@article { author = {Al-Turaiki, Isra and Altwaijry, Najwa and Agil, Abeer and Aljodhi, Haya and Alharbi, sara and Alqassem, Lina}, title = {Anomaly-Based Network Intrusion Detection Using Bidirectional Long Short Term Memory and Convolutional Neural Network}, journal = {The ISC International Journal of Information Security}, volume = {12}, number = {3}, pages = {37-44}, year = {2020}, publisher = {Iranian Society of Cryptology}, issn = {2008-2045}, eissn = {2008-3076}, doi = {10.22042/isecure.2021.271076.624}, abstract = {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}, keywords = {Network Intrusion Detection Systems,Deep Learning,Long Short Term Memory,LSTM,convolutional neural network,CNN}, url = {https://www.isecure-journal.com/article_128631.html}, eprint = {https://www.isecure-journal.com/article_128631_303cd7f91c544cf74e49019f44bfa101.pdf} }