7.7 Toward Correct and Secure Embedded Systems

Time	Label	Presentation Title Authors
14:30	7.7.1	(Best Paper Award Candidate) BETTER LATE THAN NEVER VERIFICATION OF EMBEDDED SYSTEMS AFTER DEPLOYMENT Authors: Martin Ring¹, Fritjof Bornebusch¹, Christoph Lüth¹, Robert Wille² and Rolf Drechsler¹ ¹University of Bremen, DE; ²Johannes Kepler University Linz, AT Abstract This paper investigates the benefits of verifying embedded systems after deployment. We argue that one reason for the huge state spaces of contemporary embedded and cyber-physical systems is the large variety of operating contexts, which are unknown during design. Once the system is deployed, these contexts become observable, confining several variables. By this, the search space is dramatically reduced, making verification possible even on the limited resources of a deployed system. In this paper, we propose a design and verification flow which exploits this observation. We show how specifications are transferred to the deployed system and verified there. Evaluations on a number of case studies demonstrate the reduction of the search space, and we sketch how the proposed approach can be employed in practice. Download Paper (PDF; Only available from the DATE venue WiFi)
15:00	7.7.2	EFFICIENT COMPUTATION OF DEADLINE-MISS PROBABILITY AND POTENTIAL PITFALLS Speaker: Kuan-Hsun Chen, TU Dortmund, DE Authors: Kuan-Hsun Chen, Niklas Ueter, Georg von der Brüggen and Jian-Jia Chen, Technical University of Dortmund, DE Abstract In soft real-time systems, applications can tolerate rare deadline misses. Therefore, probabilistic arguments and analyses are applicable in the timing analyses for this class of systems, as demonstrated in many existing researches. Convolution-based analyses allow to derive tight deadline-miss probabilities, but suffer from a high time complexity. Among the analytical approaches, which result in a significantly faster runtime than the convolution-based approaches, the Chernoff bounds provide the tightest results. In this paper, we show that calculating the deadline-miss probability using Chernoff bounds can be solved by considering an equivalent convex optimization problem. This allows us to, on the one hand, decrease the runtime of the Chernoff bounds while, on the other hand, ensure a tighter approximation since a larger variable space can be searched more efficiently, i.e., by using binary search techniques over a larger area instead of a sequential search over a smaller area. We evaluate this approach considering synthesized task sets. Our approach is shown to be computationally efficient for large task systems, whilst experimentally suggesting reasonable approximation quality compared to an exact analysis. Download Paper (PDF; Only available from the DATE venue WiFi)
15:15	7.7.3	FADEML: UNDERSTANDING THE IMPACT OF PRE-PROCESSING NOISE FILTERING ON ADVERSARIAL MACHINE LEARNING Speaker: Faiq Khalid, Department of computer engineering, TU Wein, AT Authors: Faiq Khalid¹, Muhammad Abdullah Hanif¹, Semeen Rehman¹, Junaid Qadir² and Muhammad Shafique¹ ¹Vienna University of Technology (TU Wien), AT; ²Information Technology University, Lahore, PK Abstract Deep neural networks (DNN)-based machine learning (ML) algorithms have recently emerged as the leading ML paradigm particularly for the task of classification due to their superior capability of learning efficiently from large datasets. The discovery of a number of well-known attacks such as dataset poisoning, adversarial examples, and network manipulation (through the addition of malicious nodes) has, however, put the spotlight squarely on the lack of security in DNN-based ML systems. In particular, malicious actors can use these well-known attacks to cause random/targeted misclassification, or cause a change in the prediction confidence, by only slightly but systematically manipulating the environmental parameters, inference data, or the data acquisition block. Most of the prior adversarial attacks have, however, not accounted for the pre-processing noise filters commonly integrated with the ML-inference module. Our contribution in this work is to show that this is a major omission since these noise filters can render ineffective the majority of the existing attacks, which rely essentially on introducing adversarial noise. Apart from this, we also extend the state of the art by proposing a novel pre-processing noise Filter-aware Adversarial ML attack called FAdeML. To demonstrate the effectiveness of the proposed methodology, we generate an adversarial attack image by exploiting the "VGGNet" DNN trained for the "German Traffic Sign Recognition Benchmarks (GTSRB)" dataset, which despite having no visual noise, can cause a classifier to misclassify even in the presence of preprocessing noise filters. We will make all the contributions open-source at: http://LinkHiddenForBlindReview to facilitate reproducible research and further R&D. Download Paper (PDF; Only available from the DATE venue WiFi)
15:30	7.7.4	REAL-TIME ANOMALOUS BRANCH BEHAVIOR INFERENCE WITH A GPU-INSPIRED ENGINE FOR MACHINE LEARNING MODELS Speaker: Hyunyoung Oh, Seoul National University, KR Authors: Hyunyoung Oh¹, Hayoon Yi¹, Hyeokjun Choe¹, Yeongpil Cho², Sungroh Yoon¹ and Yunheung Paek¹ ¹Seoul National University, KR; ²Soongsil University, KR Abstract As the age of IoT approaches, the importance of security for embedded devices is continuously increasing. Since attacks on these devices are likely to occur any time in unexpected manners, the defense systems based on a fixed set of rules will easily be subverted by such unexpected, unknown attacks. Learning-based anomaly detection is a promising technique that may potentially prevent new unknown zero-day attacks by leveraging the capability of machine learning (ML) to learn the intricate true nature of software hidden within raw information. This paper introduces our recent work to develop an MPSoC platform, called RTAD, which can efficiently support in hardware various ML models that run to detect anomalous behaviors in a real-time fashion. In our work, we assume that ML models are trained with runtime branch information since it is widely regarded that a sequence of branches serves as a record of control flow transfers during program execution. In fact, there have been numerous studies that examine various types of branches including system calls and general branches in order to infer (or detect) anomaly in branch behaviors that may be induced by diverse attacks. Our goal of real-time anomalous branch behavior inference poses two challenges to our development of RTAD. One is that RTAD must collect and transfer in a timely fashion a sequence of branches as the input to the ML model. The other is that RTAD must be able to promptly process the delivered branch data with the ML model. To tackle these challenges, we have implemented in RTAD two core hardware components: an input generation module and a GPU-inspired ML processing engine. According to our empirical results, RTAD enables various ML models to infer anomaly instantly after the victim program behaves aberrantly as the result of attacks being injected into the system. Download Paper (PDF; Only available from the DATE venue WiFi)
15:45	7.7.5	TROJANZERO: SWITCHING ACTIVITY-AWARE DESIGN OF UNDETECTABLE HARDWARE TROJANS WITH ZERO POWER AND AREA FOOTPRINT Speaker: Imran Abbasi, NUST, PK Authors: Imran Abbasi¹, Faiq Khalid², Semeen Rehman², Awais Kamboh¹, Axel Jantsch², Siddharth Garg³ and Muhammad Shafique² ¹NUST, PK; ²Vienna University of Technology (TU Wien), AT; ³University of Waterloo, CA Abstract Conventional Hardware Trojan (HT) detection techniques are based on the validation of integrated circuits to determine changes in their functionality, and on non-invasive side-channel analysis to identify the variations in their physical parameters. In particular, almost all the proposed side-channel power-based detection techniques presume that HTs are detectable because they only add gates to the original circuit with a noticeable increase in power consumption. This paper demonstrates how undetectable HTs can be realized with zero impact on the power and area footprint of the original circuit. Towards this, we propose a novel concept of TrojanZero and a systematic methodology for designing undetectable HTs in the circuits, which conceals their existence by gate-level modifications. The crux is to salvage the cost of the HT from the original circuit without being detected using standard testing techniques. Our methodology leverages the knowledge of transition probabilities of the circuit nodes to identify and safely remove expendable gates, and embeds malicious circuitry at the appropriate locations with zero power and area overheads when compared to the original circuit. We synthesize these designs and then embed in multiple ISCAS85 benchmarks using a 65nm technology library, and perform a comprehensive power and area characterization. Our experimental results demonstrate that the proposed TrojanZero designs are undetectable by the state-of-the-art power-based detection methods. Download Paper (PDF; Only available from the DATE venue WiFi)
16:00	IP3-18, 414	ASSERTION-BASED VERIFICATION THROUGH BINARY INSTRUMENTATION Speaker: Laurence Pierre, Univ. Grenoble Alpes, FR Authors: Enzo Brignon and Laurence Pierre, TIMA Lab (Univ. Grenoble Alpes, CNRS, Grenoble INP), FR Abstract Verifying the correctness and the reliability of C or C++ embedded software is a crucial issue. To alleviate this verification process, we advocate runtime assertion-based verification of formal properties. Such logic and temporal properties can be specified using the IEEE standard PSL (Property Specification Language) and automatically translated into software assertion checkers. A major issue is the instrumentation of the embedded program so that those assertion checkers will be triggered upon specific events during execution. This paper presents an automatic instrumentation solution for object files, which enables such an event-driven property evaluation. It also reports experimental results for different kinds of applications and properties. Download Paper (PDF; Only available from the DATE venue WiFi)
16:00		End of session Coffee Break in Exhibition Area Coffee Breaks in the Exhibition Area On all conference days (Tuesday to Thursday), coffee and tea will be served during the coffee breaks at the below-mentioned times in the exhibition area. Lunch Breaks (Lunch Area) On all conference days (Tuesday to Thursday), a seated lunch (lunch buffet) will be offered in the Lunch Area to fully registered conference delegates only. There will be badge control at the entrance to the lunch break area. Tuesday, March 26, 2019 Coffee Break 10:30 - 11:30 Lunch Break 13:00 - 14:30 Keynote Lecture "Leonardo da Vinci, Humanism and Engineering between Florence and Milan" by Claudio Giorgione in room 1 13:50 - 14:20 Coffee Break 16:00 - 17:00 Wednesday, March 27, 2019 Coffee Break 10:00 - 11:00 Lunch Break 12:30 - 14:30 Keynote Lecture "Heterogeneous, High Scale Computing in the Era of Intelligent, Cloud-Connected" by David Pellerin, Amazon, US in room 1 13:50 - 14:20 Coffee Break 16:00 - 17:00 Thursday, March 28, 2019 Coffee Break 10:00 - 11:00 University Booth Best Demo Award Presentation at the University Booth 10:30 Lunch Break 12:30 - 14:00 Keynote Lecture "A Fundamental Look at Models and Intelligence" by Edward A. Lee, University of California, Berkeley, US in room 1 13:20 - 13:50 Coffee Break 15:30 - 16:00

Time

Label

Presentation Title
Authors

14:30

7.7.1

(Best Paper Award Candidate)
BETTER LATE THAN NEVER VERIFICATION OF EMBEDDED SYSTEMS AFTER DEPLOYMENT
Authors:
Martin Ring¹, Fritjof Bornebusch¹, Christoph Lüth¹, Robert Wille² and Rolf Drechsler¹
¹University of Bremen, DE; ²Johannes Kepler University Linz, AT
Abstract
This paper investigates the benefits of verifying embedded systems after deployment. We argue that one reason for the huge state spaces of contemporary embedded and cyber-physical systems is the large variety of operating contexts, which are unknown during design. Once the system is deployed, these contexts become observable, confining several variables. By this, the search space is dramatically reduced, making verification possible even on the limited resources of a deployed system. In this paper, we propose a design and verification flow which exploits this observation. We show how specifications are transferred to the deployed system and verified there. Evaluations on a number of case studies demonstrate the reduction of the search space, and we sketch how the proposed approach can be employed in practice.
Download Paper (PDF; Only available from the DATE venue WiFi)

15:00

7.7.2

EFFICIENT COMPUTATION OF DEADLINE-MISS PROBABILITY AND POTENTIAL PITFALLS
Speaker:
Kuan-Hsun Chen, TU Dortmund, DE
Authors:
Kuan-Hsun Chen, Niklas Ueter, Georg von der Brüggen and Jian-Jia Chen, Technical University of Dortmund, DE
Abstract
In soft real-time systems, applications can tolerate rare deadline misses. Therefore, probabilistic arguments and analyses are applicable in the timing analyses for this class of systems, as demonstrated in many existing researches. Convolution-based analyses allow to derive tight deadline-miss probabilities, but suffer from a high time complexity. Among the analytical approaches, which result in a significantly faster runtime than the convolution-based approaches, the Chernoff bounds provide the tightest results. In this paper, we show that calculating the deadline-miss probability using Chernoff bounds can be solved by considering an equivalent convex optimization problem. This allows us to, on the one hand, decrease the runtime of the Chernoff bounds while, on the other hand, ensure a tighter approximation since a larger variable space can be searched more efficiently, i.e., by using binary search techniques over a larger area instead of a sequential search over a smaller area. We evaluate this approach considering synthesized task sets. Our approach is shown to be computationally efficient for large task systems, whilst experimentally suggesting reasonable approximation quality compared to an exact analysis.
Download Paper (PDF; Only available from the DATE venue WiFi)

15:15

7.7.3

FADEML: UNDERSTANDING THE IMPACT OF PRE-PROCESSING NOISE FILTERING ON ADVERSARIAL MACHINE LEARNING
Speaker:
Faiq Khalid, Department of computer engineering, TU Wein, AT
Authors:
Faiq Khalid¹, Muhammad Abdullah Hanif¹, Semeen Rehman¹, Junaid Qadir² and Muhammad Shafique¹
¹Vienna University of Technology (TU Wien), AT; ²Information Technology University, Lahore, PK
Abstract
Deep neural networks (DNN)-based machine learning (ML) algorithms have recently emerged as the leading ML paradigm particularly for the task of classification due to their superior capability of learning efficiently from large datasets. The discovery of a number of well-known attacks such as dataset poisoning, adversarial examples, and network manipulation (through the addition of malicious nodes) has, however, put the spotlight squarely on the lack of security in DNN-based ML systems. In particular, malicious actors can use these well-known attacks to cause random/targeted misclassification, or cause a change in the prediction confidence, by only slightly but systematically manipulating the environmental parameters, inference data, or the data acquisition block. Most of the prior adversarial attacks have, however, not accounted for the pre-processing noise filters commonly integrated with the ML-inference module. Our contribution in this work is to show that this is a major omission since these noise filters can render ineffective the majority of the existing attacks, which rely essentially on introducing adversarial noise. Apart from this, we also extend the state of the art by proposing a novel pre-processing noise Filter-aware Adversarial ML attack called FAdeML. To demonstrate the effectiveness of the proposed methodology, we generate an adversarial attack image by exploiting the "VGGNet" DNN trained for the "German Traffic Sign Recognition Benchmarks (GTSRB)" dataset, which despite having no visual noise, can cause a classifier to misclassify even in the presence of preprocessing noise filters. We will make all the contributions open-source at: http://LinkHiddenForBlindReview to facilitate reproducible research and further R&D.
Download Paper (PDF; Only available from the DATE venue WiFi)

15:30

7.7.4

REAL-TIME ANOMALOUS BRANCH BEHAVIOR INFERENCE WITH A GPU-INSPIRED ENGINE FOR MACHINE LEARNING MODELS
Speaker:
Hyunyoung Oh, Seoul National University, KR
Authors:
Hyunyoung Oh¹, Hayoon Yi¹, Hyeokjun Choe¹, Yeongpil Cho², Sungroh Yoon¹ and Yunheung Paek¹
¹Seoul National University, KR; ²Soongsil University, KR
Abstract
As the age of IoT approaches, the importance of security for embedded devices is continuously increasing. Since attacks on these devices are likely to occur any time in unexpected manners, the defense systems based on a fixed set of rules will easily be subverted by such unexpected, unknown attacks. Learning-based anomaly detection is a promising technique that may potentially prevent new unknown zero-day attacks by leveraging the capability of machine learning (ML) to learn the intricate true nature of software hidden within raw information. This paper introduces our recent work to develop an MPSoC platform, called RTAD, which can efficiently support in hardware various ML models that run to detect anomalous behaviors in a real-time fashion. In our work, we assume that ML models are trained with runtime branch information since it is widely regarded that a sequence of branches serves as a record of control flow transfers during program execution. In fact, there have been numerous studies that examine various types of branches including system calls and general branches in order to infer (or detect) anomaly in branch behaviors that may be induced by diverse attacks. Our goal of real-time anomalous branch behavior inference poses two challenges to our development of RTAD. One is that RTAD must collect and transfer in a timely fashion a sequence of branches as the input to the ML model. The other is that RTAD must be able to promptly process the delivered branch data with the ML model. To tackle these challenges, we have implemented in RTAD two core hardware components: an input generation module and a GPU-inspired ML processing engine. According to our empirical results, RTAD enables various ML models to infer anomaly instantly after the victim program behaves aberrantly as the result of attacks being injected into the system.
Download Paper (PDF; Only available from the DATE venue WiFi)

15:45

7.7.5

TROJANZERO: SWITCHING ACTIVITY-AWARE DESIGN OF UNDETECTABLE HARDWARE TROJANS WITH ZERO POWER AND AREA FOOTPRINT
Speaker:
Imran Abbasi, NUST, PK
Authors:
Imran Abbasi¹, Faiq Khalid², Semeen Rehman², Awais Kamboh¹, Axel Jantsch², Siddharth Garg³ and Muhammad Shafique²
¹NUST, PK; ²Vienna University of Technology (TU Wien), AT; ³University of Waterloo, CA
Abstract
Conventional Hardware Trojan (HT) detection techniques are based on the validation of integrated circuits to determine changes in their functionality, and on non-invasive side-channel analysis to identify the variations in their physical parameters. In particular, almost all the proposed side-channel power-based detection techniques presume that HTs are detectable because they only add gates to the original circuit with a noticeable increase in power consumption. This paper demonstrates how undetectable HTs can be realized with zero impact on the power and area footprint of the original circuit. Towards this, we propose a novel concept of TrojanZero and a systematic methodology for designing undetectable HTs in the circuits, which conceals their existence by gate-level modifications. The crux is to salvage the cost of the HT from the original circuit without being detected using standard testing techniques. Our methodology leverages the knowledge of transition probabilities of the circuit nodes to identify and safely remove expendable gates, and embeds malicious circuitry at the appropriate locations with zero power and area overheads when compared to the original circuit. We synthesize these designs and then embed in multiple ISCAS85 benchmarks using a 65nm technology library, and perform a comprehensive power and area characterization. Our experimental results demonstrate that the proposed TrojanZero designs are undetectable by the state-of-the-art power-based detection methods.
Download Paper (PDF; Only available from the DATE venue WiFi)

16:00

IP3-18, 414

ASSERTION-BASED VERIFICATION THROUGH BINARY INSTRUMENTATION
Speaker:
Laurence Pierre, Univ. Grenoble Alpes, FR
Authors:
Enzo Brignon and Laurence Pierre, TIMA Lab (Univ. Grenoble Alpes, CNRS, Grenoble INP), FR
Abstract
Verifying the correctness and the reliability of C or C++ embedded software is a crucial issue. To alleviate this verification process, we advocate runtime assertion-based verification of formal properties. Such logic and temporal properties can be specified using the IEEE standard PSL (Property Specification Language) and automatically translated into software assertion checkers. A major issue is the instrumentation of the embedded program so that those assertion checkers will be triggered upon specific events during execution. This paper presents an automatic instrumentation solution for object files, which enables such an event-driven property evaluation. It also reports experimental results for different kinds of applications and properties.
Download Paper (PDF; Only available from the DATE venue WiFi)

16:00

End of session
Coffee Break in Exhibition Area

Coffee Breaks in the Exhibition Area

On all conference days (Tuesday to Thursday), coffee and tea will be served during the coffee breaks at the below-mentioned times in the exhibition area.

Lunch Breaks (Lunch Area)

On all conference days (Tuesday to Thursday), a seated lunch (lunch buffet) will be offered in the Lunch Area to fully registered conference delegates only. There will be badge control at the entrance to the lunch break area.

Tuesday, March 26, 2019

Coffee Break 10:30 - 11:30
Lunch Break 13:00 - 14:30
Keynote Lecture "Leonardo da Vinci, Humanism and Engineering between Florence and Milan" by Claudio Giorgione in room 1 13:50 - 14:20
Coffee Break 16:00 - 17:00

Wednesday, March 27, 2019

Coffee Break 10:00 - 11:00
Lunch Break 12:30 - 14:30
Keynote Lecture "Heterogeneous, High Scale Computing in the Era of Intelligent, Cloud-Connected" by David Pellerin, Amazon, US in room 1 13:50 - 14:20
Coffee Break 16:00 - 17:00

Thursday, March 28, 2019

Coffee Break 10:00 - 11:00
University Booth Best Demo Award Presentation at the University Booth 10:30
Lunch Break 12:30 - 14:00
Keynote Lecture "A Fundamental Look at Models and Intelligence" by Edward A. Lee, University of California, Berkeley, US in room 1 13:20 - 13:50
Coffee Break 15:30 - 16:00