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Documents authored by Hasan, Monowar

Document
DOI: 10.4230/LIPIcs.ECRTS.2024.13
DeepTrust^RT: Confidential Deep Neural Inference Meets Real-Time!

Authors: Mohammad Fakhruddin Babar and Monowar Hasan

Published in: LIPIcs, Volume 298, 36th Euromicro Conference on Real-Time Systems (ECRTS 2024)

Abstract
Deep Neural Networks (DNNs) are becoming common in "learning-enabled" time-critical applications such as autonomous driving and robotics. One approach to protect DNN inference from adversarial actions and preserve model privacy/confidentiality is to execute them within trusted enclaves available in modern processors. However, running DNN inference inside limited-capacity enclaves while ensuring timing guarantees is challenging due to (a) large size of DNN workloads and (b) extra switching between "normal" and "trusted" execution modes. This paper introduces new time-aware scheduling schemes - DeepTrust^RT - to securely execute deep neural inferences for learning-enabled real-time systems. We first propose a variant of EDF (called DeepTrust^RT-LW) that slices each DNN layer and runs them sequentially in the enclave. However, due to extra context switch overheads of individual layer slices, we further introduce a novel layer fusion technique (named DeepTrust^RT-FUSION). Our proposed scheme provides hard real-time guarantees by fusing multiple layers of DNN workload from multiple tasks; thus allowing them to fit and run concurrently within the enclaves while maintaining real-time guarantees. We implemented and tested DeepTrust^RT ideas on the Raspberry Pi platform running OP-TEE+DarkNet-TZ DNN APIs and three DNN workloads (AlexNet-squeezed, Tiny Darknet, YOLOv3-tiny). Compared to the layer-wise partitioning approach (DeepTrust^RT-LW), DeepTrust^RT-FUSION can schedule up to 3x more tasksets and reduce context switches by up to 11.12x. We further demonstrate the efficacy of DeepTrust^RT using a flight controller (ArduPilot) case study and find that DeepTrust^RT-FUSION retains real-time guarantees where DeepTrust^RT-LW becomes unschedulable.
Cite as

Mohammad Fakhruddin Babar and Monowar Hasan. DeepTrust^RT: Confidential Deep Neural Inference Meets Real-Time!. In 36th Euromicro Conference on Real-Time Systems (ECRTS 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 298, pp. 13:1-13:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{babar_et_al:LIPIcs.ECRTS.2024.13,
  author =	{Babar, Mohammad Fakhruddin and Hasan, Monowar},
  title =	{{DeepTrust^RT: Confidential Deep Neural Inference Meets Real-Time!}},
  booktitle =	{36th Euromicro Conference on Real-Time Systems (ECRTS 2024)},
  pages =	{13:1--13:24},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-324-9},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{298},
  editor =	{Pellizzoni, Rodolfo},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECRTS.2024.13},
  URN =		{urn:nbn:de:0030-drops-203161},
  doi =		{10.4230/LIPIcs.ECRTS.2024.13},
  annote =	{Keywords: DNN, TrustZone, Real-Time Systems}
}
Document
DOI: 10.4230/LIPIcs.ECRTS.2017.23
Contego: An Adaptive Framework for Integrating Security Tasks in Real-Time Systems

Authors: Monowar Hasan, Sibin Mohan, Rodolfo Pellizzoni, and Rakesh B. Bobba

Published in: LIPIcs, Volume 76, 29th Euromicro Conference on Real-Time Systems (ECRTS 2017)

Abstract
Embedded real-time systems (RTS) are pervasive. Many modern RTS are exposed to unknown security flaws, and threats to RTS are growing in both number and sophistication. However, until recently, cyber-security considerations were an afterthought in the design of such systems. Any security mechanisms integrated into RTS must (a) co-exist with the real-time tasks in the system and (b) operate without impacting the timing and safety constraints of the control logic. We introduce Contego, an approach to integrating security tasks into RTS without affecting temporal requirements. Contego is specifically designed for legacy systems, viz., the real-time control systems in which major alterations of the system parameters for constituent tasks is not always feasible. Contego combines the concept of opportunistic execution with hierarchical scheduling to maintain compatibility with legacy systems while still providing flexibility by allowing security tasks to operate in different modes. We also define a metric to measure the effectiveness of such integration. We evaluate Contego using synthetic workloads as well as with an implementation on a realistic embedded platform (an open-source ARM CPU running real-time Linux).
Cite as

Monowar Hasan, Sibin Mohan, Rodolfo Pellizzoni, and Rakesh B. Bobba. Contego: An Adaptive Framework for Integrating Security Tasks in Real-Time Systems. In 29th Euromicro Conference on Real-Time Systems (ECRTS 2017). Leibniz International Proceedings in Informatics (LIPIcs), Volume 76, pp. 23:1-23:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)

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@InProceedings{hasan_et_al:LIPIcs.ECRTS.2017.23,
  author =	{Hasan, Monowar and Mohan, Sibin and Pellizzoni, Rodolfo and Bobba, Rakesh B.},
  title =	{{Contego: An Adaptive Framework for Integrating Security Tasks in Real-Time Systems}},
  booktitle =	{29th Euromicro Conference on Real-Time Systems (ECRTS 2017)},
  pages =	{23:1--23:22},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-037-8},
  ISSN =	{1868-8969},
  year =	{2017},
  volume =	{76},
  editor =	{Bertogna, Marko},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECRTS.2017.23},
  URN =		{urn:nbn:de:0030-drops-71728},
  doi =		{10.4230/LIPIcs.ECRTS.2017.23},
  annote =	{Keywords: Real-Time Systems, Security, Hierarchical Scheduling}
}
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