1 Department of Medical Laboratory Technology, College of Medical Technology, The Islamic University, Najaf, Iraq.
2 Department of Computer Technical Engineering, College of Technical Engineering, The Islamic University, Babylon branch, Iraq.
7GSC Advanced Research and Reviews, 2026, 28(01), 175–177
Article DOI: 10.30574/gscarr.2026.28.1.0170
Received on 13 June 2026; revised on 20 July 2026; accepted on 22 July 2026
This paper explores the application of artificial intelligece, particularly deep learning, in intrusion detection systems (IDS) for modern network security. With the increasing complexity of cyber threats, traditional security mechanisms are no longer sufficient. Deep learning models such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) provide advanced capabilities in detecting anomalies and malicious patterns. This study reviews existing techniques, datasets, and challenges while proposing improvements in detection accuracy and efficiency.
Intrusion Detection; Deep Learning; Network Security; Cybersecurity; Anomaly Detection
Preview Article PDF
Noor Hassanin Hashim, Saja Raheem Mohammad and Zainab Abbas Kadhim. AI-based Intrusion Detection Systems using Deep Learning. GSC Advanced Research and Reviews, 2026, 28(01), 175–177. Article DOI: https://doi.org/10.30574/gscarr.2026.28.1.0170.