AED: A Novel Approach for Intrusion Detection without Abnormal Samples in Big Data Environment

The rapid advance of multimedia devices, including sensors, cameras, and mobile phones, has given rise to the prevalence of Internet of Multimedia Things (IoMT), generating huge volumes of application-oriented multimedia data. At the same time, network security issues in the multimedia big data environment also increases. Network intrusion detection (NID) system demonstrates its power in preventing cyber-attacks against multimedia platforms. However, the existing NID methods that are based on machine learning or deep learning classifiers may fail when there is a lack of abnormal traffic samples for training in the real-world scenario. We propose a novel approach for intrusion detection based on deep AutoEncoder and Differential comparison named AED, which only requires the normal traffic samples in the training phase. We conduct extensive experiments on two real-world datasets to evaluate the effectiveness of the proposed AED. The experimental results show that AED can outperform the baseline methods of three categories in terms of accuracy, precision, recall, and F1-score.