Improving Cyber Threat Detection with CNN and RNN-Based Intrusion Detection Systems

With the rapid evolution of cyber threats, traditional security mechanisms struggle to effectively detect and mitigate attacks in real-time. Intrusion Detection Systems (IDS) have emerged as a crucial component in cybersecurity, aiming to identify and respond to malicious activities within networks and systems. Traditional IDS methods, such as signature-based and rule-based approaches, suffer from limitations like high false alarm rates and an inability to detect novel threats. To address these challenges, deep learning techniques have gained significant attention due to their ability to learn complex patterns and detect anomalies with higher accuracy.This research explores the application of deep learning models, including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and hybrid approaches, in enhancing IDS performance. We analyze publicly available datasets such as KDD Cup 99, NSL-KDD, and CIC-IDS2017 to evaluate the effectiveness of deep learning-based IDS. The study further discusses key challenges, including model interpretability, adversarial attacks, and real-time deployment constraints. Our findings indicate that deep learning significantly improves intrusion detection accuracy compared to traditional methods, making it a promising approach for modern cybersecurity frameworks