Cloud Intrusion Detection Method Based on Stacked Contractive Auto-Encoder and Support Vector Machine
Cloud Intrusion Detection Method Based on Stacked Contractive Auto-Encoder and Support Vector Machine
复制标题
基于堆叠式收缩自编码器和支持向量机的云入侵检测方法
DOI：
10.1109/tcc.2020.3001017
复制
发表时间：
2022-07-01
影响因子：
6.5
通讯作者：
Wang, Na
中科院分区：
文献类型：
--
作者：
Wang, Wenjuan;Du, Xuehui;Wang, Na
安全问题严重破坏了云计算环境，影响了云计算的健康可持续发展。入侵检测是保护云计算环境免受恶意攻击的技术之一。然而，云计算环境下的网络流量具有规模大、维度高、冗余度高的特点，这些特点给云入侵检测系统的发展带来了严峻的挑战。深度学习技术在入侵检测方面显示出了巨大的潜力。因此，本研究的目的是利用深度学习自动提取本质特征，实现高效率的检测性能。提出了一种有效的堆叠压缩自动编码器(SCAE)无监督特征提取方法。通过使用SCAE方法，可以从原始网络流量中自动学习更好和更健壮的低维特征。设计了一种基于SCAE和支持向量机分类算法的云入侵检测系统。SCAE+支持向量机方法结合了深度学习和浅层学习技术，它充分利用了它们的优势，显著减少了分析开销。实验表明，在KDD CUP 99和NSL-KDD这两个入侵检测评估数据集上，SCAE+支持向量机方法的检测性能优于其他三种方法。
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.