TY - JOUR ID - 128631 TI - Anomaly-Based Network Intrusion Detection Using Bidirectional Long Short Term Memory and Convolutional Neural Network JO - The ISC International Journal of Information Security JA - ISECURE LA - en SN - 2008-2045 AU - Al-Turaiki, Isra AU - Altwaijry, Najwa AU - Agil, Abeer AU - Aljodhi, Haya AU - Alharbi, sara AU - Alqassem, Lina AD - Information Technology Department, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia. AD - Information Technology Department, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia AD - Information Technology Department, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia. Y1 - 2020 PY - 2020 VL - 12 IS - 3 SP - 37 EP - 44 KW - Network Intrusion Detection Systems KW - Deep Learning KW - Long Short Term Memory KW - LSTM KW - convolutional neural network KW - CNN DO - 10.22042/isecure.2021.271076.624 N2 - With present-day technological advancements, the number of devices connected to the Internet has increased dramatically. Cybersecurity attacks are increasingly becoming a threat to individuals and organizations. Contemporary security frameworks incorporate Network Intrusion Detection Systems (NIDS). These systems are an essential component for ensuring the security of computer networks against attacks. In this paper, two deep learning architectures are proposed for both binary and multi-class classification of network attacks. The models, CNN-IDS and LSTM-IDS, are based on Convolutional Neural Network and Long Short Term Memory architectures, respectively. The models are evaluated using the well-known NSL-KDD dataset. The performance is measured in terms of accuracy, precision, recall, and F-measure. Experimental results show that the models achieve good performance in terms of accuracy and recall. Network intrusion detection systems are an integral part of contemporary networks. They provide administrators with an early warning for known and unknown attacks. In this paper, two deep learning architectures to aid administrators in detecting network attacks are outlined UR - https://www.isecure-journal.com/article_128631.html L1 - https://www.isecure-journal.com/article_128631_303cd7f91c544cf74e49019f44bfa101.pdf ER -