What is it about?
Security for the Internet of Things (IoT) and Intrusion Detection Systems (IDS) have both benefited greatly from the use of Machine Learning (ML) approaches, the latest technology in cybersecurity. It looks on the viability of detecting IoT botnet security using recurrent variation auto-encoder. As no one method has proven to have the ability to handle this security danger, botnets can generate Distributed Denial of Service (DDoS) common cyber-attacks(Hackers flood a network) with requests to exhaust bandwidth.
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Why is it important?
In many cases, DDoS attacks are meant to be more of a nuisance than anything else, and pose a serious security risk on IoT networks. IoT environment criteria, such as computing power and energy efficiency, are frequently not addressed by these technologies. Among the solutions for botnet security detection is variation Autoencoder. ML is the foundation of many botnet detection techniques.
Perspectives
Writing this article was a great pleasure as it has co-authors with whom I have had long standing collaborations. This article discussed four important topics (IoT, Machine Learning, IDS, and DDoS. A new method was developed to protect data.
Alaa Hussein Al-Hamami
Al Hikma University College
Read the Original
This page is a summary of: Implementing machine learning in cyber security-based IoT for botnets security detection by applying recurrent variational autoencoder, January 2024, American Institute of Physics,
DOI: 10.1063/5.0234381.
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