Open AccessThreat Detection using Machine/Deep Learning in IOT Environments
Author(s) -
Danish Javeed,
Umar Mohammedbadamasi,
Tahir Iqbal,
Aliyu Umar,
Cosmas Obiora Ndubuisi
Publication year - 2020
Publication title -
international journal of computer networks and communications security
Language(s) - English
Resource type - Journals
eISSN - 2410-0595
pISSN - 2308-9830
DOI - 10.47277/ijcncs/8(8)2
Subject(s) - backdoor , internet of things , computer science , adversary , computer security , order (exchange) , focus (optics) , quality (philosophy) , artificial intelligence , business , philosophy , physics , finance , epistemology , optics
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