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Arm PSA-Certified IoT Chip Security: A Case Study

Tsinghua Science and Technology 2023年第2期28卷 244-257页

作者： Fei Chen Duming Luo Jianqiang Li Victor C.M.Leung Shiqi Li Junfeng Fan College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China Department of Electrical and Computer Engineering the University of British ColumbiaVancouverBC V6T 1Z4Canada Open Security Research Inc.Shenzhen 518000China

With the large scale adoption of Internet of Things(IoT)applications in people’s lives and industrial manufacturing processes,IoT security has become an important problem *** security significantly relies on the security of the underlying hardware chip,which often contains critical information,such as encryption *** understand existing IoT chip security,this study analyzes the security of an IoT security chip that has obtained an Arm Platform Security Architecture(PSA)Level 2 *** analysis shows that the chip leaks part of the encryption key and presents a considerable security ***,we use commodity equipment to collect electromagnetic traces of the *** a statistical T-test,we find that the target chip has physical leakage during the AES encryption *** further use correlation analysis to locate the detailed encryption interval in the collected electromagnetic trace for the Advanced Encryption Standard(AES)encryption *** the basis of the intermediate value correlation analysis,we recover half of the 16-byte AES encryption *** repeat the process for three different tests;in all the tests,we obtain the same result,and we recover around 8 bytes of the 16-byte AES encryption ***,experimental results indicate that despite the Arm PSA Level 2 certification,the target security chip still suffers from physical *** layer application developers should impose strong security mechanisms in addition to those of the chip itself to ensure IoT application security.

关键词： Internet of Things(IoT)security chip Arm Platform Security Architecture(PSA)certification electromagnetic side-channel attack Advanced Encryption Standard(AES)encryption key leakage

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Research on abnormal behavior recognition of the elderly based on spatial-temporal feature fusion 22

Research on abnormal behavior recognition of the elderly bas...

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3rd International Symposium on Artificial Intelligence for Medical Sciences, ISAIMS 2022

作者： He, Yulong Huang, Hanming Wu, Yezheng Zhu, Guanglei College of Computer Science and Engineering College of Software Guangxi Normal University China

ISBN: (纸本)9781450398442

With the deepening of the aging society, the health care of the elderly and disabled people has been widely concerned by society. It is of great practical significance to monitor the physical conditions and abnormal behaviors of elderly and disabled people based on sensor devices to prevent diseases and dangerous behaviors. However, most of the current algorithms (classical machine learning algorithms, CNN, RNN, LSTM) are only limited to the traditional Euclidean space, with little expansion in Non-Euclidean space. The translation invariance limits the expression ability of spatial data. The signals collected by the wearable sensor based on human abnormal behavior recognition often come from different parts and different directions of the human body. The traditional Euclidean algorithm is used to solve the problem, which makes the classification results easy to ignore the spatial characteristics of the sensor signals. The emergence of graph neural networks provides a new method to solve this problem. A large number of studies have proved that the graph neural network has a great role in dealing with Non-Euclidean space problems. Therefore, this paper proposes a new method for abnormal behavior recognition of the elderly based on spatiotemporal feature fusion (SCGAT). The Non-Euclidean spatial features of sensor signals are extracted by graph attention neural network, and the classification model is constructed by combining the extracted time domain features of the time-series coding network. In the experimental results, the proposed method achieves better classification results than most of the current algorithms. © 2022 ACM.

关键词： Health care

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A Hybrid Deep Fused Learning Approach to Segregate Infectious Diseases

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computers, Materials & Continua 2023年第2期74卷 4239-4259页

作者： Jawad Rasheed Shtwai Alsubai Department of Software Engineering Nisantasi UniversityIstanbul34398Turkey Dapartment of Computer Science College of Computer Engineering and Sciences in Al-KharjPrince Sattam Bin Abdulaziz UniversityP.O.Box 151Al-Kharj11942Saudi Arabia

Humankind is facing another deadliest pandemic of all times in history,caused by *** from this challenging pandemic,World Health Organization(WHO)considers tuberculosis(TB)as a preeminent infectious disease due to its high infection ***,both TB and COVID-19 severely affect the lungs,thus hardening the job of medical practitioners who can often misidentify these diseases in the current ***,the time of need calls for an immediate and meticulous automatic diagnostic tool that can accurately discriminate both *** one of the preliminary smart health systems that examine three clinical states(COVID-19,TB,and normal cases),this study proposes an amalgam of image filtering,data-augmentation technique,transfer learning-based approach,and advanced deep-learning classifiers to effectively segregate these *** first employed a generative adversarial network(GAN)and Crimmins speckle removal filter on X-ray images to overcome the issue of limited data and *** pre-processed image is then converted into red,green,and blue(RGB)and Commission Internationale de l’Elcairage(CIE)color spaces from which deep fused features are formed by extracting relevant features using DenseNet121 and *** feature extractor extracts 1000 most useful features which are then fused and finally fed to two variants of recurrent neural network(RNN)classifiers for precise discrimination of threeclinical *** analysis showed that the proposed Bi-directional long-short-term-memory(Bi-LSTM)model dominated the long-short-termmemory(LSTM)network by attaining an overall accuracy of 98.22%for the three-class classification task,whereas LSTM hardly achieved 94.22%accuracy on the test dataset.

关键词： computer-aided diagnosis decision support system deep transfer learning deep fused features tuberculosis COVID-19

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MemSpate: Memory Usage Protocol Guided Fuzzing 1

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25th International Conference on Formal engineering Methods, ICFEM 2024

作者： Fu, Zhiyuan Jiang, Jiacheng Wen, Cheng Xu, Zhiwu Qin, Shengchao College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangzhou Institute of Technology Xidian University Xi’An China

ISBN: (数字)9789819606177

ISBN: (纸本)9789819606160

Memory safety vulnerabilities are high-risk and common vulnerabilities in software testing, often leading to a series of system errors. Fuzz testing is widely recognized as one of the most effective methods for detecting vulnerabilities, including memory safety ones. However, current fuzzing solutions typically only partially address memory usage, limiting their ability to detect memory safety vulnerabilities. In this paper, we introduce MemSpate, a dedicated fuzzer designed to detect memory safety vulnerabilities. Utilizing a more comprehensive memory usage protocol, MemSpate identifies the memory operation sequences that may violate the protocol and estimates the overall memory consumption to exceed an acceptable limit. It then monitors the coverage of these operation sequences and tracks the maximum memory consumption, both of which are used as a new feedback mechanism to guide the fuzzing process. We evaluated MemSpate on 12 real-world open-source programs and compared its performance with 5 state-of-the-art fuzzers. The results demonstrate that MemSpate surpasses all other fuzzers in terms of discovering memory safety vulnerabilities. Furthermore, our experiments have led to the discovery of 4 previously unknown vulnerabilities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Safety testing

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Enhancing Code Generation for Dataflow Programming: Fine-Tuning Large Language Models with the DFCPP Dataset 22

Enhancing Code Generation for Dataflow Programming: Fine-Tun...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Luo, Qiuming Ma, Xi Wei, Xuan Shenzhen University College of Computer Science and Software Engineering Shenzhen China Tianjin Yuyi Kaihong Intelligent Technology Tianjin China

ISBN: (纸本)9798331509712

In recent years, large language models (LLMs) based on the Transformer architecture have demonstrated excellent performance in code generation, but there have been fewer studies on data flow languages. This study proposes a scheme for fine-tuning large language models based on the DFCPP dataset. We demonstrate the model's ability to generate dataflow graph (DAG) topologies and achieve significant performance improvements. Experimental results show that the BLEU score of the fine-tuned model in the DFCPP code generation task reaches 0.193, which is an increase of 112.1% compared to the non-fine-tuned model (0.091). This demonstrates the effectiveness of fine-tuning techniques in domain-specific code generation. © 2024 IEEE.

关键词： Data flow analysis

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Efficient Dual-Confounding Eliminating for Weakly-supervised Temporal Action Localization 24

Efficient Dual-Confounding Eliminating for Weakly-supervised...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Li, Ao Liu, Huijun Sheng, Jinrong Chen, Zhongming Ge, Yongxin School of Big Data and Software Engineering Chongqing University Chongqing China College of Computer Science Chongqing University Chongqing China

ISBN: (纸本)9798400706868

Weakly-supervised Temporal Action Localization (WTAL) following a localization-by-classification paradigm has achieved significant results, yet still grapples with confounding arising from ambiguous snippets. Previous works have attempted to distinguish these ambiguous snippets from action snippets without investigating the underlying causes of their formation, thus failing to effectively eliminate the bias on both action-context and action-content. In this paper, we revisit WTAL from the perspective of structural causal model to identify the true origins of confounding, and propose an efficient dual-confounding eliminating framework to alleviate these biases. Specifically, we construct a Substituted Confounder Set (SCS) to eliminate the confounding bias on action-context by leveraging the modal disparity between RGB and FLOW. Then, a Multi-level Consistency Mining (MCM) method is designed to mitigate the confounding bias on action-content by utilizing the consistency between discriminative snippets and corresponding proposals at both the feature and label levels. Notably, SCS and MCM could be seamlessly integrated into any two-stream models without additional parameters by Expectation-Maximization (EM) algorithm. Extensive experiments on two challenging benchmarks including THUMOS14 and ActivityNet-1.2 demonstrate the superior performance of our method. © 2024 ACM.

关键词： Expectation maximization algorithm

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Multimodal Biometric Recognition Neural Networks with Branch Attention Mechanisms 2

Multimodal Biometric Recognition Neural Networks with Branch...

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2nd Asia Conference on Advanced Robotics, Automation, and control engineering, ARACE 2023

作者： Liujun Mingjin, Chen Guoyong Zengxi, Huang Xihua University College of Computer and Software Engineering Chengdu China Sichuan Police College Criminal Science and Technology Department Chengdu China

ISBN: (纸本)9798350328363

It is generally known that compared to unimodal systems, the multi-modal biometric systems can improve recognition accuracy by utilizing the complementary features of multiple biometric. Nevertheless, the modalities used may not always play equally important roles in multi-modal recognition. Especially, in practical application scenarios, it often occurs that some of the biometric samples are contaminated by noise and corruption. In this context, the multi-modal recognition systems should pay more attention to the high-quality modality than the ones with low-quality samples, so as to maintain high accuracy. In this paper, we proposes a multi-branch deep learning network for multi-modal recognition, each branch using one biometric sample as input. To highlight the more important biometric modalities, we propose to embed a branch attention module between branches before multi-modal feature fusion. The branch attention module includes fuse, select, and calibrate steps, which can dynamically calculate the weight of each branch based on its importance. The proposed multi-modal recognition model is tested on a chimeric bimodal dataset with the face and the ear modalities. Our proposed method still achieves 92.76% of rank-1 on the most contaminated dataset;the rest of the experimental results are shown in the Experiment and Performance Analysis section. Our experimental results clearly demonstrate that the proposed branch attention method can improve the multi-modal recognition performance. © 2023 IEEE.

关键词： Biometrics

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DDN: Dual-domain Dynamic Normalization for Non-stationary Time Series Forecasting 38

DDN: Dual-domain Dynamic Normalization for Non-stationary Ti...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Dai, Tao Wu, Beiliang Liu, Peiyuan Li, Naiqi Yuerong, Xue Xia, Shu-Tao Zhu, Zexuan College of Computer Science and Software Engineering Shenzhen University China Tsinghua Shenzhen International Graduate School Tsinghua University China

Deep neural networks (DNNs) have recently achieved remarkable advancements in time series forecasting (TSF) due to their powerful ability of sequence dependence modeling. To date, existing DNN-based TSF methods still suffer from unreliable predictions for real-world data due to its non-stationarity characteristics, i.e., data distribution varies quickly over time. To mitigate this issue, several normalization methods (e.g., SAN) have recently been specifically designed by normalization in a fixed period/window in the time domain. However, these methods still struggle to capture distribution variations, due to the complex time patterns of time series in the time domain. Based on the fact that wavelet transform can decompose time series into a linear combination of different frequencies, which exhibits distribution variations with time-varying periods, we propose a novel Dual-domain Dynamic Normalization (DDN) to dynamically capture distribution variations in both time and frequency domains. Specifically, our DDN tries to eliminate the non-stationarity of time series via both frequency and time domain normalization in a sliding window way. Besides, our DDN can serve as a plug-in-play module, and thus can be easily incorporated into other forecasting models. Extensive experiments on public benchmark datasets under different forecasting models demonstrate the superiority of our DDN over other normalization methods. Code is available at https://***/Hank0626/DDN. © 2024 Neural information processing systems foundation. All rights reserved.

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Towards 360∘ image compression for machines via modulating pixel significance

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Multimedia Tools and Applications 2024年第42期83卷 90271-90288页

作者： Zheng, Silin Shen, Xuelin Zhang, Qiudan Chen, Zhuo Yang, Wenhan Wang, Xu College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen51800 China Guangdong Shenzhen51800 China Peng Cheng Laboratory Guangdong Shenzhen51800 China

The rapid growth of computer vision-based applications, including smart cities and autonomous driving, has created a pressing demand for efficient 360∘ image compression and computer vision analytics. In most circumstances, 360∘ image compression and computer vision face challenges arising from the oversampling inherent in the Equirectangular Projection (ERP). However, these two fields often employ divergent technological approaches. Since image compression aims to reduce redundancy, computer vision analytics attempts to compensate for the semantic distortion caused by the projection process, resulting in a potential conflict between the two objectives. This paper explores a potential route, i.e.360∘ Image Coding for Machine (360-ICM), which offers an image processing framework that addresses both object deformation and oversampling redundancy within a unified framework. The key innovation lies in inferring a pixel-wise significant map by jointly considering the requirements of redundancy removal and object deformation offsetting. The significance map would be subsequently fed to a deformation-aware image compression network, guiding the bit allocation process as an external condition. More specifically, we employ a deformation-aware image compression network that is characterized by the Spatial Feature Transform (SFT) layer, which is capable of performing complex affine transformations of high-level semantic features, to be essential in dealing with the deformation. The image compression network and significance inference network are jointly trained under the supervision of a 360∘ image-specified object detection network, obtaining a compact representation that is both analytics-oriented and deformation-aware. Extensive experimental results have demonstrated the superiority of the proposed method over existing state-of-the-art image codecs in terms of rate-analytics performance. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part

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Video2Reward: Generating Reward Function from Videos for Legged Robot Behavior Learning 27

Video2Reward: Generating Reward Function from Videos for Leg...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Zeng, Runhao Zhou, Dingjie Liang, Qiwei Liu, Junlin Li, Hui Huang, Changxin Li, Jianqiang Hu, Xiping Sun, Fuchun Artificial Intelligence Research Institute Shenzhen MSU-BIT University China College of Mechatronics and Control Engineering Shenzhen University China College of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Computer Science and Technology Tsinghua University China

ISBN: (纸本)9781643685489

Learning behavior in legged robots presents a significant challenge due to its inherent instability and complex constraints. Recent research has proposed the use of a large language model (LLM) to generate reward functions in reinforcement learning, thereby replacing the need for manually designed rewards by experts. However, this approach, which relies on textual descriptions to define learning objectives, fails to achieve controllable and precise behavior learning with clear directionality. In this paper, we introduce a new video2reward method, which directly generates reward functions from videos depicting the behaviors to be mimicked and learned. Specifically, we first process videos containing the target behaviors, converting the motion information of individuals in the videos into keypoint trajectories represented as coordinates through a video2text transforming module. These trajectories are then fed into an LLM to generate the reward function, which in turn is used to train the policy. To enhance the quality of the reward function, we develop a video-assisted iterative reward refinement scheme that visually assesses the learned behaviors and provides textual feedback to the LLM. This feedback guides the LLM to continually refine the reward function, ultimately facilitating more efficient behavior learning. Experimental results on tasks involving bipedal and quadrupedal robot motion control demonstrate that our method surpasses the performance of state-of-the-art LLM-based reward generation methods by over 37.6% in terms of human normalized score. More importantly, by switching video inputs, we find our method can rapidly learn diverse motion behaviors such as walking and running. © 2024 The Authors.

关键词： Robots

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