TY - GEN
T1 - Profiling-based classification algorithms for security applications in internet of things
AU - Seo, Eunil
AU - Kim, Hyoungshick
AU - Chung, Tai Myoung
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - Due to the various types of network resources involved in the Internet of Things (IoT), it becomes challenging to detect security incidents and unexpected faults in IoT environments. The nature of network objects (e.g., system, user, service, and devices) is too various and changeable to predict objects' behaviors and to identify the best parameters for the machine learning model in order to detect anomalies against IoT protection. We propose a new profiling method called 'Management Information Base for IoT (MIB-IoT)' by extending conventional MIB to a more generalized structure in order to represent not only the structured properties of network objects but also the best machine learning model for each network object in a systematic fashion. MIB-IoT profiles can be defined for various applications such as abnormal behavior detection, malicious behavior detection, and even data source identification. To demonstrate the feasibility of the proposed MIB-IoT, we apply various classification algorithms on datasets consisting of normal operation data, hardware fault data, and malicious data. The experiment results show that the classification algorithm using MIB-IoT is capable of achieving an accuracy of 99.81% for malicious behavior detection and an accuracy of 78.51% for data source identification respectively.
AB - Due to the various types of network resources involved in the Internet of Things (IoT), it becomes challenging to detect security incidents and unexpected faults in IoT environments. The nature of network objects (e.g., system, user, service, and devices) is too various and changeable to predict objects' behaviors and to identify the best parameters for the machine learning model in order to detect anomalies against IoT protection. We propose a new profiling method called 'Management Information Base for IoT (MIB-IoT)' by extending conventional MIB to a more generalized structure in order to represent not only the structured properties of network objects but also the best machine learning model for each network object in a systematic fashion. MIB-IoT profiles can be defined for various applications such as abnormal behavior detection, malicious behavior detection, and even data source identification. To demonstrate the feasibility of the proposed MIB-IoT, we apply various classification algorithms on datasets consisting of normal operation data, hardware fault data, and malicious data. The experiment results show that the classification algorithm using MIB-IoT is capable of achieving an accuracy of 99.81% for malicious behavior detection and an accuracy of 78.51% for data source identification respectively.
KW - Abnormal behavior detection
KW - Classification
KW - Internet of Things (IoT)
KW - Machine learning
KW - Management Information Base (MIB)
KW - Profiling
UR - https://www.scopus.com/pages/publications/85072771600
U2 - 10.1109/ICIOT.2019.00033
DO - 10.1109/ICIOT.2019.00033
M3 - Conference contribution
AN - SCOPUS:85072771600
T3 - Proceedings - 2019 IEEE International Congress on Internet of Things, ICIOT 2019 - Part of the 2019 IEEE World Congress on Services
SP - 138
EP - 146
BT - Proceedings - 2019 IEEE International Congress on Internet of Things, ICIOT 2019 - Part of the 2019 IEEE World Congress on Services
A2 - Bertino, Elisa
A2 - Chang, Carl K.
A2 - Chen, Peter
A2 - Damiani, Ernesto
A2 - Goul, Michael
A2 - Oyama, Katsunori
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th IEEE International Congress on Internet of Things, ICIOT 2019
Y2 - 8 July 2019 through 13 July 2019
ER -