Cloud Intrusion Detection Method Based on Stacked Contractive Auto-Encoder and Support Vector Machine

• Published in: IEEE transactions on cloud computing Vol. 10; no. 3; pp. 1634 - 1646
• Main Authors: Wang, Wenjuan, Du, Xuehui, Shan, Dibin, Qin, Ruoxi, Wang, Na
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.07.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Cloud computing; Coders; Communications traffic; contractive auto-encoder; Deep learning; Dimensionality reduction; Feature extraction; Intrusion detection; intrusion detection system (IDS); Intrusion detection systems; Machine learning; Redundancy; support vector machine; Support vector machines; Sustainable development; Telecommunication traffic
• ISSN: 2168-7161; 2372-0018; 2372-0018
• DOI: 10.1109/TCC.2020.3001017

Security issues have resulted in severe damage to the cloud computing environment, adversely affecting the healthy and sustainable development of cloud computing. Intrusion detection is one of the technologies for protecting the cloud computing environment from malicious attacks. However, network traffic in the cloud computing environment is characterized by large scale, high dimensionality, and high redundancy, these characteristics pose serious challenges to the development of cloud intrusion detection systems. Deep learning technology has shown considerable potential for intrusion detection. Therefore, this study aims to use deep learning to extract essential feature representations automatically and realize high detection performance efficiently. An effective stacked contractive autoencoder (SCAE) method is presented for unsupervised feature extraction. By using the SCAE method, better and robust low-dimensional features can be automatically learned from raw network traffic. A novel cloud intrusion detection system is designed on the basis of the SCAE and support vector machine (SVM) classification algorithm. The SCAE+SVM approach combines both deep and shallow learning techniques, and it fully exploits their advantages to significantly reduce the analytical overhead. Experiments show that the proposed SCAE+SVM method achieves higher detection performance compared to three other state-of-the-art methods on two well-known intrusion detection evaluation datasets, namely KDD Cup 99 and NSL-KDD.