In this paper, we address the problem of fingerprinting microservices in a unified, efficient, accurate and non-intrusive fashion.
To achieve accurate fingerprinting, we design a supervised. Bayesian learning model called a fingerprint model that clas- sifies the sequences of system calls ...
In this paper, we address the problem of fingerprinting microservices in a unified, efficient, accurate and non-intrusive fashion.
Sidecars monitor individual services and relay the information to the service mesh control plane to make dynamic decisions at runtime.
We proposed a technique for automatically determining the architecture of a CNN model adaptive to fingerprint classification.
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This paper proposes using machine learning techniques, as part of the service discovery process, to select microservice instances in a given context.
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This paper presents a comprehensive approach to evaluate and compare different intrusion detection approaches for microservice applications.
Aug 30, 2021 · This paper proposes using machine learning techniques, as part of the service discovery process, to select microservice instances in a given context.
Aug 26, 2021 · In this project, we identify real fingerprints pattern and classify them with convolutional neural networks(CNN).
Missing: Microservice | Show results with:Microservice
ML-NetLang outperforms comparable state-of-the-art techniques using behavioral-based, correlation-based, and machine-learning solutions. Index Terms— ...
Missing: Microservice | Show results with:Microservice