TY - JOUR
T1 - Active Anomaly Detection in Heterogeneous Processes
AU - Huang, Boshuang
AU - Cohen, Kobi
AU - Zhao, Qing
N1 - Funding Information:
This work was supported in part by the National Science Foundation under Grant CCF-1815559 and in part by the Army Research Office under Grant W911NF-17-1-0464. K. Cohen was supported in part by the Cyber Security Research Center at Ben-Gurion University of the Negev and in part by the U.S.-Israel Binational Science Foundation under Grant 2017723.
Funding Information:
Manuscript received June 27, 2017; revised August 3, 2018; accepted August 10, 2018. Date of publication August 21, 2018; date of current version March 15, 2019. This work was supported in part by the National Science Foundation under Grant CCF-1815559 and in part by the Army Research Office under Grant W911NF-17-1-0464. K. Cohen was supported in part by the Cyber Security Research Center at Ben-Gurion University of the Negev and in part by the U.S.-Israel Binational Science Foundation under Grant 2017723. This paper was presented at the 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, April.
Publisher Copyright:
© 2018 IEEE.
PY - 2019/4/1
Y1 - 2019/4/1
N2 - An active inference problem of detecting anomalies among heterogeneous processes is considered. At each time, a subset of processes can be probed. The objective is to design a sequential probing strategy that dynamically determines which processes to observe at each time and when to terminate the search so that the expected detection time is minimized under a constraint on the probability of misclassifying any process. This problem falls into the general setting of sequential design of experiments pioneered by Chernoff in 1959, in which a randomized strategy, referred to as the Chernoff test, was proposed and shown to be asymptotically optimal as the error probability approaches zero. For the problem considered in this paper, a low-complexity deterministic test is shown to enjoy the same asymptotic optimality while offering significantly better performance in the finite regime and faster convergence to the optimal rate function, especially when the number of processes is large. Furthermore, the proposed test offers considerable reduction in computation complexity.
AB - An active inference problem of detecting anomalies among heterogeneous processes is considered. At each time, a subset of processes can be probed. The objective is to design a sequential probing strategy that dynamically determines which processes to observe at each time and when to terminate the search so that the expected detection time is minimized under a constraint on the probability of misclassifying any process. This problem falls into the general setting of sequential design of experiments pioneered by Chernoff in 1959, in which a randomized strategy, referred to as the Chernoff test, was proposed and shown to be asymptotically optimal as the error probability approaches zero. For the problem considered in this paper, a low-complexity deterministic test is shown to enjoy the same asymptotic optimality while offering significantly better performance in the finite regime and faster convergence to the optimal rate function, especially when the number of processes is large. Furthermore, the proposed test offers considerable reduction in computation complexity.
KW - Active hypothesis testing
KW - anomaly detection
KW - dynamic search
KW - sequential design of experiments
KW - target whereabout
UR - http://www.scopus.com/inward/record.url?scp=85052697066&partnerID=8YFLogxK
U2 - 10.1109/TIT.2018.2866257
DO - 10.1109/TIT.2018.2866257
M3 - Article
AN - SCOPUS:85052697066
SN - 0018-9448
VL - 65
SP - 2284
EP - 2301
JO - IEEE Transactions on Information Theory
JF - IEEE Transactions on Information Theory
IS - 4
M1 - 8443436
ER -