Developing an Intrusion Detection System (IDS) For Network Security Using Machine Learning
Keywords:
Internet of Things (IoT); Intrusion Detection System (IDS); Machine Learning (ML); Deep Learning (DL); and Minimum Redundancy Maximum Relevance (mRMR).Abstract
The Internet of Things (IoT) technologies have become so widespread that they are now the main cause of network security management problems. With the ever-growing integration of IoT into both consumer and industrial applications, security plays a very important role. The research in this paper is about the creation of a modern Intrusion Detection System (IDS) that can be applied to IoT network security using Machine Learning (ML) and Deep Learning (DL). The very heart of this system is the use of ML to discover and deal with possible threats in a quick and efficient way, though the focus here is on feature engineering that enhances detection accuracy. Through the use of mRMR for feature selection and PCA for feature reduction, our system is able to optimize the processing and analysis of network behaviors so that it can differentiate between normal operations and different types of attacks efficiently. This paper is about the comparison of effectiveness between ML and DL models in detecting threats inside IoT environments, which are tested on the ToN-IOT dataset. The findings show that our ML and DL-driven IDS not only have a high level of accuracy in threat detection but also significantly reduces the computational demands, therefore enabling more efficient real-time applications in IoT security.
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