BayesImposter: Bayesian Estimation Based .bss Imposter Attack on Industrial Control Systems

Abstract: Over the last six years, several papers used memory deduplication to trigger various security issues, such as leaking heap-address and causing bit-flip in the physical memory. The most essential requirement for successful memory deduplication is to provide identical copies of a physical page. Recent works use a brute-force approach to create identical copies of a physical page that is an inaccurate and time-consuming primitive from the attacker's perspective. Our work begins to fill this gap by providing a domain-specific structured way to duplicate a physical page in cloud settings in the context of industrial control systems (ICSs). Here, we show a new attack primitive - \textit{BayesImposter}, which points out that the attacker can duplicate the .bss section of the target control DLL file of cloud protocols using the \textit{Bayesian estimation} technique. Our approach results in less memory (i.e., 4 KB compared to GB) and time (i.e., 13 minutes compared to hours) compared to the brute-force approach used in recent works. We point out that ICSs can be expressed as state-space models; hence, the \textit{Bayesian estimation} is an ideal choice to be combined with memory deduplication for a successful attack in cloud settings. To demonstrate the strength of \textit{BayesImposter}, we create a real-world automation platform using a scaled-down automated high-bay warehouse and industrial-grade SIMATIC S7-1500 PLC from Siemens as a target ICS. We demonstrate that \textit{BayesImposter} can predictively inject false commands into the PLC that can cause possible equipment damage with machine failure in the target ICS. Moreover, we show that \textit{BayesImposter} is capable of adversarial control over the target ICS resulting in severe consequences, such as killing a person but making it looks like an accident. Therefore, we also provide countermeasures to prevent the attack.