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Deep learning approaches for anomaly-based intrusion detection systems: A survey, taxonomy, and open issues
Knowledge-Based Systems ( IF 8.8 ) Pub Date : 2019-10-16 , DOI: 10.1016/j.knosys.2019.105124
Arwa Aldweesh , Abdelouahid Derhab , Ahmed Z. Emam

The massive growth of data that are transmitted through a variety of devices and communication protocols have raised serious security concerns, which have increased the importance of developing advanced intrusion detection systems (IDSs). Deep learning is an advanced branch of machine learning, composed of multiple layers of neurons that represent the learning process. Deep learning can cope with large-scale data and has shown success in different fields. Therefore, researchers have paid more attention to investigating deep learning for intrusion detection. This survey comprehensively reviews and compares the key previous deep learning-focused cybersecurity surveys. Through an extensive review, this survey provides a novel fine-grained taxonomy that categorizes the current state-of-the-art deep learning-based IDSs with respect to different facets, including input data, detection, deployment, and evaluation strategies. Each facet is further classified according to different criteria. This survey also compares and discusses the related experimental solutions proposed as deep learning-based IDSs.

By analysing the experimental studies, this survey discusses the role of deep learning in intrusion detection, the impact of intrusion detection datasets, and the efficiency and effectiveness of the proposed approaches. The findings demonstrate that further effort is required to improve the current state-of-the art. Finally, open research challenges are identified, and future research directions for deep learning-based IDSs are recommended.



中文翻译:

基于异常的入侵检测系统的深度学习方法:调查,分类法和未解决的问题

通过各种设备和通信协议传输的数据的大量增长引起了严重的安全问题,这增加了开发高级入侵检测系统(IDS)的重要性。深度学习是机器学习的高级分支,由代表学习过程的多层神经元组成。深度学习可以应对大规模数据,并已在不同领域取得成功。因此,研究人员已将更多的精力用于研究深度学习以进行入侵检测。这项调查全面回顾并比较了以前针对深度学习的关键网络安全调查。通过广泛的审核,该调查提供了一种新颖的细分类法,该分类法针对不同方面对当前基于深度学习的最新IDS进行了分类,包括输入数据,检测,部署和评估策略。每个构面根据不同的标准进一步分类。该调查还比较并讨论了相关的实验解决方案,这些解决方案被提议为基于深度学习的IDS。

通过分析实验研究,本次调查讨论了深度学习在入侵检测中的作用,入侵检测数据集的影响以及所提出方法的效率和有效性。这些发现表明,需要进一步的努力来改善当前的最新技术水平。最后,确定了开放的研究挑战,并建议了基于深度学习的IDS的未来研究方向。

更新日期:2020-01-16
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