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Secure RFID-Assisted Authentication Protocol for Vehicular Cloud Computing Environment

IEEE Transactions on Intelligent Transportation Systems 2024年第9期25卷 12528-12537页

作者： Saleem, Muhammad Asad Li, Xiong Mahmood, Khalid Tariq, Tayyaba Alenazi, Mohammed J. F. Das, Ashok Kumar University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Institute for Cyber Security School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Sichuan University Key Laboratory of Data Protection and Intelligent Management Ministry of Education Chengdu610065 China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518000 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan National Yunlin University of Science and Technology Graduate School of Engineering Science and Technology Yunlin Douliu64002 Taiwan King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh11451 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500032 India

Vehicular network technology has made substantial advancements in recent years in the field of Intelligent Transportation Systems. Vehicular Cloud Computing (VCC) has emerged as a novel paradigm with a substantial increase in data exchange within Vehicular ad-hoc networks (VANETs). VCC integrates cloud computing, vehicular networking, and Internet of Things (IoT) technologies. It enables Infrastructure-to-Vehicle (I2V), Vehicle-to-Vehicle (V2V), and Vehicle-to-Device (V2D) communication. VCC optimizes vehicle, cloud infrastructure, and IoT resources while addressing significant communication security and vehicle-user privacy challenges. To address these issues, we developed an RFID-based authentication protocol for VCC based on a Henon map using a hash function. In addition, we also incorporated a Physical Unclonable Function (PUF) to resist physical tampering attacks. We validate the protocol's security formally and informally. The formal security analysis is conducted through a widely used RoR model. We use the Scyther simulation tool to verify the proposed protocol's security against various attacks. Moreover, we compare the performance of our protocol with similar existing protocols across important performance parameters such as communication and computation overheads and security attributes. The proposed protocol yields substantial improvements, demonstrating a 40.74% reduction in computation overhead and a 13.03% decrease in communication overhead as compared to related protocols, delivering both enhanced performance and resource efficiency. The analysis demonstrates its capacity to support secure communication in the VCC environment and satisfy desirable security attributes. © 2024 IEEE.

关键词： Authentication

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PPA: Preference Profiling Attack Against Federated Learning 30

PPA: Preference Profiling Attack Against Federated Learning

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30th Annual Network and Distributed System security Symposium, NDSS 2023

作者： Zhou, Chunyi Gao, Yansong Fu, Anmin Chen, Kai Dai, Zhiyang Zhang, Zhi Xue, Minhui Zhang, Yuqing School of Computer Science and Engineering Nanjing University of Science and Technology China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Science China Data61 CSIRO Syndey Australia National Computer Network Intrusion Protection Center University of Chinese Academy of Science China

ISBN: (纸本)1891562835

—Federated learning (FL) trains a global model across a number of decentralized users, each with a local dataset. Compared to traditional centralized learning, FL does not require direct access to local datasets and thus aims to mitigate data privacy concerns. However, data privacy leakage in FL still exists due to inference attacks, including membership inference, property inference, and data inversion. In this work, we propose a new type of privacy inference attack, coined Preference Profiling A ttack (PPA), t hat accurately profiles t he p rivate p references o f a l ocal u ser, e .g., m ost liked (disliked) items from the client’s online shopping and most common expressions from the user’s selfies. I n g eneral, P PA can profile t op-k (i.e., k = 1, 2, 3 a nd k = 1 i n p articular) preferences contingent on the local client (user)’s characteristics. Our key insight is that the gradient variation of a local user’s model has a distinguishable sensitivity to the sample proportion of a given class, especially the majority (minority) class. By observing a user model’s gradient sensitivity to a class, PPA can profile the sample proportion of the class in the user’s local dataset, and thus the user’s preference of the class is exposed. The inherent statistical heterogeneity of FL further facilitates PPA. We have extensively evaluated the PPA’s effectiveness using four datasets (MNIST, CIFAR10, RAF-DB and Products-10K). Our results show that PPA achieves 90% and 98% top-1 attack accuracy to the MNIST and CIFAR10, respectively. More importantly, in real-world commercial scenarios of shopping (i.e., Products-10K) and social network (i.e., RAF-DB), PPA gains a top-1 attack accuracy of 78% in the former case to infer the most ordered items (i.e., as a commercial competitor), and 88% in the latter case to infer a victim user’s most often facial expressions, e.g., disgusted. The top-3 attack accuracy and top-2 accuracy is up to 88% and 100% for the Products-10K and RAF-DB, re

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Spatial-temporal changes and driving factors of eco-environmental quality in the Three-North region of China

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Journal of Arid Land 2023年第3期15卷 231-252页

作者： LONG Yi JIANG Fugen DENG Muli WANG Tianhong SUN Hua Research Center of Forestry Remote Sensing&Information Engineering Central South University of Forestry and TechnologyChangsha 410004China Key Laboratory of Forestry Remote Sensing Based Big Data&Ecological Security for Hunan Province Changsha 410004China Key Laboratory of National Forestry&Grassland Administration on Forest Resources Management and Monitoring in Southern Area Changsha 410004China

Eco-environmental quality is a measure of the suitability of the ecological environment for human survival and socioeconomic *** the spatial-temporal distribution and variation trend of eco-environmental quality is essential for environmental protection and ecological *** remote sensing ecological index(RSEI)can quickly and objectively quantify eco-environmental quality and has been extensively utilized in regional ecological environment *** this paper,Moderate Resolution Imaging Spectroradiometer(MODIS)images during the growing period(July-September)from 2000 to 2020 were obtained from the Google Earth Engine(GEE)platform to calculate the RSEI in the three northern regions of China(the Three-North region).The Theil-Sen median trend method combined with the Mann-Kendall test was used to analyze the spatial-temporal variation trend of eco-environmental quality,and the Hurst exponent and the Theil-Sen median trend were superimposed to predict the future evolution trend of eco-environmental *** addition,ten variables from two categories of natural and anthropogenic factors were analyzed to determine the drivers of the spatial differentiation of eco-environmental quality by the geographical *** results showed that from 2000 to 2020,the RSEI in the Three-North region exhibited obvious regional characteristics:the RSEI values in Northwest China were generally between 0.2 and 0.4;the RSEI values in North China gradually increased from north to south,ranging from 0.2 to 0.8;and the RSEI values in Northeast China were mostly above *** average RSEI value in the Three-North region increased at an average growth rate of 0.0016/a,showing the spatial distribution characteristics of overall improvement and local degradation in eco-environmental quality,of which the areas with improved,basically stable and degraded eco-environmental quality accounted for 65.39%,26.82%and 7.79%of the total study area,*** Hurst

关键词： eco-environmental quality remote sensing ecological index Google Earth Engine Hurst exponent geographical detector Three-North region of China

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Semantic-Enhanced Static Vulnerability Detection in Baseband Firmware

Semantic-Enhanced Static Vulnerability Detection in Baseband...

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International Conference on Software engineering (ICSE)

作者： Yiming Liu Cen Zhang Feng Li Yeting Li Jianhua Zhou Jian Wang Lanlan Zhan Yang Liu Wei Huo Institute of Information Engineering CAS School of Cyber Security UCAS Beijing China Key Laboratory of Network Assessment Technology CAS Beijing Key Laboratory of Network Security and Protection Technology Nanyang Technological University Singapore

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

Cellular network is the infrastructure of mobile communication. Baseband firmware, which carries the implementation of cellular network, has critical security impact on its vulnerabilities. To handle the inherent complexity in cellular communication, cellular protocols are usually implemented as message-centric systems, containing the common message processing phase and message specific handling phase. Though the latter takes most of the code (99.67%) and exposed vulnerabilities (74%), it is rather under-studied: existing detectors either cannot sufficiently analyze it or focused on analyzing the former phase. To fill this gap, we proposed a novel semantic-enhanced static vulnerability detector named BVFinder focusing on message specific phase vulnerability detection. Generally, it identifies a vulnerability by locating whether a predefined sensitive memory operation is tainted by any attacker-controllable input. Specifically, to reach high automation and preciseness, it made two key improvements: a semantic-based taint source identification and an enhanced taint propagation. The former employs semantic search techniques to identify registers and memory offsets that carry attacker-controllable inputs. This is achieved by matching the inputs to their corresponding message and data types using textual features and addressing patterns within the assemblies. On the other hand, the latter technology guarantees effective taint propagation by employing additional indirect call resolution algorithms. The evaluation shows that BVFinder outperforms the state-of-the-art detectors by detecting three to four times of amount of vulnerabilities in the dataset. Till now, BVFinder has found four zero-day vulnerabilities, with four CVEs and 12,410 USD bounty assigned. These vulnerabilities can potentially cause remote code execution to phones using Samsung shannon baseband, affecting hundreds of millions of end devices.

关键词： Cellular networks Codes Baseband Automation Semantic search Detectors Registers

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Leveraging semantic relations in code and data to enhance taint analysis of embedded systems 24

Leveraging semantic relations in code and data to enhance ta...

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Proceedings of the 33rd USENIX Conference on security Symposium

作者： Jiaxu Zhao Yuekang Li Yanyan Zou Zhaohui Liang Yang Xiao Yeting Li Bingwei Peng Nanyu Zhong Xinyi Wang Wei Wang Wei Huo Institute of Information Engineering Chinese Academy of Sciences China and School of Cyber Security University of Chinese Academy of Sciences China and Key Laboratory of Network Assessment Technology Chinese Academy of Sciences China and Beijing Key Laboratory of Network Security and Protection Technology China University of New South Wales Australia Institute of Information Engineering Chinese Academy of Sciences China and Key Laboratory of Network Assessment Technology Chinese Academy of Sciences China and Beijing Key Laboratory of Network Security and Protection Technology China

ISBN: (纸本)9781939133441

IoT devices have significantly impacted our daily lives, and detecting vulnerabilities in embedded systems early on is critical for ensuring their security. Among the existing vulnerability detection techniques for embedded systems, static taint analysis has been proven effective in detecting severe vulnerabilities, such as command injection vulnerabilities, which can cause remote code execution. Nevertheless, static taint analysis is faced with the problem of identifying sources comprehensively and *** paper presents LARA, a novel static taint analysis technique to detect vulnerabilities in embedded systems. The design of LARA is inspired by an observation that pertains to semantic relations within and between the code and data of embedded software: user input entries can be categorized as URIs or keys (data), and identifying their handling code (code) and relations can help systematically and comprehensively identify the sources for taint analysis. Transforming the observation into a practical methodology poses challenges. To address these challenges, LARA employs a combination of pattern-based static analysis and large language model(LLM)-aided analysis, aiming to replicate how human experts would utilize the findings during analysis and enhance it. The pattern-based static analysis simulates human experience, while the LLM-aided analysis captures the way human experts perceive code semantics. We implemented LARA and evaluated it on 203 IoT devices from 21 vendors. In general, LARA detects 556 and 602 more known vulnerabilities than SATC and KARONTE while reducing false positives by 57.0% and 54.3%. Meanwhile, with more sources and sinks from LARA, EMTAINT can detect 245 more vulnerabilities. To date, LARA has found 245 0-day vulnerabilities in 57 devices, all of which were confirmed or fixed with 162 CVE IDs assigned.

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ESRFuzzer:an enhanced fuzzing framework for physical SOHO router devices to discover multi-Type vulnerabilities

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Cybersecurity 2021年第1期4卷 364-385页

作者： Yu Zhang Wei Huo Kunpeng Jian Ji Shi Longquan Liu Yanyan Zou Chao Zhang Baoxu Liu Institute of Information Engineering Chinese Academy of SciencesBeijingChina School of Cyber Security University of Chinese Academy of SciencesBeijingChina Key Laboratory of Network Assessment Technology Chinese Academy of SciencesBeijingChina Beijing Key Laboratory of Network Security and Protection Technology BeijingChina Institute for Network Science and Cyberspace Tsinghua UniversityBeijingChina Beijing National Research Center for Information Science and Technology BeijingChina

SOHO(small office/home office)routers provide services for end devices to connect to the Internet,playing an important role in ***,security vulnerabilities pervasively exist in these routers,especially in the web server modules,greatly endangering end *** discover these vulnerabilities,fuzzing web server modules of SOHO routers is the most popular ***,its effectiveness is limited due to the lack of input specification,lack of routers’internal running states,and lack of testing environment recovery ***,existing works for device fuzzing are more likely to detect memory corruption *** this paper,we propose a solution ESRFuzzer to address these *** is a fully automated fuzzing framework for testing physical SOHO *** continuously and effectively generates test cases by leveraging two input semantic models,i.e.,key-VALUE data model and CONF-READ communication model,and automatically recovers the testing environment with power *** also coordinates diversified mutation rules with multiple monitoring mechanisms to trigger multi-type *** the guidance of the two semantic models,ESRFuzzer can work in two ways:general mode fuzzing and D-CONF mode *** mode fuzzing can discover both issues which occur in the CONF and READ operation,while D-CONF mode fuzzing focus on the READ-op issues especially missed by general mode *** ran ESRFuzzer on 10 popular routers across five *** total,it discovered 136 unique issues,120 of which have been confirmed as 0-day vulnerabilities we *** an improvement of SRFuzzer,ESRFuzzer have discovered 35 previous undiscovered READ-op issues that belong to three vulnerability types,and 23 of them have been confirmed as 0-day vulnerabilities by *** experimental results show that ESRFuzzer outperforms state-of-the-art solutions in terms of types and number of vulnerabilities found.

关键词： Fuzzing IoT Automatic vulnerability detection

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A Web Attack Detection Method Based on DistilBERT and Feature Fusion for Power Micro-Application Server 2

A Web Attack Detection Method Based on DistilBERT and Featur...

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2nd International Conference on Advanced Electronics, Electrical and Green Energy, AEEGE 2023

作者： Nige, Li Lu, Chen Lei, Zhao Zhenning, Teng Zhiqiang, Wang Yiyang, Shao Xiaolin, Gui State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology State Grid Smart Grid Research Institute Co.Ltd. Nanjing China Xi'an Jiaotong University Faculty of Electronic and Information Engineering Shaanxi Province Key Laboratory of Computer Network Xi'an China

ISBN: (纸本)9798350322866

The widespread use of power micro-applications has significantly expanded the attack surface of their server side, thus increasing the risk of vulnerability attacks such as SQL injection, XSS, and CRLF injection. To ensure the security of power systems, these attacks must be detected precisely and timely. Therefore, this paper proposes a server-side web attack detection method based on DistilBERT and feature fusion for HTTP and HTTPS requests. The method treats HTTP and HTTPS requests as text data, specifically, extracts deep semantic features using DistilBERT, and fuses them with well-designed empirical features to comprehensively characterize and classify the request. Consequently, anomalous requests can be detected. The experimental results show that the accuracy, precision, recall, and F1 score of the method on HTTP CSIC 2010 and FWAF datasets are close to or higher than 99%. Compared with other methods as far as we known, this method has better performance and efficiency. © 2023 IEEE.

关键词： Cybersecurity

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Adaptive constrained population extremal optimisation-based robust proportional-integral-derivation frequency control method for an islanded microgrid

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IET Cyber-Systems and Robotics 2021年第3期3卷 210-227页

作者： Kang-Di Lu Guo-Qiang Zeng Wuneng Zhou College of Information Sciences and Technology Donghua UniversityShanghaiChina College of Cyber Security and the National-Local Joint Engineering Research Center of Network Security Detection and Protection Technology Jinan UniversityGuangzhouChina National-Local Joint Engineering Laboratory of Digitalise Electrical Design Technology Wenzhou UniversityWenzhouChina

The expected penetration of renewable sources is driving the islanded microgrid towards uncertainties,which have highly influence the reliability and complexities of frequency *** alleviate the influence caused by load fluctuations and inherent variability of renewable sources,this article proposes an optimised robust proportional-integralderivation(PID)frequency control method by taking full advantage of a robust control strategy while simultaneously maintaining the basic characteristics of a PID *** the process of iterated optimisation,a weighted objective function is used to balance the tracking error performance,robust stability and disturbance attenuation ***,the robust PID frequency(RPIDF)controller is determined by an adaptive constrained population extremal optimisation algorithm based on self-adaptive penalty constraint-handling *** proposed control method is examined on a typical islanded microgrid,and the control performance is evaluated under various disturbances and parametric ***,the simulation results indicate that the fitness value of the proposed method is 1.7872,which is lower than 2.9585 and 3.0887 obtained by two other evolutionary algorithms-based RPIDF ***,the comprehensive simulation results fully demonstrate that the proposed method is superior to other comparison methods in terms of four performance indices on the most considered scenarios.

关键词： constrained evolutionary algorithm frequency control islanded microgrid population extremal optimisation robust PID controller

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Analysis of EEG in unipolar and bipolar depression based on phase synchronization 4

Analysis of EEG in unipolar and bipolar depression based on ...

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4th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2021

作者： Duan, Lijuan Liu, Hongli Wang, Changming Qiao, Yuanhua Beijing University of Technology Natl. Engineering Laboratory for Critical Technologies of Information Security Classified Protection Beijing China Capital Medical University Beijing Xuanwu Hospital Beijing China

ISBN: (纸本)9781665413220

Resting-state EEG studies have shown significant differences in functional connectivity networks between patients with unipolar and bipolar depression as well as healthy controls (HC). However, the present study has paid little attention to identify the EEG resting-state functional connectivity patterns of bipolar depression patients (BD). This paper proposes a method for unipolar and bipolar disorder recognition based on phase-synchronized feature fusion. From the perspective of time-frequency domain, phase lay index, phase locking value and weighted phase lay index and their mutual fusion were extracted, respectively. Feature matrix were sent to different machine learning classifiers to classify in the different bands. It can be seen from the classification results that the fusion feature PLV_ PLI _WPLI more effect than the other feature and the best performance in the beta band. Otherwise, the visualization results showed that the patients main brain regions were distributed in the frontal lobe, temporal lobe and parietal lobe compared with normal person. It revealed that new light on the pathological mechanism of BD and suggests that EEG resting-state phase synchronization analysis may identify potentially effective biomarkers for its clinical diagnosis in our study. © 2021 IEEE.

关键词： Electroencephalography

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An automated method with anchor-free detection and U-shaped segmentation for nuclei instance segmentation 2

An automated method with anchor-free detection and U-shaped ...

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2nd ACM International Conference on Multimedia in Asia, MMAsia 2020

作者： Feng, Xuan Duan, Lijuan Chen, Jie Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Trusted Computing Beijing100124 China Natl. Engineering Laboratory of Critical Technologies of Information Security Classified Protection Beijing100124 China Peng Cheng Laboratory Shenzhen518055 China

ISBN: (纸本)9781450383080

Nuclei segmentation plays an important role in cancer diagnosis. Automated methods for digital pathology become popular due to the developments of deep learning and neural networks. However, this task still faces challenges. Most of current techniques cannot be applied directly because of the clustered state and the large number of nuclei in images. Moreover, anchor-based methods for object detection lead a huge amount of calculation, which is even worse on pathological images with a large target density. To address these issues, we propose a novel network with an anchor-free detection and a U-shaped segmentation. An altered feature enhancement module is attached to improve the performance in dense target detection. Meanwhile, the U-Shaped structure in segmentation block ensures the aggregation of features in different dimensions generated from the backbone network. We evaluate our work on a Multi-Organ Nuclei Segmentation dataset from MICCAI 2018 challenge. In comparisons with others, our proposed method achieves state-of-the-art performance. © 2021 ACM.

关键词： Object detection

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