Power Profiling and Analysis of Code Obfuscation for Embedded Devices

In the era of the internet of things (IoT), software-enabled inter-connected devices are of paramount importance. The embedded systems are very frequently used in both security and privacy-sensitive applications. However, the underlying software (a.k.a. firmware) very often suffers from a wide range of security vulnerabilities, mainly due to their outdated systems or reusing existing vulnerable libraries; which is evident by the surprising rise in the number of attacks against embedded systems. Therefore, to protect those embedded systems, detecting the presence of vulnerabilities in the large pool of embedded devices and their firmware plays a vital role. To this end, there exist several approaches to identify and trigger potential vulnerabilities within deployed embedded systems firmware. In this survey, we provide a comprehensive review of the state-of-the-art proposals, which detect vulnerabilities in embedded systems and firmware images by employing various analysis techniques, including static analysis, dynamic analysis, symbolic execution, and hybrid approaches. Furthermore, we perform both quantitative and qualitative comparisons among the surveyed approaches. Moreover, we devise taxonomies based on the applications of those approaches, the features used in the literature, and the type of the analysis. Finally, we identify the unresolved challenges and discuss possible future directions in this field of research.

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Real-Time Instance Segmentation of Traffic Videos for Embedded Devices

Sensors ◽

10.3390/s21010275 ◽

2021 ◽

Vol 21 (1) ◽

pp. 275

Author(s):

Ruben Panero Martinez ◽

Ionut Schiopu ◽

Bruno Cornelis ◽

Adrian Munteanu

Keyword(s):

Real Time ◽

Network Architecture ◽

Training Procedure ◽

Segmentation Method ◽

Embedded Devices ◽

Network Training ◽

Assignment Algorithm ◽

Ablation Study ◽

Reduced Rate ◽

Instance Segmentation

The paper proposes a novel instance segmentation method for traffic videos devised for deployment on real-time embedded devices. A novel neural network architecture is proposed using a multi-resolution feature extraction backbone and improved network designs for the object detection and instance segmentation branches. A novel post-processing method is introduced to ensure a reduced rate of false detection by evaluating the quality of the output masks. An improved network training procedure is proposed based on a novel label assignment algorithm. An ablation study on speed-vs.-performance trade-off further modifies the two branches and replaces the conventional ResNet-based performance-oriented backbone with a lightweight speed-oriented design. The proposed architectural variations achieve real-time performance when deployed on embedded devices. The experimental results demonstrate that the proposed instance segmentation method for traffic videos outperforms the you only look at coefficients algorithm, the state-of-the-art real-time instance segmentation method. The proposed architecture achieves qualitative results with 31.57 average precision on the COCO dataset, while its speed-oriented variations achieve speeds of up to 66.25 frames per second on the Jetson AGX Xavier module.

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Authentication of Embedded Devices

10.1007/978-3-030-60769-2 ◽

2021 ◽

Keyword(s):

Embedded Devices

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On blockchain integration into mobile crowdsensing via smart embedded devices: A comprehensive survey

Journal of Systems Architecture ◽

10.1016/j.sysarc.2021.102011 ◽

2021 ◽

Vol 115 ◽

pp. 102011 ◽

Cited By ~ 1

Author(s):

Zhiyan Chen ◽

Claudio Fiandrino ◽

Burak Kantarci

Keyword(s):

Embedded Devices ◽

Mobile Crowdsensing ◽

Comprehensive Survey

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LiDAR Point Cloud Recognition and Visualization with Deep Learning for Overhead Contact Inspection

Sensors ◽

10.3390/s20216387 ◽

2020 ◽

Vol 20 (21) ◽

pp. 6387 ◽

Cited By ~ 1

Author(s):

Xiaohan Tu ◽

Cheng Xu ◽

Siping Liu ◽

Shuai Lin ◽

Lipei Chen ◽

...

Keyword(s):

Deep Learning ◽

Computational Complexity ◽

Point Cloud ◽

High Speed ◽

Point Cloud Data ◽

Embedded Devices ◽

Cloud Data ◽

Manual Inspection ◽

Order Of Magnitude ◽

Supply Systems

As overhead contact (OC) is an essential part of power supply systems in high-speed railways, it is necessary to regularly inspect and repair abnormal OC components. Relative to manual inspection, applying LiDAR (light detection and ranging) to OC inspection can improve efficiency, accuracy, and safety, but it faces challenges to efficiently and effectively segment LiDAR point cloud data and identify catenary components. Recent deep learning-based recognition methods are rarely employed to recognize OC components, because they have high computational complexity, while their accuracy needs to be improved. To track these problems, we first propose a lightweight model, RobotNet, with depthwise and pointwise convolutions and an attention module to recognize the point cloud. Second, we optimize RobotNet to accelerate its recognition speed on embedded devices using an existing compilation tool. Third, we design software to facilitate the visualization of point cloud data. Our software can not only display a large amount of point cloud data, but also visualize the details of OC components. Extensive experiments demonstrate that RobotNet recognizes OC components more accurately and efficiently than others. The inference speed of the optimized RobotNet increases by an order of magnitude. RobotNet has lower computational complexity than other studies. The visualization results also show that our recognition method is effective.

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