Abstract
The use of containers has become mainstream and ubiquitous in cloud environments. A container is a way to abstract processes and file systems into a single unit separate from the kernel. They provide a lightweight virtual environment that groups and isolates a set of processes and resources such as memory, CPU, disk, etc., from the host and any other containers. Docker is an example of container-based technologies for application containers. However, there are security issues that affect the widespread and confident usage of container platform. This paper proposes a model for a real-time intrusion detection system (IDS) that can be used to detect malicious applications running in Docker containers. Our IDS uses n-grams of system calls and the probability of occurrence of this n-gram is then calculated. Further the trace is processed using Maximum Likelihood Estimator (MLE) and Simple Good Turing (SGT) to provide a better estimation of unseen values of system call sequences. UNM dataset has been used to validate the approach and a comparison of the results obtained using MLE and SGT has been done. We got an accuracy ranging from 87–97% for different UNM datasets.
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Srinivasan, S., Kumar, A., Mahajan, M., Sitaram, D., Gupta, S. (2019). Probabilistic Real-Time Intrusion Detection System for Docker Containers. In: Thampi, S., Madria, S., Wang, G., Rawat, D., Alcaraz Calero, J. (eds) Security in Computing and Communications. SSCC 2018. Communications in Computer and Information Science, vol 969. Springer, Singapore. https://doi.org/10.1007/978-981-13-5826-5_26
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DOI: https://doi.org/10.1007/978-981-13-5826-5_26
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