Intrusion Detection in IOT Networks using Machine  Learning Techniques

Ahmed Adnan Hadi; Khalid Murad Abdullah

doi:10.55529/jecnam.42.1.18

Intrusion Detection in IOT Networks using Machine Learning Techniques

https://doi.org/10.55529/jecnam.42.1.18

Authors

Ahmed Adnan Hadi Open Educational College, Al-Qadisiyah Center, Iraq.
Khalid Murad Abdullah Open Educational College, Al-Qadisiyah Center, Iraq.

Keywords:

Internet of Things (IoT), Intrusion Detection System (IDS), Distributed Denial of Service (DDoS), Artificial Intelligence (AI), and Long Short Term Memory (LSTM).

Abstract

Artificial intelligence (AI) and machine learning (ML) are essential for processing vast datasets and forecasting unknown events, offering innovative solutions to IoT security challenges. Recurrent neural networks (RNNs) have extended the predictive capacity of traditional neural networks, particularly in forecasting sequential events. With the increasing frequency of system attacks, the integration of machine learning into intrusion detection systems (IDS) is vital to identify and report potential threats, thereby safeguarding IoT infrastructure against destructive attacks

Published

2024-02-01

How to Cite

Ahmed Adnan Hadi, & Khalid Murad Abdullah. (2024). Intrusion Detection in IOT Networks using Machine Learning Techniques. Journal of Electronics, Computer Networking and Applied Mathematics , 4(02), 1–18. https://doi.org/10.55529/jecnam.42.1.18