An Integrated Approach for Intrusion Detection in Intelligent Grid Computing Networks Using Machine Learning

Le résumé fourni par la source

Intelligent Grid (IG) systems improve the usability of old energy networks, but they can still be hacked in many ways. Intruders can get into the system through these holes, risking IG networks' safety and privacy. An Intrusion Detection System (IDS) keeps services safe and secure in an IG setting. With the help of Machine Learning (ML) techniques and characteristics, this work shows an IDS for IG platforms. The categorization algorithm comprises a Convolutional Neural Network (CNN) and a Gated Recurrent Unit (GRU). The research uses Precision, Intrusion Detecting Rate (IDR), and False Alarming Ratio (FAR) to rate how well the suggested approach works. It turns out that the Random Forest (RF) and Neural Network (NN) algorithms did outperform the others. The study found that the KDD-99 records had a False Alarm Rate (FAR) of 7.29%, and the NSL-KDD records had a FAR of 7.31%. 88.68% of the time, both methods find things, and 90.87% of the time, they confirm that they are correct.

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