| Full text | |
| Author(s): |
Carrer, Alexandre Marques
;
Margi, Cintia Borges
Total Authors: 2
|
| Document type: | Journal article |
| Source: | 2024 IEEE 49TH CONFERENCE ON LOCAL COMPUTER NETWORKS, LCN 2024; v. N/A, p. 9-pg., 2024-01-01. |
| Abstract | |
In recent years, there has been an increase in research concerning Intrusion Detection Systems (IDS) for Internet of Things (IoT). Detecting network attacks is important to ensure network integrity and availability. Existing methods in the literature typically rely on monitoring network metrics and behavior for intrusion detection. On the other hand, the attack footprint affects not only the network metrics but also the operational metrics of individual devices. Operational metrics could be used to enable informed anomaly detection and enhance network-based intrusion detection systems approaches in the literature. Thus, this work evaluates the use of operational metrics from individual sensors for intrusion detection in the IoT paradigm. For that, we implemented and analyzed a centralized IDS that utilizes both network and operational metrics. Blackhole, Greyhole, and Flooding attacks were simulated on a network with emulated IoT devices. The IDS is implemented with an XGBoost classifier model that is validated by classifying a network with out-of-distribution attack cases. Despite the overhead caused in terms of processing and metrics transmission to the IDS, the operational metrics presented higher information gain and SHAP values in the collected metrics and increased IDS detection rate to 97% in the implemented attacks. (AU) | |
| FAPESP's process: | 20/09850-0 - Applied Artificial Intelligence Research Center: accelerating the evolution of industries toward standard 5.0 |
| Grantee: | Jefferson de Oliveira Gomes |
| Support Opportunities: | Research Grants - Research Centers in Engineering Program |
| FAPESP's process: | 22/07523-8 - Artificial intelligence applied to the internet of things: new technologies for communications |
| Grantee: | Cíntia Borges Margi |
| Support Opportunities: | Regular Research Grants |