Intrusion Detection System Using Deep Learning Asymmetric Autoencoder (DLAA)

Arjun Singh, Surbhi Chauhan, Sonam Gupta, A. Yadav
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引用次数: 0

Abstract

To protect a network security, a good network IDS is essential. With the advancement of science and technology, present intrusion detection technology is unable to manage today's complex and volatile network abnormal traffic without taking into account the detection technology's scalability, sustainability, and training time. A new deep learning method is presented to address these issues, which used an unsupervised non-symmetric convolutional autoencoder to learn the dataset features. Furthermore, a novel method based on a non-symmetric convolutional autoencoder and a multiclass SVM is proposed. The KDD99 dataset is used to create the simulation. In comparison to other approaches, the experimental outcomes suggest that the proposed approach achieves good results, which considerably lowers training time and enhances the IDS detection capability.
基于深度学习非对称自编码器(DLAA)的入侵检测系统
为了保护网络安全,一个好的网络入侵检测系统是必不可少的。随着科学技术的进步,现有的入侵检测技术在不考虑检测技术的可扩展性、可持续性和训练时间的情况下,已经无法管理当今复杂多变的网络异常流量。为了解决这些问题,提出了一种新的深度学习方法,该方法使用无监督非对称卷积自编码器来学习数据集特征。在此基础上,提出了一种基于非对称卷积自编码器和多类支持向量机的方法。KDD99数据集用于创建模拟。与其他方法相比,实验结果表明,该方法取得了较好的效果,大大缩短了训练时间,增强了IDS检测能力。
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