| Title | Hybridization of Deep Learning & Machine Learning For IoT Based Intrusion Classification |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Kapoor, Mehul, Kaur, Puneet Jai |
| Conference Name | 2022 International Conference on Breakthrough in Heuristics And Reciprocation of Advanced Technologies (BHARAT) |
| Keywords | Deep Learning, Internet of Things, Intrusion detection, IoT, machine learning, Measurement, Platform as a service, pubcrawl, Real-time Systems, resilience, Resiliency, Scalability, security, Security Heuristics |
| Abstract | With the rise of IoT applications, about 20.4 billion devices will be online in 2020, and that number will rise to 75 billion a month by 2025. Different sensors in IoT devices let them get and process data remotely and in real time. Sensors give them information that helps them make smart decisions and manage IoT environments well. IoT Security is one of the most important things to think about when you're developing, implementing, and deploying IoT platforms. People who use the Internet of Things (IoT) say that it allows people to communicate, monitor, and control automated devices from afar. This paper shows how to use Deep learning and machine learning to make an IDS that can be used on IoT platforms as a service. In the proposed method, a cnn mapped the features, and a random forest classifies normal and attack classes. In the end, the proposed method made a big difference in all performance parameters. Its average performance metrics have gone up 5% to 6%. |
| DOI | 10.1109/BHARAT53139.2022.00038 |
| Citation Key | kapoor_hybridization_2022 |
Hybridization of Deep Learning & Machine Learning For IoT Based Intrusion Classification