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1. Distributed Corruption Detection in Networks

   https://doi.org/10.4086/toc.2020.v016a001  

   Alon, Noga; Mossel, Elchanan; Pemantle, Robin (January 2020, Theory of Computing)

   We consider the problem of distributed corruption detection in networks. In this model each node of a directed graph is either truthful or corrupt. Each node reports the type (truthful or corrupt) of each of its outneighbors. If it is truthful, it reports the truth, whereas if it is corrupt, it reports adversarially. This model, first considered by Preparata, Metze and Chien in 1967, motivated by the desire to identify the faulty components of a digital system by having the other components checking them, became known as the PMC model. The main known results for this model characterize networks in which all corrupt (that is, faulty) nodes can be identified, when there is a known upper bound on their number. We are interested in networks in which a large fraction of the nodes can be classified. It is known that in the PMC model, in order to identify all corrupt nodes when their number is t, all in-degrees have to be at least t. In contrast, we show that in d regular-graphs with strong expansion properties, a 1 - O(1/d) fraction of the corrupt nodes, and a 1 - O(1/d) fraction of the truthful nodes can be identified, whenever there is a majority of truthful nodes. We also observe that if the graph is very far from being a good expander, namely, if the deletion of a small set of nodes splits the graph into small components, then no corruption detection is possible even if most of the nodes are truthful. Finally we discuss the algorithmic aspects and the computational hardness of the problem. 

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2. Exploring Data Corruption Inside SZ

   https://doi.org/10.1109/BigData55660.2022.10020891  

   Shan, Ruiwen; Calhoun, Jon C. (December 2022, 2022 IEEE International Conference on Big Data (Big Data))
3. Corruption-robust offline reinforcement learning

   Zhang, Xuezhou; Chen, Yiding; Zhu, Xiaojin; Sun, Wen (January 2022, The 25th International Conference on Artificial Intelligence and Statistics)
4. Exploiting Memory Corruption Vulnerabilities in Connman for IoT Devices

   https://doi.org/10.1109/DSN.2019.00036  

   English, K. Virgil; Obaidat, Islam; Sridhar, Meera (June 2019, 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN))

   In the recent past, there has been a rapid increase in attacks on consumer Internet-of-Things (IoT) devices. Several attacks currently focus on easy targets for exploitation, such as weak configurations (weak default passwords). However, with governments, industries, and organizations proposing new laws and regulations to reduce and prevent such easy targets in the IoT space, attackers will move to more subtle exploits in these devices. Memory corruption vulnerabilities are a significant class of vulnerabilities in software security through which attackers can gain control of the entire system. Numerous memory corruption vulnerabilities have been found in IoT firmware already deployed in the consumer market. This paper presents an approach for exploiting stack-based buffer-overflow attacks in IoT firmware, to hijack the device remotely. To show the feasibility of this approach, we demonstrate exploiting a common network software application, Connman, used widely in IoT firmware such as Samsung smart TVs. A series of experiments are reported on, including: crashing and executing arbitrary code in the targeted software application in a controlled environment, adopting the attacks in uncontrolled environments (with standard software defenses such as W⊕X and ASLR enabled), and installing publicly available IoT firmware that uses this software application on a Raspberry Pi. The presented exploits demonstrate the ease in which an adversary can control IoT devices. 

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5. Corruption Detection in Networks of Bi-directional Dynamical Systems

   https://doi.org/10.1109/CDC40024.2019.9029971  

   Subramanian, Venkat Ram; Lamperski, Andrew; Salapaka, Murti V. (December 2019, Conference on Decision and Control)