets such as CICIDS2017, NSL-KDD, and UNSW-NB15. Experimental results demonstrate that AI-based models outperform traditional rule-based systems in terms of accuracy, precision, recall, and real-time detection capability. The findings indicate that AI not only improves detection accuracy and reduces false positives but also enhances response time and supports proactive security. The paper also discusses real-world applications, challenges, limitations, and future research directions. In conclusion, AI-driven cybersecurity is essential for building resilient digital infrastructures. By combining intelligent algorithms with security expertise, organizations can effectively detect, prevent, and respond to cyber threats in an increasingly complex digital world.