网络入侵检测系统:混合深度学习方法的系统文献综述

S. Wanjau, G. Wambugu, A. Oirere
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引用次数: 0

摘要

网络入侵检测系统(nids)已经成为标准的安全解决方案,它通过仔细检查传入和传出的网络流量来发现对组织计算机网络的未经授权的访问,以寻找恶意活动的迹象。近年来,基于深度学习的nids已经成为网络安全研究的一个活跃领域,并且已经对这些系统进行了一些调查。尽管有大量的调查涵盖了这一新兴的研究领域,但文献中缺乏对不同混合深度学习模型的实证分析。本文综述了用于网络入侵检测的混合深度学习模型,并指出了研究人员和从业人员正在开发现代入侵检测系统的特点。本文首先阐述了网络入侵检测系统的概念。其次,介绍了用于nids设计的混合深度学习技术的分类。最后,对基于混合深度学习的NIDS进行了综述。本研究采用系统文献综述的方法,通过文献综述,制定了一个正式的、系统的程序,同时明确了获取信息的协议。调查结果表明,与其他深度学习算法相比,基于混合深度学习的模型产生了理想的性能。结果还表明,优化、经验风险最小化和模型复杂性控制是基于深度学习的混合模型设计的最重要特征。最后,讨论了研究调查中暴露的文献中的关键问题,并提出了研究人员和实践者在设计用于网络入侵检测的深度学习方法方面的几个潜在的未来方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Network Intrusion Detection Systems: A Systematic Literature Review o f Hybrid Deep Learning Approaches
Network Intrusion Detection Systems (NIDSs) have become standard security solutions that endeavours to discover unauthorized access to an organizational computer network by scrutinizing incoming and outgoing network traffic for signs of malicious activity. In recent years, deep learning based NIDSs have emerged as an active area of research in cybersecurity and several surveys have been done on these systems. Although a plethora of surveys exists covering this burgeoning body of research, there lacks in the literature an empirical analysis of the different hybrid deep learning models. This paper presents a review of hybrid deep learning models for network intrusion detection and pinpoints their characteristics which researchers and practitioners are exploiting to develop modern NIDSs. The paper first elucidates the concept of network intrusion detection systems. Secondly, the taxonomy of hybrid deep learning techniques employed in designing NIDSs is presented. Lastly, a survey of the hybrid deep learning based NIDS is presented. The study adopted the systematic literature review methodology, a formal and systematic procedure by conducting bibliographic review, while defining explicit protocols for obtaining information. The survey results suggest that hybrid deep learning-based models yield desirable performance compared to other deep learning algorithms. The results also indicate that optimization, empirical risk minimization and model complexity control are the most important characteristics in the design of hybrid deep learning-based models. Lastly, key issues in the literature exposed in the research survey are discussed and then propose several potential future directions for researchers and practitioners in the design of deep learning methods for network intrusion detection.
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