On Cyber-Physical Forensics, Attacks, and Defenses
2019-12-06T15:26:22Z (GMT) by
Cyber-physical systems, through various sensors and actuators, are used to handle interactions of the cyber-world with the physical-world. Conventionally, the temporal component of the physical-world has been used only for estimating real-time deadlines and responsiveness of control-loop algorithms. However, there are various other applications where the relationship of the temporal component and the cyber-world are of interest. An example is the ability to reconstruct a sequence of past temporal activities from the current state of the cyber-world, which is of obvious interest to cyber-forensic investigators. Another example is the ability to control the temporal components in broadcast communication networks, which leads to new attack and defense capabilities. These relationships have not been explored traditionally.
To address this gap, this dissertation proposes three systems that cast light on the effect of temporal component of the physical-world on the cyber-world. First, we present Timeliner, a smartphone cyber-forensics technique that recovers past actions from a single static memory image. Following that, we present work on CAN (Controller Area Network), a broadcast communication network used in automotive applications. We show in DUET that the ability to control communication temporally allows two compromised ECUs, an attacker and an accomplice, to stealthily suppress and impersonate a victim ECU, even in the presence of a voltage-based intrusion detection system. In CANDID, we show that the ability to temporally control CAN communication opens up new defensive capabilities that make the CAN much more secure.
The evaluation results show that Timeliner is very accurate and can reveal past evidence (up to an hour) of user actions across various applications on Android devices. The results also show that DUET is highly effective at impersonating victim ECUs while evading both message-based and voltage-based intrusion detection systems, irrespective of the features and the training algorithms used. Finally, CANDID is able to provide new defensive capabilities to CAN environments with reasonable communication and computational overheads.