TY - JOUR
T1 - Illiad
T2 - InteLLigent invariant and anomaly detection in cyber-physical systems
AU - Muralidhar, Nikhil
AU - Wang, Chen
AU - Self, Nathan
AU - Momtazpour, Marjan
AU - Nakayama, Kiyoshi
AU - Sharma, Ratnesh
AU - Ramakrishnan, Naren
N1 - Publisher Copyright:
© 2018 ACM.
PY - 2018/2
Y1 - 2018/2
N2 - Cyber-physical systems (CPSs) are today ubiquitous in urban environments. Such systems now serve as the backbone to numerous critical infrastructure applications, from smart grids to IoT installations. Scalable and seamless operation of such CPSs requires sophisticated tools for monitoring the time series progression of the system, dynamically tracking relationships, and issuing alerts about anomalies to operators. We present an online monitoring system (illiad) that models the state of the CPS as a function of its relationships between constituent components, using a combination of model-based and data-driven strategies. In addition to accurate inference for state estimation and anomaly tracking, illiad also exploits the underlying network structure of the CPS (wired or wireless) for state estimation purposes. We demonstrate the application of illiad to two diverse settings: a wireless sensor motes application and an IEEE 33-bus microgrid.
AB - Cyber-physical systems (CPSs) are today ubiquitous in urban environments. Such systems now serve as the backbone to numerous critical infrastructure applications, from smart grids to IoT installations. Scalable and seamless operation of such CPSs requires sophisticated tools for monitoring the time series progression of the system, dynamically tracking relationships, and issuing alerts about anomalies to operators. We present an online monitoring system (illiad) that models the state of the CPS as a function of its relationships between constituent components, using a combination of model-based and data-driven strategies. In addition to accurate inference for state estimation and anomaly tracking, illiad also exploits the underlying network structure of the CPS (wired or wireless) for state estimation purposes. We demonstrate the application of illiad to two diverse settings: a wireless sensor motes application and an IEEE 33-bus microgrid.
KW - Big-data
KW - IoT
KW - State-estimation
KW - Urban computing
KW - Urban informatics
UR - http://www.scopus.com/inward/record.url?scp=85042467387&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85042467387&partnerID=8YFLogxK
U2 - 10.1145/3066167
DO - 10.1145/3066167
M3 - Article
AN - SCOPUS:85042467387
SN - 2157-6904
VL - 9
JO - ACM Transactions on Intelligent Systems and Technology
JF - ACM Transactions on Intelligent Systems and Technology
IS - 3
M1 - 35
ER -