Threat Detection using Machine/Deep Learning in IOT Environments

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引用次数: 5

Abstract

The quality of human life is improving day by day and IOT plays a very important role in this improvement. Everything related to internet have some security concerns. This paper aims to improve the security in IOT environments. In any of the IOT networks the unknown and knows flaws can be a backdoor for any adversary. The increase use of such environment results in the increase of zero day cyber-attacks. This paper aims to focus on different models of DL in order to predict the attacks in IOT environments. The main aim of this research is to provide a very best solution for the detection of threats in order to improve the infrastructures of IOT. In this paper different experiments has been conducted and its results has been discussed in order to provide an effective solution
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在物联网环境中使用机器/深度学习进行威胁检测
人类的生活质量日益提高,物联网在其中发挥着非常重要的作用。与互联网有关的一切都有一些安全问题。本文旨在提高物联网环境下的安全性。在任何物联网网络中,未知和已知的漏洞都可能成为任何对手的后门。这种环境使用的增加导致零日网络攻击的增加。本文旨在关注不同的深度学习模型,以预测物联网环境中的攻击。本研究的主要目的是为检测威胁提供最佳解决方案,以改善物联网的基础设施。本文进行了不同的实验,并对其结果进行了讨论,以提供一个有效的解决方案
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Structural Analysis of Enhanced Performance Organic Light Emitting Diodes (OLEDs) Threat Detection using Machine/Deep Learning in IOT Environments An Efficient Approach of Threat Hunting Using Memory Forensics
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