TY - GEN
T1 - Enabling Risk Management for Smart Infrastructures with an Anomaly Behavior Analysis Intrusion Detection System
AU - Pacheco, Jesus
AU - Zhu, Xiaoyang
AU - Badr, Youakim
AU - Hariri, Salim
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/10/9
Y1 - 2017/10/9
N2 - The Internet of Things (IoT) connects not only computers and mobile devices, but it also interconnects smart buildings, homes, and cities, as well as electrical grids, gas, and water networks, automobiles, airplanes, etc. However, IoT applications introduce grand security challenges due to the increase in the attack surface. Current security approaches do not handle cybersecurity from a holistic point of view; hence a systematic cybersecurity mechanism needs to be adopted when designing IoTbased applications. In this work, we present a risk management framework to deploy secure IoT-based applications for Smart Infrastructures at the design time and the runtime. At the design time, we propose a risk management method that is appropriate for smart infrastructures. At the design time, our framework relies on the Anomaly Behavior Analysis (ABA) methodology enabled by the Autonomic Computing paradigm and an intrusion detection system to detect any threat that can compromise IoT infrastructures by. Our preliminary experimental results show that our framework can be used to detect threats and protect IoT premises and services.
AB - The Internet of Things (IoT) connects not only computers and mobile devices, but it also interconnects smart buildings, homes, and cities, as well as electrical grids, gas, and water networks, automobiles, airplanes, etc. However, IoT applications introduce grand security challenges due to the increase in the attack surface. Current security approaches do not handle cybersecurity from a holistic point of view; hence a systematic cybersecurity mechanism needs to be adopted when designing IoTbased applications. In this work, we present a risk management framework to deploy secure IoT-based applications for Smart Infrastructures at the design time and the runtime. At the design time, we propose a risk management method that is appropriate for smart infrastructures. At the design time, our framework relies on the Anomaly Behavior Analysis (ABA) methodology enabled by the Autonomic Computing paradigm and an intrusion detection system to detect any threat that can compromise IoT infrastructures by. Our preliminary experimental results show that our framework can be used to detect threats and protect IoT premises and services.
KW - IoT
KW - anomaly behavior analysis
KW - cyber security
KW - risk management
KW - threat model
UR - http://www.scopus.com/inward/record.url?scp=85035245013&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85035245013&partnerID=8YFLogxK
U2 - 10.1109/FAS-W.2017.167
DO - 10.1109/FAS-W.2017.167
M3 - Conference contribution
AN - SCOPUS:85035245013
T3 - Proceedings - 2017 IEEE 2nd International Workshops on Foundations and Applications of Self* Systems, FAS*W 2017
SP - 324
EP - 328
BT - Proceedings - 2017 IEEE 2nd International Workshops on Foundations and Applications of Self* Systems, FAS*W 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd IEEE International Workshops on Foundations and Applications of Self* Systems, FAS*W 2017
Y2 - 18 September 2017 through 22 September 2017
ER -