A new DDoS attacks intrusion detection model based on deep learning for cybersecurity. Issue 118 (July 2022)

• Record Type: Journal Article
• Authors: Akgun, Devrim; Hizal, Selman; Cavusoglu, Unal
• Is Part Of: Computers & security. Issue 118(2022)
• Publication Date: 2022-07
• Subjects: Intrusion detection system -- Deep learning -- Cloud security -- DDoS -- Data preprocessing; Computer security -- Periodicals; Electronic data processing departments -- Security measures -- Periodicals; 005.805
• DOI: 10.1016/j.cose.2022.102748
• Languages: English
• ISSNs: 0167-4048

Abstract

Abstract: The data is exposed to many attacks during communication in the network environment. It is becoming increasingly essential to identify intrusions into network communications. Researchers use machine learning techniques to design effective intrusion detection systems. In this study, we proposed an intrusion detection system that includes preprocessing procedures and a deep learning model to detect DDoS attacks. For this purpose, various models based on Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), and Long Short Term Memory (LSTM) have been evaluated in terms of detection performance and real-time performance. We tested the suggested model using the CIC-DDoS2019 dataset, which is frequently used in the literature. We applied preprocess techniques such as feature elimination, random subset selection, feature selection, duplication removal, and normalization to the CIC-DDoS2019 dataset. As a result, better recognition performance was obtained for the training and testing evaluations. According to the test results, 99.99% for binary and 99.30% for multiclass accuracy using the CNN-based inception like model gave the best results among the proposed models. Also, the inference time of the proposed model for various sizes of test data looks promising compared to baseline models with a smaller number of trainable parameters. The proposed IDS system, together with the preprocessing methods, provides better results when compared to state-of-the-art studies.