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Yingao Elaine Yao

Publications and source records attributed to Yingao Elaine Yao.

2 recordsLinked to original sources

ICS-Sniper: A Targeted Blackhole Attack on Encrypted ICS Traffic

Modern industrial control systems (ICS) increasingly host their Supervisory Control and Data Acquisition (SCADA) services in the cloud to reduce the costs of large-scale automation. To protect site-SCADA communications, ICS operators commonly use VPN tunneling and standard security practices. We show that, despite these security measures, an on-path Internet adversary can disrupt ICS operations without infiltrating the ICS perimeter, breaking encryption, or knowledge of the control logic. We present ICS-Sniper, a targeted blackhole attack that analyzes the VPN traffic metadata (sizes, direction, timing of packets) to identify narrow time windows, called critical superperiods, during which the site-SCADA traffic would likely contain highly critical commands or data. Post-analysis, in a subsequent operational cycle, ICS-Sniper drops a small set of payload-carrying packets in the critical superperiods to disrupt the ICS's operations. We demonstrate three attacks on two realistic modern Secure Water Treatment (SWaT) plant testbeds that can potentially violate the operational safety of the ICS while evading state-of-the-art ICS attack detectors.

cs.CR↗

Framework for Discovering GPS Spoofing Attacks in Drone Swarms

Swarm robotics, particularly drone swarms, are used in various safety-critical tasks. While a lot of attention has been given to improving swarm control algorithms for improved intelligence, the security implications of various design choices in swarm control algorithms have not been studied. We highlight how an attacker can exploit the vulnerabilities in swarm control algorithms to disrupt drone swarms. Specifically, we show that the attacker can target a swarm member (target drone) through GPS spoofing attacks, and indirectly cause other swarm members (victim drones) to veer from their course, resulting in collisions. We call these Swarm Propagation Vulnerabilities (SPVs). In this paper, we introduce two fuzzing tools, SwarmFuzzGraph and SwarmFuzzBinary, to efficiently find SPVs in swarm control algorithms. SwarmFuzzGraph uses a combination of graph theory and gradient-guided optimization to find SPVs. Our evaluation on a popular swarm control algorithm shows that SwarmFuzzGraph achieves an average success rate of 48.8% in finding SPVs. However, SwarmFuzzGraph fails to find any SPVs in drone swarms with different topologies. We then propose SwarmFuzzBinary, which uses observation-based seed scheduling and binary search to find SPVs. The evaluation shows that SwarmFuzzBinary's success rate is comparable to SwarmFuzzGraph and work in all tested algorithms.

cs.CR↗