TY - GEN
T1 - DefRec
T2 - 27th Annual Network and Distributed System Security Symposium, NDSS 2020
AU - Lin, Hui
AU - Zhuang, Jianing
AU - Hu, Yih Chun
AU - Zhou, Huayu
N1 - Publisher Copyright:
© 2020 27th Annual Network and Distributed System Security Symposium, NDSS 2020. All Rights Reserved.
PY - 2020
Y1 - 2020
N2 - Reconnaissance is critical for adversaries to prepare attacks causing physical damage in industrial control systems (ICS) like smart power grids. Disrupting reconnaissance is challenging. The state-of-the-art moving target defense (MTD) techniques based on mimicking and simulating system behaviors do not consider the physical infrastructure of power grids and can be easily identified. To overcome these challenges, we propose physical function virtualization (PFV) that “hooks” network interactions with real physical devices and uses these real devices to build lightweight virtual nodes that follow the actual implementation of network stacks, system invariants, and physical state variations in the real devices. On top of PFV, we propose DefRec, a defense mechanism that significantly increases the effort required for an adversary to infer the knowledge of power grids' cyber-physical infrastructures. By randomizing communications and crafting decoy data for virtual nodes, DefRec can mislead adversaries into designing damage-free attacks. We implement PFV and DefRec in the ONOS network operating system and evaluate them in a cyber-physical testbed, using real devices from different vendors and HP physical switches to simulate six power grids. The experimental results show that with negligible overhead, PFV can accurately follow the behavior of real devices. DefRec can delay adversaries' reconnaissance for more than 100 years by adding a number of virtual nodes less than or equal to 20% of the number of real devices.
AB - Reconnaissance is critical for adversaries to prepare attacks causing physical damage in industrial control systems (ICS) like smart power grids. Disrupting reconnaissance is challenging. The state-of-the-art moving target defense (MTD) techniques based on mimicking and simulating system behaviors do not consider the physical infrastructure of power grids and can be easily identified. To overcome these challenges, we propose physical function virtualization (PFV) that “hooks” network interactions with real physical devices and uses these real devices to build lightweight virtual nodes that follow the actual implementation of network stacks, system invariants, and physical state variations in the real devices. On top of PFV, we propose DefRec, a defense mechanism that significantly increases the effort required for an adversary to infer the knowledge of power grids' cyber-physical infrastructures. By randomizing communications and crafting decoy data for virtual nodes, DefRec can mislead adversaries into designing damage-free attacks. We implement PFV and DefRec in the ONOS network operating system and evaluate them in a cyber-physical testbed, using real devices from different vendors and HP physical switches to simulate six power grids. The experimental results show that with negligible overhead, PFV can accurately follow the behavior of real devices. DefRec can delay adversaries' reconnaissance for more than 100 years by adding a number of virtual nodes less than or equal to 20% of the number of real devices.
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U2 - 10.14722/ndss.2020.24365
DO - 10.14722/ndss.2020.24365
M3 - Conference contribution
AN - SCOPUS:85138844551
T3 - 27th Annual Network and Distributed System Security Symposium, NDSS 2020
BT - 27th Annual Network and Distributed System Security Symposium, NDSS 2020
PB - The Internet Society
Y2 - 23 February 2020 through 26 February 2020
ER -