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security of Medical Cyber-physical Systems: An Empirical Study on Imaging Devices

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Security of Medical Cyber-physical Systems: An Empirical Stu...

IEEE Conference on computer Communications Workshops, INFOCOM Wksps

作者： Zhiqiang Wang Pingchuan Ma Xiaoxiang Zou Jianyi Zhang Tao Yang Beijing Electronic Science and Technology Institute State Information Center National Computer Network Emergency Response Technical Team/Coordination Center of China Key Lab of Information Network Security Ministry of Public Security

ISBN: (数字)9781728186955

ISBN: (纸本)9781728186962

Recent years have witnessed a boom of connected medical devices, which brings security issues in the meantime. Medical imaging devices, an essential part of medical cyber-physical systems, play a vital role in modern hospitals and are often life-critical. However, security and privacy issues in these medical cyber-physical systems are sometimes ignored. We perform an empirical study on imaging devices to analyse the security of medical cyber-physical systems. To be precise, we design a threat model and propose prospective attack techniques for medical imaging devices. To tackle potential cyber threats, we introduce protection mechanisms, evaluate the effectiveness and efficiency of protection mechanisms as well as its interplay with attack techniques. To scoring security, we design a hierarchical system that provides actionable suggestions for imaging devices in different scenarios. We investigate 15 devices from 9 manufacturers to demonstrate empirical comprehension and real-world security issues.

关键词： security Virtual private networks Runtime Hospitals DICOM

Neural Gradient Regularizer

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arXiv 2023年

作者： Xu, Shuang Wang, Yifan Zhao, Zixiang Peng, Jiangjun Cao, Xiangyong Meng, Deyu Zhang, Yulun Timofte, Radu Van Gool, Luc School of Mathematics and Statistics Northwestern Polytechnical University Xi’an710021 China Research and Development Institute Northwestern Polytechnical University in Shenzhen Shenzhen518063 China School of Electronic and Information Engineering The Key Laboratory for Intelligent Networks and Network Security Ministry of Education Xi’an Jiaotong University Xi’an710049 China School of Mathematics and Statistics Xi’an Jiaotong University Xi’an710049 China Computer Vision Lab ETH Zurich Zürich8092 Switzerland

Owing to its significant success, the prior imposed on gradient maps has consistently been a subject of great interest in the field of image processing. Total variation (TV), one of the most representative regularizers, is known for its ability to capture the intrinsic sparsity prior underlying gradient maps. Nonetheless, TV and its variants often underestimate the gradient maps, leading to the weakening of edges and details whose gradients should not be zero in the original image (i.e., image structures is not describable by sparse priors of gradient maps). Recently, total deep variation (TDV) has been introduced, assuming the sparsity of feature maps, which provides a flexible regularization learned from large-scale datasets for a specific task. However, TDV requires to retrain the network with image/task variations, limiting its versatility. To alleviate this issue, in this paper, we propose a neural gradient regularizer (NGR) that expresses the gradient map as the output of a neural network. Unlike existing methods, NGR does not rely on any subjective sparsity or other prior assumptions on image gradient maps, thereby avoiding the underestimation of gradient maps. NGR is applicable to various image types and different image processing tasks, functioning in a zero-shot learning fashion, making it a versatile and plug-and-play regularizer. Extensive experimental results demonstrate the superior performance of NGR over state-of-the-art counterparts for a range of different tasks, further validating its effectiveness and versatility. © 2023, CC BY.

关键词： Large dataset

Are Dense labels Always Necessary for 3D Object Detection from Point Cloud?

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arXiv 2024年

作者： Gao, Chenqiang Liu, Chuandong Shu, Jun Liu, Fangcen Liu, Jiang Yang, Luyu Gao, Xinbo Meng, Deyu The School of Intelligent Systems Engineering The Shen-zhen Campus of Sun Yatsen University Sun Yat-sen University Guangdong Shenzhen518107 China The School of Computer Science Wuhan University Wuhan430072 China School of Mathematics and Statistics Ministry of Education Key Lab of Intelligent Networks and Network Security Xi’an Jiaotong University Shaanxi 710049 China School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Meta Menlo Park94025 United States University of Maryland College Park20742 United States

Current state-of-the-art (SOTA) 3D object detection methods often require a large amount of 3D bounding box annotations for training. However, collecting such large-scale densely-supervised datasets is notoriously costly. To reduce the cumbersome data annotation process, we propose a novel sparsely-annotated framework, in which we just annotate one 3D object per scene. Such a sparse annotation strategy could significantly reduce the heavy annotation burden, while inexact and incomplete sparse supervision may severely deteriorate the detection performance. To address this issue, we develop the SS3D++ method that alternatively improves 3D detector training and confident fully-annotated scene generation in a unified learning scheme. Using sparse annotations as seeds, we progressively generate confident fully-annotated scenes based on designing a missing-annotated instance mining module and reliable background mining module. Our proposed method produces competitive results when compared with SOTA weakly-supervised methods using the same or even more annotation costs. Besides, compared with SOTA fully-supervised methods, we achieve on-par or even better performance on the KITTI dataset with about 5× less annotation cost, and 90% of their performance on the Waymo dataset with about 15× less annotation cost. The additional unlabeled training scenes could further boost the performance. The code will be available at https://***/gaocq/SS3D2. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

Semantics-Guided Contrastive network for Zero-Shot Object Detection

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arXiv 2021年

作者： Yan, Caixia Chang, Xiaojun Luo, Minnan Liu, Huan Zhang, Xiaoqin Zheng, Qinghua The Ministry of Education Key Lab of Intelligent Networks and Network Security National Engineering Lab for Big Data Analytics School of Electronic and Information Engineering Xi'an Jiaotong University Shaanxi710049 China The ReLER Lab AAAI Faculty of Engineering and Information Technology University of Technology Sydney Australia The College of Computer Science and Artificial Intelligence Wenzhou University China

Zero-shot object detection (ZSD), the task that extends conventional detection models to detecting objects from unseen categories, has emerged as a new challenge in computer vision. Most existing approaches tackle the ZSD task with a strict mapping-transfer strategy that may lead to suboptimal ZSD results: 1) the learning process of these models neglects the available semantic information on unseen classes, which can easily bias towards the seen categories;2) the original visual feature space is not well-structured for the ZSD task due to the lack of discriminative information. To address these issues, we develop a novel Semantics-Guided Contrastive network for ZSD, named ContrastZSD, a detection framework that first brings contrastive learning mechanism into the realm of zero-shot detection. Particularly, ContrastZSD incorporates two semantics-guided contrastive learning subnets that contrast between region-category and region-region pairs respectively. The pairwise contrastive tasks take advantage of supervision signals derived from both the ground truth label and class similarity information. By performing supervised contrastive learning over those explicit semantic supervision, the model can learn more knowledge about unseen categories to avoid the bias problem to seen concepts, while optimizing the visual data structure to be more discriminative for better visual-semantic alignment. Extensive experiments are conducted on two popular benchmarks for ZSD, i.e., PASCAL VOC and MS COCO. Results show that our method outperforms the previous state-of-the-art on both ZSD and generalized ZSD tasks. © 2021, CC BY.

关键词： Object detection

Deep structural point process for learning temporal interaction networks

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arXiv 2021年

作者： Cao, Jiangxia Lin, Xixun Cong, Xin Guo, Shu Tang, Hengzhu Liu, Tingwen Wang, Bin Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security University of Chinese Academy of Sciences China National Computer Network Emergency Response Technical Team Coordination Center of China Xiaomi AI Lab Xiaomi Inc.

This work investigates the problem of learning temporal interaction networks. A temporal interaction network consists of a series of chronological interactions between users and items. Previous methods tackle this problem by using different variants of recurrent neural networks to model interaction sequences, which fail to consider the structural information of temporal interaction networks and inevitably lead to sub-optimal results. To this end, we propose a novel Deep Structural Point Process termed as DSPP for learning temporal interaction networks. DSPP simultaneously incorporates the topological structure and long-range dependency structure into the intensity function to enhance model expressiveness. To be specific, by using the topological structure as a strong prior, we first design a topological fusion encoder to obtain node embeddings. An attentive shift encoder is then developed to learn the long-range dependency structure between users and items in continuous time. The proposed two modules enable our model to capture the user-item correlation and dynamic influence in temporal interaction networks. DSPP is evaluated on three real-world datasets for both tasks of item prediction and time prediction. Extensive experiments demonstrate that our model achieves consistent and significant improvements over state-of-the-art baselines. Copyright © 2021, The Authors. All rights reserved.

关键词： Recurrent neural networks

Blockchain-Based Secure Authentication Scheme for Medical Data Sharing 5th

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Blockchain-Based Secure Authentication Scheme for Medical Da...

5th International Conference of Pioneer computer Scientists, Engineers and Educators, ICPCSEE 2019

作者： Cheng, Xu Chen, Fulong Xie, Dong Sun, Hui Huang, Cheng Qi, Zhuyun School of Computer and Information Anhui Normal University Wuhu241002 China Anhui Provincial Key Lab of Network and Information Security Wuhu241002 China School of Electronic and Computer Engineering Peking University Shenzhen518000 China

ISBN: (纸本)9789811501173

Data security is vital for medical cyber physical system (MCPS). The decentralization feature of blockchain is helpful to solve the problem that the secure authentication process is highly dependent on the trusted third party and implement data security transmission. In this paper, the blockchain technology is used to describe the security requirements in authentication process. A network model of MCPS based on blockchain is proposed. Through analysis of medical data storage architecture, data was ensured not to be tampered and trackable. The security threat was eliminated by bilinear mapping in the authentication process of medical data providers and users. The credibility problem of the trusted third party was avoided and the two-way authentication was realized between the hospital and blockchain node. The security analysis and performance test were carried out to verify the security and related performance of the authentication protocol. The results show that the MCPS based on blockchain realizes medical treatment data sharing, and meets safety requirements in the security authentication phase. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Blockchain

Triple-view Learning for Predicting Antibiotic Resistance Genes

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Triple-view Learning for Predicting Antibiotic Resistance Ge...

2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Luo, Shujie Wu, Haifang Zhao, Weizhong Jiang, Xingpeng He, Tingting Central China Normal University Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning Wuhan430079 China School of Computer Central China Normal University Hubei Wuhan430079 China National Language Resources Monitoring and Research Center for Network Media Central China Normal University Hubei Wuhan430079 China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin541004 China Guangxi Key Lab of Multi-source Information Mining and Security Guangxi Normal University Guilin541004 China

ISBN: (纸本)9781665401265

With the increasing resistance of bacteria to antibiotics, the problem of antibiotic resistance has become a major challenge in healthcare. Therefore, the accurate identification of antibiotic resistance genes (ARGs) in bacteria is particularly urgent and important. Although culture-based methods can accurately identify ARGs, they are limited by domain knowledge and are time-consuming. With the availability of whole genome sequence data, most existing methods rely on sequence alignment to calculate sequence similarity to predict ARGs. However, for ARGs that has low sequence identity with known sequences, the existing methods do not identify them well. To address the challenges, we propose a multiple view learning based framework for ARGs. Experimental results demonstrate that the proposed approach performs better than the selected representative baselines on the real-world dataset. © 2021 IEEE.

关键词： Bacteria

An improved RL-based framework for multiple biomedical event extraction via self-supervised learning

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An improved RL-based framework for multiple biomedical event...

2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Zhao, Yao Zhao, Weizhong Jiang, Xingpeng He, Tingting Su, Bianping Central China Normal University Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning Hubei Wuhan430079 China School of Computer Central China Normal University Hubei Wuhan430079 China National Language Resources Monitoring and Research Center for Network Media Central China Normal University Hubei Wuhan430079 China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin541004 China Guangxi Key Lab of Multi-source Information Mining and Security Guangxi Normal University Guilin541004 China Xi'An University of Architecture and Technology Shanxi Xi'an China

ISBN: (纸本)9781665401265

The main goal of biomedical event extraction is to structurally extract biomedical events from texts, however, the specificity of the domain makes both text modeling and data annotation very difficult. We propose a self-supervised learning-based data augmentation method in this paper and design specific augmentation strategies for biomedical entities and event triggers in biomedical texts, which solves the problem of sparse annotation data to some extent. In addition we improve the reinforcement learning-based event extraction method to improve the training efficiency of the model. The experiments on two datasets demonstrate the effectiveness of our method. © 2021 IEEE.

关键词： Reinforcement learning

FANS: fuzzing android native system services via automated interface analysis 20

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FANS: fuzzing android native system services via automated i...

Proceedings of the 29th USENIX Conference on security Symposium

作者： Baozheng Liu Chao Zhang Guang Gong Yishun Zeng Haifeng Ruan Jianwei Zhuge Institute of Network Science and Cyberspace Tsinghua University and Beijing National Research Center for Information Science and Technology Alpha Lab 360 Internet Security Center Department of Computer Science and Technology Tsinghua University

ISBN: (纸本)9781939133175

Android native system services provide essential supports and fundamental functionalities for user apps. Finding vulnerabilities in them is crucial for Android security. Fuzzing is one of the most popular vulnerability discovery solutions, yet faces several challenges when applied to Android native system services. First, such services are invoked via a special interprocess communication (IPC) mechanism, namely binder, via service-specific interfaces. Thus, the fuzzer has to recognize all interfaces and generate interface-specific test cases automatically. Second, effective test cases should satisfy the interface model of each interface. Third, the test cases should also satisfy the semantic requirements, including variable dependencies and interface *** this paper, we propose an automated generation-based fuzzing solution FANS to find vulnerabilities in Android native system services. It first collects all interfaces in target services and uncovers deep nested multi-level interfaces to test. Then, it automatically extracts interface models, including feasible transaction code, variable names and types in the transaction data, from the abstract syntax tree (AST) of target interfaces. Further, it infers variable dependencies in transactions via the variable name and type knowledge, and infers interface dependencies via the generation and use relationship. Finally, it employs the interface models and dependency knowledge to generate sequences of transactions, which have valid formats and semantics, to test interfaces of target services. We implemented a prototype of FANS from scratch and evaluated it on six smartphones equipped with a recent version of Android, i.e., android-9.0.0_r46 , and found 30 unique vulnerabilities deduplicated from thousands of crashes, of which 20 have been confirmed by Google. Surprisingly, we also discovered 138 unique Java exceptions during fuzzing.

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