Due to the emergent adoption of distributed systems when building applications, demand for reliability and availability has increased. These properties can be achieved through replication techniques using middleware algorithms that must be capable of tolerating faults. Certain faults such as arbitrary faults, however, may be more difficult to tolerate, resulting in more complex and resource intensive algorithms that end up being not so practical to use. We propose and experiment with the use of consistency validation techniques to harden a benign fault-tolerant Paxos, thus being able to detect and tolerate non-malicious arbitrary faults.
In @cite_10 , although it discusses several concepts on detecting arbitrary faults, it only implements semantic checks. This is similar to part of our approach, with a low coverage because the checks are implemented only at the application layer.
@cite_10
4564113 2755542034 2788535427 2146070389
Abstract : In this report, we study tolerance of semantic faults, one of the crucial Issues In the Simplex(TM) architecture. In particular, we examine semantic faults that cause the controlled device to be unsafe (i.e., unable to carry out its normal operation) and eventually cause the device to become damaged. We also consider fault detection as a safety check. For the class of control systems operating around an equilibrium, the objective of maintaining the safety of the controlled device is formulated as a stabilization problem, and the safety of the controlled device is tested against the stability region of the device under the safety control. To establish the stability region, we apply the Lyapunov stability theory and linear matrix inequality (LMI) methodologies. It is shown that the stability region for a given safety controller as well as a safety control law can be systematically derived by LMI based approaches. We conclude the report with a summary of the procedure for deriving the safety check and safety controller for a given application. In this paper, we study the task of detecting semantic parts of an object. This is very important in computer vision, as it provides the possibility to parse an object as human do, and helps us better understand object detection algorithms. Also, detecting semantic parts is very challenging especially when the parts are partially or fully occluded. In this scenario, the popular proposal-based methods like Faster-RCNN often produce unsatisfactory results, because both the proposal extraction and classification stages may be confused by the irrelevant occluders. To this end, we propose a novel detection framework, named DeepVoting, which accumulates local visual cues, called visual concepts (VC), to locate the semantic parts. Our approach involves adding two layers after the intermediate outputs of a deep neural network. The first layer is used to extract VC responses, and the second layer performs a voting mechanism to capture the spatial relationship between VC's and semantic parts. The benefit is that each semantic part is supported by multiple VC's. Even if some of the supporting VC's are missing due to occlusion, we can still infer the presence of the target semantic part using the remaining ones. To avoid generating an exponentially large training set to cover all occlusion cases, we train our model without seeing occlusion and transfer the learned knowledge to deal with occlusions. This setting favors learning the models which are naturally robust and adaptive to occlusions instead of over-fitting the occlusion patterns in the training data. In experiments, DeepVoting shows significantly better performance on semantic part detection in occlusion scenarios, compared with Faster-RCNN, with one order of magnitude fewer parameters and 2.5x testing speed. In addition, DeepVoting is explainable as the detection result can be diagnosed via looking up the voted VC's. Abstract This paper aims at designing a diagnosis tool that shall be used to support experts for detecting and localizing faults in a sensor grid of a building system. It is a tool-aided diagnosis with mathematical models and reasoning tools that determines whether a sensor is faulty or not. It is based on detection tests and logical diagnosis analysis for the first principle. At the beginning, a succinct state of art is provided for existing fault detection and diagnosis (FDD) methods. Then, the diagnosis algorithm is proposed: it deals with a BRIDGE approach of FDD for a building system focusing on sensor grids. Sensor faults are diagnosed thanks to detection tests and diagnosis first principle. In addition, this approach provides the possible fault modes including multiple sensor faults. Finally, a series of tests are performed in order to validate the approach. An application example shows the efficiency of the proposed technique: an office setting at Grenoble Institute of Technology. So far many software countermeasures against fault attacks have been proposed. However, most of them are tailored to a specific cryptographic algorithm or focus on securing the processed data only. In this work we present a generic and elegant approach by using a highly fault secure algebraic structure. This structure is compatible to finite fields and rings and preserves its error detection property throughout addition and multiplication. Additionally, we introduce a method to generate a fingerprint of the instruction sequence. Thus, it is possible to check the result for data corruption as well as for modifications in the program flow. This is even possible if the order of the instructions is randomized. Furthermore, the properties of the countermeasure allow the deployment of error detection as well as error diffusion. We point out that the overhead for the calculations and for the error checking within this structure is reasonable and that the transformations are efficient. In addition we discuss how our approach increases the security in various kinds of fault scenarios.