Analysis and prevention of IoT vulnerabilities by implementing a lightweight AD-IoT intrusion detection system model

Until a few years, the Internet of Things (IoT) has been continually evolving and today it is giving a great welcome to a paradigmatic style of technology, for this reason new electronic devices are being invented that are used continuously by many people. But these devices must be protected in a ce...

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Bibliographic Details
Published in2020 IEEE Congreso Bienal de Argentina (ARGENCON) pp. 1 - 4
Main Authors Andrade-Arenas, Laberanio, Ramos-Romero, Jhoel Anthony
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2020
Subjects
Analytical models
Computational modeling
cyberattacks
Data models
Machine learning algorithms
Measurement
Network-based IDS
protocol
Protocols
Radio frequency
vulnerability
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DOI10.1109/ARGENCON49523.2020.9505497

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Summary:Until a few years, the Internet of Things (IoT) has been continually evolving and today it is giving a great welcome to a paradigmatic style of technology, for this reason new electronic devices are being invented that are used continuously by many people. But these devices must be protected in a certain way since there are many attacks on the IoT web, with which, by integrating new algorithms, certain network security will be obtained. In addition, the use of new model systems; such as the IoT Anomaly Detection System (AD-IoT) that uses the Random Forest (RF) machine learning algorithm to detect web attacks; It will help us reduce attacks from bad IoT computer users. In this present article, a very effective evaluative metric was obtained through a group of data that will help us develop and modify the communication security paradigms of IoT devices.
DOI:10.1109/ARGENCON49523.2020.9505497