Deep Learning Based Network Intrusion Detection System using CNN-LSTM

Abstract

Abstract— The increasing complexity of cyber threats has made traditional security systems insufficient for protecting modern network infrastructures. Advanced persistent threats, botnets, and multi-stage cyber attacks require intelligent systems capable of analyzing large volumes of network traffic. Deep learning techniques provide an effective solution by automatically learning complex patterns from data without extensive manual feature engineering. By combining CNN and LSTM architectures, the proposed system can analyze both structural and sequential characteristics of network traffic, enabling more accurate detection of malicious activities. This approach enhances the capability of intrusion detection systems and contributes to improving the overall reliability and security of modern computer networks. These days, with everyone glued to their devices and the internet running pretty much everything, network security has become a major issue for both businesses and regular folks. Cyber-attacks — DDoS, malware, phishing, people trying to sneak into accounts — they aren’t just happening more often, they’re getting trickier too. The problem is, traditional Intrusion Detection Systems aren’t keeping up. Systems that rely on fixed signatures or rules struggle with new, unknown attacks and end up flagging way too many false positives, which honestly just becomes a headache. So, this research focuses on a new approach: a deep learning-based Network Intrusion Detection System that uses a combination of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. The idea is simple. The CNN looks through raw network traffic data and pulls out meaningful features automatically. Then, the LSTM checks for patterns over time, spotting suspicious behavior as it unfolds. By combining CNN and LSTM, the system digs into both what’s happening at a single moment and how things change over time, letting it catch malicious activity more accurately. To build and test the model, using well-known datasets like NSL-KDD and CICIDS2017. These datasets cover all sorts of attacks: DoS, Probe, Remote-to-Local, User-to-Root — you name it. Before jumping into training, they cleaned up the data, normalized it, picked out the most useful features, and converted everything into numbers so the deep learning algorithms could make sense of it. These steps help the model focus and boost its performance.To see how well the model works, we measured accuracy, precision, recall, and F1-score. Turns out, the hybrid CNN-LSTM system outperformed traditional machine learning methods. It nailed higher detection accuracy and cut down on false positives, making network security a little less stressful and a lot more reliable. The proposed deep learning-based intrusion detection system can provide an effective solution for enhancing network security in modern environments. It can be applied to enterprise networks, cloud infrastructures, and large-scale communication systems to detect malicious activities in real time. Future research can focus on optimizing the model for real-time deployment, integrating attention mechanisms, and extending the system for Internet of Things (IoT) network security.