TY - GEN
T1 - A new burst-DFA model for SCADA anomaly detection
AU - Markman, Chen
AU - Wool, Avishai
AU - Cardenas, Alvaro A.
N1 - Publisher Copyright: © 2017 ACM.
PY - 2017/11/3
Y1 - 2017/11/3
N2 - In Industrial Control Systems (ICS/SCADA), machine to machine data traffic is highly periodic. Past work showed that in many cases, it is possible to model the traffic between each individual Programmable Logic Controller (PLC) and the SCADA server by a cyclic Deterministic Finite Automaton (DFA), and to use the model to detect anomalies in the traffic. However, a recent analysis of network traffic in a water facility in the U.S, showed that cyclic-DFA models have limitations. In our research, we examine the same data corpus; our study shows that the communication on all of the channels in the network is done in bursts of packets, and that the bursts have semantic meaning-the order within a burst depends on the messages. Using these observations, we suggest a new burst- DFA model that .ts the data much be.er than previous work. Our model treats the traffic on each channel as a series of bursts, and matches each burst to the DFA, taking the burst's beginning and end into account. Our burst-DFA model successfully explains between 95% and 99% of the packets in the data-corpus, and goes a long way toward the construction of a practical anomaly detection system.
AB - In Industrial Control Systems (ICS/SCADA), machine to machine data traffic is highly periodic. Past work showed that in many cases, it is possible to model the traffic between each individual Programmable Logic Controller (PLC) and the SCADA server by a cyclic Deterministic Finite Automaton (DFA), and to use the model to detect anomalies in the traffic. However, a recent analysis of network traffic in a water facility in the U.S, showed that cyclic-DFA models have limitations. In our research, we examine the same data corpus; our study shows that the communication on all of the channels in the network is done in bursts of packets, and that the bursts have semantic meaning-the order within a burst depends on the messages. Using these observations, we suggest a new burst- DFA model that .ts the data much be.er than previous work. Our model treats the traffic on each channel as a series of bursts, and matches each burst to the DFA, taking the burst's beginning and end into account. Our burst-DFA model successfully explains between 95% and 99% of the packets in the data-corpus, and goes a long way toward the construction of a practical anomaly detection system.
UR - http://www.scopus.com/inward/record.url?scp=85037147693&partnerID=8YFLogxK
U2 - https://doi.org/10.1145/3140241.3140245
DO - https://doi.org/10.1145/3140241.3140245
M3 - منشور من مؤتمر
T3 - CPS-SPC 2017 - Proceedings of the 2017 Workshop on Cyber-Physical Systems Security and PrivaCy, co-located with CCS 2017
SP - 1
EP - 12
BT - CPS-SPC 2017 - Proceedings of the 2017 Workshop on Cyber-Physical Systems Security and PrivaCy, co-located with CCS 2017
T2 - 3rd ACM Workshop on Cyber-Physical Systems Security and PrivaCy, CPS-SPC 2017
Y2 - 3 November 2017
ER -
