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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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COMPARISON OF DESIGN- AND DATACENTRIC METHODS FOR DISTRIBUTED ATTACK DETECTION IN CYBERPHYSICAL SYSTEMS
Cyber-physical systems are vulnerable to a variety of cyber, physical and cyber-physical attacks. The security of cyber-physical systems can be enhanced beyond what can be achieved through firewalls and trusted components by building trust from observed and/or expected behaviors. These behaviors can be encoded as invariants. Information flows that do not satisfy the invariants are used to identify and isolate malfunctioning devices and cyber intrusions. However, the distributed architectures of cyber-physical systems often contain multiple access points that are physically and/or digitally linked. Thus, invariants may be difficult to determine and/or computationally prohibitive to check in real time. Researchers have employed various methods for determining the invariants by analyzing the designs of and/or data generated by cyber-physical systems such as water treatment plants and electric power grids. This chapter compares the effectiveness of detecting attacks on a water treatment plant using design-centric invariants versus data-centric rules, the latter generated using a variety of data mining methods. The methods are compared based on the maximization of true positives and minimization of false positives.
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- Award ID(s):
- 1837472
- PAR ID:
- 10190268
- Date Published:
- Journal Name:
- Critical Infrastructure Protection XIV
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Reconnaissance is critical for adversaries to prepare attacks causing physical damage in industrial control systems (ICS) like smart power grids. Disrupting the 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 those challenges, we propose physical function virtualization (PFV) that ``hooks'' network interactions with real physical devices and uses them to build lightweight virtual nodes following the actual implementation of network stacks, system invariants, and physical state variations of real devices. On top of PFV, we propose DefRec, a defense mechanism that significantly increases the reconnaissance efforts for adversaries to obtain the knowledge of power grids' cyber-physical infrastructures. By randomizing communications and crafting decoy data for the virtual physical 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, which uses real devices from different vendors and HP physical switches to simulate six power grids. The experiment results show that with negligible overhead, PFV can accurately follow the behavior of real devices. DefRec can significantly delay passive attacks for at least five months and isolate proactive attacks with less than $$10^{-30}$$ false negatives.more » « less
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