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Zhou, Qian, Dai, Hua, Liu, Liang, Shi, Kai, Chen, Jie, Jiang, Hong.  2022.  The final security problem in IOT: Don’t count on the canary!. 2022 7th IEEE International Conference on Data Science in Cyberspace (DSC). :599–604.
Memory-based vulnerabilities are becoming more and more common in low-power and low-cost devices in IOT. We study several low-level vulnerabilities that lead to memory corruption in C and C++ programs, and how to use stack corruption and format string attack to exploit these vulnerabilities. Automatic methods for resisting memory attacks, such as stack canary and address space layout randomization ASLR, are studied. These methods do not need to change the source program. However, a return-oriented programming (ROP) technology can bypass them. Control flow integrity (CFI) can resist the destruction of ROP technology. In fact, the security design is holistic. Finally, we summarize the rules of security coding in embedded devices, and propose two novel methods of software anomaly detection process for IOT devices in the future.
Peng, Jiang, Jiang, Wendong, Jiang, Hong, Ge, Huangxu, Gong, Peilin, Luo, Lingen.  2022.  Stochastic Vulnerability Analysis methodology for Power Transmission Network Considering Wind Generation. 2022 Power System and Green Energy Conference (PSGEC). :85–90.
This paper proposes a power network vulnerability analysis method based on topological approach considering of uncertainties from high-penetrated wind generations. In order to assess the influence of the impact of wind generation owing to its variable wind speed etc., the Quasi Monte Carlo based probabilistic load flow is adopted and performed. On the other hand, an extended stochastic topological vulnerability method involving Complex Network theory with probabilistic load flow is proposed. Corresponding metrics, namely stochastic electrical betweenness and stochastic net-ability are proposed respectively and applied to analyze the vulnerability of power network with wind generations. The case study of CIGRE medium voltage benchmark network is performed for illustration and evaluation. Furthermore, a cascading failures model considering the stochastic metrics is also developed to verify the effectiveness of proposed methodology.