Abstract
In the modern applications for Internet-of-Things (IoT) and Cyber-Physical Systems (CPSs) heterogeneous embedded devices exchange high volumes of data. Interconnection with cloud services is becoming popular. Thus, enhanced security is imperative but network monitoring is computational intensive. Parallel programming utilizing Graphics Processing Units (GPUs) is a well-tried practice for drastically reducing the computation time in computation intensive domains. This paper presents CloudNet – a lightweight and efficient GPU-accelerated anti-malware engine, utilizing the CUDA General Purpose GPU (GPGPU). The core of the system computes the digests of files using a CUDA-optimized SHA-3 hashing mechanism. Malware digests are stored in a data structure so that detection checks take place as network traffic is processed. Work includes a comparative analysis for three types of data structures (hash table, tree, and array) to identify the most appropriate for this specific field. We develop several versions of two basic variations of applications, including performance comparisons of GPU-accelerated implementation to the reference and optimized CPU implementations. The CloudNet is developed in order to protect CPSs that communicate information to the industrial cloud. A trace of an industrial wind park traffic is utilized for the evaluation of CloudNet, achieving two times faster network monitoring than typical CPU solutions.
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Acknowledgment
This work has received funding from the European Union Horizon’s 2020 research and innovation programme under grant agreement No. 780315 (SEMIoTICS). The authors would also like to thank the network engineers maintaining the subject wind park in Brande, Denmark for their valuable input in interpreting the network traces.
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Hatzivasilis, G., Fysarakis, K., Askoxylakis, I., Bilanakos, A. (2019). CloudNet Anti-malware Engine: GPU-Accelerated Network Monitoring for Cloud Services. In: Fournaris, A., Lampropoulos, K., Marín Tordera, E. (eds) Information and Operational Technology Security Systems. IOSec 2018. Lecture Notes in Computer Science(), vol 11398. Springer, Cham. https://doi.org/10.1007/978-3-030-12085-6_11
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