IoT Security Using Machine Learning Methods
Date
2023-04-17
Authors
Hosseini Goki, Seyedamiryousef
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
The rapid growth of internet-connected devices has made robust cybersecurity measures
essential to protect against cyber threats. IoT cybersecurity includes various methods
and technologies to secure internet-connected devices and systems from cyber attacks.
The unique nature of IoT devices and systems poses several challenges to cybersecurity,
including limited processing power, minimal security features, and vulnerability to
attacks like DoS and DDoS. Cybersecurity strategies for IoT include encryption,
authentication, access control, and threat detection and response, which utilize machine
learning and artificial intelligence technologies to identify and respond to potential
cyber attacks in real-time. The report discusses two projects related to cybersecurity in
IoT environments, one focused on developing an intrusion detection system (IDS) based
on deep learning algorithms to detect DDoS attacks, and another focused on identifying
potential abnormalities in IoT networks using a fingerprint. These projects highlight the
importance of prioritizing cybersecurity measures to protect against the growing
number of cyber threats facing IoT devices and systems.
Description
Keywords
IoT, Security, Intrusion Detection System, Machine Learning, DDoS Attacks, Fingerprint