Sekar, R., Bendre, M., Dhurjati, D., Bollineni P.: A fast automaton based method for detecting anomalous program behaviors. Maggi, F., Matteucci, M., Zanero, S.: Detecting intrusions through system call sequence and argument analysis. In: The Annual Computer Security Applications Conference, Piscataway, NJ, pp. Keywordsįorrest, S., Hofmeyr, S., Somayaji, A.: The evolution of system call collecting. The results demonstrate that Midiag can well classify these applications as different types and accurately diagnose the injected faults. We have validated Midiag with many different types of applications deployed in containers. The GRU-based neural network is employed to model the patterns of system call sequences to predict the next system call, and thus we can diagnose faults by comparing the predicted next system call and the actual next one in the specific pattern. Midiag collects system calls with a non-invasive lightweight tool, and then uses k-means to cluster system call sequences as patterns with the longest common subsequence. To deal with the above problem, this paper proposes a sequential trace-based fault diagnosis framework called as Midiag by mining the patterns of microservices’ system call sequences. However, since a cloud application often has a large amount of different microservices, it is difficult for operators to analyze these microservices with a unified model.
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