期刊
IEEE ACCESS
卷 10, 期 -, 页码 99837-99849出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2022.3206425
关键词
Feature extraction; Convolutional neural networks; Intrusion detection; Deep learning; Training data; Machine learning algorithms; Wireless sensor networks; Intrusion detection system; deep learning; convolutional neural network; long-short term memory; accuracy; false alarm rate; binary classification; multiclass classification
资金
- Ministry of Higher Education (MOHE), Malaysia, under the Fundamental Research Grant Scheme (FRGS) [FRGS/1/2019/TK10/UTM/02/16]
Network security is crucial in daily interactions and networks, with researchers developing a hybrid intrusion detection system model using deep learning algorithms and artificial neural networks. Through training on different datasets, the model demonstrates high detection rate and accuracy.
Network security becomes indispensable to our daily interactions and networks. As attackers continue to develop new types of attacks and the size of networks continues to grow, the need for an effective intrusion detection system has become critical. Numerous studies implemented machine learning algorithms to develop an effective IDS; however, with the advent of deep learning algorithms and artificial neural networks that can generate features automatically without human intervention, researchers began to rely on deep learning. In our research, we took advantage of the Convolutional Neural Network's ability to extract spatial features and the Long Short-Term Memory Network's ability to extract temporal features to create a hybrid intrusion detection system model. We added batch normalization and dropout layers to the model to increase its performance. Based on the binary and multiclass classification, the model was trained using three datasets: CIC-IDS 2017, UNSW-NB15, and WSN-DS. The confusion matrix determines the system's effectiveness, which includes evaluation criteria such as accuracy, precision, detection rate, F1-score, and false alarm rate (FAR). The effectiveness of the proposed model was demonstrated by experimental results showing a high detection rate, high accuracy, and a relatively low FAR.
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