TY - GEN
T1 - Anomaly-based Intrusion Detection Approach for IoT Networks Using Machine Learning
AU - Maniriho, Pascal
AU - Niyigaba, Ephrem
AU - Bizimana, Zephanie
AU - Twiringiyimana, Valens
AU - Mahoro, Leki Jovial
AU - Ahmad, Tohari
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/11/17
Y1 - 2020/11/17
N2 - The proliferation of the Internet of Things (IoT) devices in smart environments such as smart cities or smart home facilitate communication between various objects. Nevertheless, this technological advancement comes with security challenges of IoT devices. Thus, current attacks targeting IoT networks have become motivating factors in implementing security mechanisms. Such attacks come in the form of intrusion or anomalies. Anomaly detection mechanisms have been implemented to prevent confidential resources from malevolent users. Therefore, this paper presents a new anomaly-based approach for IoT networks which is implemented with a hybrid feature selection engine that only selects most relevant features; and the Random Forest algorithm which classifies each traffic as normal or anomalous. The performance was evaluated using IoTID20, one of the latest anomaly detection datasets collected in the IoT Environment. The experimental results show that the proposed method achieves relatively high accuracy while detecting DoS (99.95%), MITM (99.97%), Scanning (99.96%) attacks.
AB - The proliferation of the Internet of Things (IoT) devices in smart environments such as smart cities or smart home facilitate communication between various objects. Nevertheless, this technological advancement comes with security challenges of IoT devices. Thus, current attacks targeting IoT networks have become motivating factors in implementing security mechanisms. Such attacks come in the form of intrusion or anomalies. Anomaly detection mechanisms have been implemented to prevent confidential resources from malevolent users. Therefore, this paper presents a new anomaly-based approach for IoT networks which is implemented with a hybrid feature selection engine that only selects most relevant features; and the Random Forest algorithm which classifies each traffic as normal or anomalous. The performance was evaluated using IoTID20, one of the latest anomaly detection datasets collected in the IoT Environment. The experimental results show that the proposed method achieves relatively high accuracy while detecting DoS (99.95%), MITM (99.97%), Scanning (99.96%) attacks.
KW - anomaly detection
KW - internet of things
KW - intrusion detection system
KW - machine learning
KW - network security
UR - http://www.scopus.com/inward/record.url?scp=85099644464&partnerID=8YFLogxK
U2 - 10.1109/CENIM51130.2020.9297958
DO - 10.1109/CENIM51130.2020.9297958
M3 - Conference contribution
AN - SCOPUS:85099644464
T3 - CENIM 2020 - Proceeding: International Conference on Computer Engineering, Network, and Intelligent Multimedia 2020
SP - 303
EP - 308
BT - CENIM 2020 - Proceeding
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2020
Y2 - 17 November 2020 through 18 November 2020
ER -
