| Title | Classifier with Deep Deviation Detection in PoE-IoT Devices |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Bhat, P., Batakurki, M., Chari, M. |
| Conference Name | 2020 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT) |
| Date Published | jul |
| Keywords | behavior analysis, Cameras, computer network security, Decision Tree, Decision trees, deep deviation detection, deep packet inspection, DPI, edge intelligence, feature extraction, firmware, Image edge detection, Internet of Things, IoT, learning (artificial intelligence), machine learning, ML, Monitoring, network traffic characteristics, poe, PoE-IoT classification, PoE-IoT devices, pubcrawl, Resiliency, Scalability, security, Switches, telecommunication traffic, vulnerable IoT devices |
| Abstract | With the rapid growth in diversity of PoE-IoT devices and concept of "Edge intelligence", PoE-IoT security and behavior analysis is the major concern. These PoE-IoT devices lack visibility when the entire network infrastructure is taken into account. The IoT devices are prone to have design faults in their security capabilities. The entire network may be put to risk by attacks on vulnerable IoT devices or malware might get introduced into IoT devices even by routine operations such as firmware upgrade. There have been various approaches based on machine learning(ML) to classify PoE-IoT devices based on network traffic characteristics such as Deep Packet Inspection(DPI). In this paper, we propose a novel method for PoE-IoT classification where ML algorithm, Decision Tree is used. In addition to classification, this method provides useful insights to the network deployment, based on the deviations detected. These insights can further be used for shaping policies, troubleshooting and behavior analysis of PoE-IoT devices. |
| DOI | 10.1109/CONECCT50063.2020.9198605 |
| Citation Key | bhat_classifier_2020 |
Classifier with Deep Deviation Detection in PoE-IoT Devices