Deep Learning Based Intrusion Detection Systems Techniques in IoT - Survey

Samay Kalpesh Patel, Sapna Sadhwani, R. Muthalagu, Pranav Mothabhau Pawar
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Abstract

Industry 4.0 is changing the way we communicate and operate as a society, its bringing changes in technologies, industries and a part of this industry Internet of Thing (IoT), they are devices which communicate with each other and are being integrated slowly in all sectors. this creates number of concerns especially towards security and privacy. Cyber intrusion attacks form a major part of the concern as it compromises integrity of sensitive data and are growing in volume with variations increasing rapidly. High complexity of such intrusion attacks has defeated most of the traditional defense techniques This paper focuses on exploring research that was conducted in area of IoT security, specifically in improvement of Intrusion detection system using Deep learning techniques. The results and methods are also discussed which can form a potential base for further research.
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基于深度学习的物联网入侵检测系统技术
工业4.0正在改变我们作为一个社会的沟通和运作方式,它带来了技术,行业和这个行业物联网(IoT)的一部分的变化,它们是相互通信的设备,正在慢慢地整合到所有部门。这带来了许多担忧,尤其是在安全和隐私方面。网络入侵攻击是担忧的主要部分,因为它危及敏感数据的完整性,并且数量正在增长,变化也在迅速增加。这种入侵攻击的高复杂性使得大多数传统的防御技术都无法实现。本文重点探讨了物联网安全领域的研究,特别是利用深度学习技术改进入侵检测系统。本文还对结果和方法进行了讨论,为进一步的研究奠定了基础。
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