• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.3888-3910
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• 2018

As an increasing number of defense methods against control-data attacks are deployed in practice, control-data attacks have become challenging, and non-control-data attacks are on the rise. However, defense methods against non-control-data attacks are still deficient even though these attacks can produce damage as significant as that of control-data attacks. We present a method to defend against non-control-data attacks using influence domain monitoring (IDM). A definition of the data influence domain is first proposed to describe the characteristics of a variable during its life cycle. IDM extracts security-critical non-control data from the target program and then instruments the target for monitoring these variables' influence domains to ensure that corrupted variables will not be used as the attackers intend. Therefore, attackers may be able to modify the value of one security-critical variable by exploiting certain memory corruption vulnerabilities, but they will be prevented from using the variable for nefarious purposes. We evaluate a prototype implementation of IDM and use the experimental results to show that this method can defend against most known non-control-data attacks while imposing a moderate amount of performance overhead.

• Nuclear Engineering and Technology
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• v.54 no.6
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• pp.2031-2036
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• 2022

Energetic particle strikes the device and induces data corruption in the configuration memory (CRAM), causing errors and even malfunctions in a system on chip (SoC). Software-based fault injection is a convenient way to assess device performance. In this paper, dynamic partial reconfiguration (DPR) is adopted to make fault injection on a Xilinx 16 nm FinFET Ultrascale+ MPSoC. And the reconfiguration module implements the Sobel and Gaussian image filtering, respectively. Fault injections are executed on the static and reconfiguration modules' bitstreams, respectively. Another contribution is that the failure modes and effects analysis (FMEA) method is applied to evaluate the system reliability, according to the obtained injection results. This paper proposes a software-based solution to estimate programmable device vulnerability.