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arXiv 2024年

作者： Tong, Xin Jin, Bo Lin, Zhi Wang, Binjun Yu, Ting Cheng, Qiang School of Information and Network Security People’s Public Security University of China Beijing China National Engineering Research Center of Classified Protection and Safeguard Technology for Cybersecurity The Third Research Institute of the Ministry of Public Security of China Shanghai China Department of Engineering Physics Tsinghua University China Science and Technology Information Bureau The Ministry of Public Security of China Beijing China The Third Research Institute of the Ministry of Public Security of China Shanghai China

Large Language Models (LLMs) have demonstrated significant potential and effectiveness across multiple application domains. To assess the performance of mainstream LLMs in public security tasks, this study aims to construct a specialized evaluation benchmark tailored to the Chinese public security domain—CPSDbench. CPSDbench integrates datasets related to public security collected from real-world scenarios, supporting a comprehensive assessment of LLMs across four key dimensions: text classification, information extraction, question answering, and text generation. Furthermore, this study introduces a set of innovative evaluation metrics designed to more precisely quantify the efficacy of LLMs in executing tasks related to public security. Through the in-depth analysis and evaluation conducted in this research, we not only enhance our understanding of the performance strengths and limitations of existing models in addressing public security issues but also provide references for the future development of more accurate and customized LLM models targeted at applications in this field. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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DRL-based Impairment-aware Resource Allocation Algorithm in C + L Band Elastic Optical Networks

DRL-based Impairment-aware Resource Allocation Algorithm in ...

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International Conference on Optical Communications and Networks (ICOCN)

作者： Dan Yan Nan Feng Zhiqun Gu Xiaobo Zuo Shihao Fan Jijun Zhao Hebei Key Laboratory of Security and Protection Information Sensing and Processing Hebei University of Engineering Handan China 54th Institute of China Electronics Technology Corporation Shijiazhuang China Hebei Key Laboratory of Photonic Information Technology and Application(PITA) Shijiazhuang China State Key Laboratory of Information Photonics and Optical Communication Beijing University of Posts and Telecommunications Beijing China

We propose an impairment-aware resource allocation algorithm for C+L-band elastic optical networks based on deep reinforcement learning (DRL). Simulation results show that the proposed algorithm effectively reduces th... 详细信息

ISBN: (数字)9798350367652

ISBN: (纸本)9798350367669

关键词： Integrated optics Stimulated emission Simulation Raman scattering Optical fiber networks Deep reinforcement learning Resource management

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Energy-efficient and Privacy-preserving Edge Intelligence for 6G-Empowered Intelligent Transportation Systems

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IEEE Network 2025年

作者： Zhu, Chenlu Fan, Xiaoxuan Deng, Xianjun Xiao, Ziheng Guo, Tingting Liu, Shenghao Sun, Jiaqi Park, Jong Hyuk Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Wuhan Kedao Geographic Information Engineering Co. Wuhan China Department of Computer Science and Engineering Seoul National University of Science and Technology Seoul Korea Republic of

6G-empowered Intelligent Transportation Systems (ITS) generate large amounts of data through millions of devices and sensors at the terminal and network edge. Edge intelligence advances the frontier of data-driven artificial intelligence (AI) to the network edge, which enables AI applications to better utilize edge data. Broader connectivity, higher device density, and stronger data exchange present challenges for reliable device communication and privacy data protection in 6G-empowered ITS. To address these challenges, this paper proposes an energy-efficient and privacy-preserving edge intelligence framework named EP-EI. EP-EI deploys a deep reinforcement learning-based cluster head selection model at the edge to ensure energy-efficient communication for terminal devices. Meanwhile, EP-EI designs a privacy-aware hierarchical computing framework for terminal-edge-cloud to prevent privacy leakage. Furthermore, the application scenarios, challenges, and future directions of EP-EI are discussed. Finally, simulation experiments in a specific intelligent transportation case study demonstrate that EP-EI can provide energy-efficient and privacy-protecting services. © 2025 IEEE.

关键词： Differential privacy

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Underwater Gas Leakage Flow Detection and Classification Based on Multibeam Forward-Looking Sonar

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哈尔滨工程大学学报（英文版） 2024年第3期23卷 674-687页

作者： Yuanju Cao Chao Xu Jianghui Li Tian Zhou Longyue Lin Baowei Chen Southampton Ocean Engineering Joint Institute Harbin Engineering UniversityHarbin 150001China College of Underwater Acoustic Engineering Harbin Engineering UniversityHarbin 150001China National Key Laboratory of Underwater Acoustic Technology Harbin Engineering UniversityHarbin 150001China Key Laboratory of Marine Information Acquisition and Security(Harbin Engineering University) Ministry of Industry and Information TechnologyHarbin 150001China State Key Laboratory of Marine Environmental Science College of Ocean and Earth SciencesXiamen UniversityXiamen 361102China

The risk of gas leakage due to geological flaws in offshore carbon capture, utilization, and storage, as well as leakage from underwater oil or gas pipelines, highlights the need for underwater gas leakage monitoring technology. Remotely operated vehicles(ROVs) and autonomous underwater vehicles(AUVs) are equipped with high-resolution imaging sonar systems that have broad application potential in underwater gas and target detection tasks. However, some bubble clusters are relatively weak scatterers, so detecting and distinguishing them against the seabed reverberation in forward-looking sonar images are challenging. This study uses the dual-tree complex wavelet transform to extract the image features of multibeam forward-looking sonar. Underwater gas leakages with different flows are classified by combining deep learning theory. A pool experiment is designed to simulate gas leakage, where sonar images are obtained for further processing. Results demonstrate that this method can detect and classify underwater gas leakage streams with high classification accuracy. This performance indicates that the method can detect gas leakage from multibeam forward-looking sonar images and has the potential to predict gas leakage flow.

关键词： Carbon capture utilization and storage(CCUS) Gas leakage Forward-looking sonar Dual-tree complex wavelet transform(DT-CWT) Deep learning

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A Novel Scheme to Reduce the ONU's DSP Complexity for IMDD-OFDMA-PON 20

A Novel Scheme to Reduce the ONU's DSP Complexity for IMDD-O...

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20th International Conference on Optical Communications and Networks, ICOCN 2022

作者： Lian, Bin Feng, Nan Wei, Zhongcheng School of Water Conservancy and Hydroelectric Power Hebei University of Engineering Handan056038 China Hebei University of Engineering Hebei Key Laboratory of Security and Protection Information Sensing and Processing Handan056038 China 54th Research Institute of Cetc Shijiazhuang050081 China Hebei Key Laboratory of Photonic Information Technology and Application Shijiazhuang050081 China School of Information and Electrical Engineering Hebei University of Engineering Handan056038 China

ISBN: (数字)9781665458986

ISBN: (纸本)9781665458986

The orthogonal frequency division multiplexing passive optical network (OFDM-PON) is one of the significant topics for the next generation PON technology, and how to minimize the size of DSP processing and the number of sampling rate is paramount important to realize cost saving. This paper investigates a novel scheme for reducing the ONU's DSP complexity for intensity-modulated and direct-detection (IM-DD)-OFDMA-PON. Results efficiently verify the proposed scheme as well as ensure the transmission performance. © 2022 IEEE.

关键词： Passive optical networks

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ACETest: Automated Constraint Extraction for Testing Deep Learning Operators

arXiv

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arXiv 2023年

作者： Shi, Jingyi Xiao, Yang Li, Yuekang Li, Yeting Yu, Dongsong Yu, Chendong Su, Hui Chen, Yufeng Huo, Wei Institute of Information Engineering CAS School of Cyber Security UCAS Beijing China University of New South Wales Sydney Australia Zhongguancun Laboratory Beijing China Key Laboratory of Network Assessment Technology CAS China Beijing Key Laboratory of Network Security and Protection Technology China

Deep learning (DL) applications are prevalent nowadays as they can help with multiple tasks. DL libraries are essential for building DL applications. Furthermore, DL operators are the important building blocks of the DL libraries, that compute the multi-dimensional data (tensors). Therefore, bugs in DL operators can have great impacts. Testing is a practical approach for detecting bugs in DL operators. In order to test DL operators effectively, it is essential that the test cases pass the input validity check and are able to reach the core function logic of the operators. Hence, extracting the input validation constraints is required for generating high-quality test cases. Existing techniques rely on either human effort or documentation of DL library APIs to extract the constraints. They cannot extract complex constraints and the extracted constraints may differ from the actual code implementation. To address the challenge, we propose ACETest, a technique to automatically extract input validation constraints from the code to build valid yet diverse test cases which can effectively unveil bugs in the core function logic of DL operators. For this purpose, ACETest can automatically identify the input validation code in DL operators, extract the related constraints and generate test cases according to the constraints. The experimental results on popular DL libraries, TensorFlow and PyTorch, demonstrate that ACETest can extract constraints with higher quality than state-of-the-art (SOTA) techniques. Moreover, ACETest is capable of extracting 96.4% more constraints and detecting 1.95 to 55 times more bugs than SOTA techniques. In total, we have used ACETest to detect 108 previously unknown bugs on TensorFlow and PyTorch, with 87 of them confirmed by the developers. Lastly, five of the bugs were assigned with CVE IDs due to their security impacts. © 2023, CC BY.

关键词： Libraries

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Sherlock on specs: building LTE conformance tests through automated reasoning 23

Sherlock on specs: building LTE conformance tests through au...

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Proceedings of the 32nd USENIX Conference on security Symposium

作者： Yi Chen Di Tang Yepeng Yao Mingming Zha XiaoFeng Wang Xiaozhong Liu Haixu Tang Baoxu Liu Indiana University Bloomington CAS-KLONAT Key Laboratory of Network Assessment Technology CAS BKLONSPT Beijing Key Laboratory of Network Security and Protection Technology Institute of Information Engineering CAS and School of Cyber Security University of Chinese Academy of Sciences Worcester Polytechnic Institute

ISBN: (纸本)9781939133373

Conformance tests are critical for finding security weaknesses in carrier network systems. However, building a conformance test procedure from specifications is challenging, as indicated by the slow progress made by the 3GPP, particularly in developing security-related tests, even with a large amount of resources already committed. A unique challenge in building the procedure is that a testing system often cannot directly invoke the condition event in a security requirement or directly observe the occurrence of the operation expected to be triggered by the event. Addressing this issue requires an event chain to be found, which once initiated leads to a chain reaction so the testing system can either indirectly triggers the target event or indirectly observe the occurrence of the expected event. To find a solution to this problem and make progress towards a fully automated conformance test generation, we developed a new approach called Contester, which utilizes natural language processing and machine learning to build an event dependency graph from a 3GPP specification, and further perform automated reasoning on the graph to discover the event chains for a given security requirement. Such event chains are further converted by Contester into a conformance test procedure, which is then executed by a testing system to evaluate the compliance of user equipment (UE) with the security requirement. Our evaluation shows that given 22 security requirements from the LTE NAS specification, Contester successfully generated over a hundred test procedures in just 25 minutes. After running these procedures on 22 popular UEs including iPhone 13, Pixel 5a and IoT devices, our approach uncovered 197 security requirement violations, with 190 never reported before, rendering these devices to serious security risks such as MITM, fake base station and reply attacks.

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Isrs Impact-Reduced Routing, Modulation, Band, and Spectrum Allocation Algorithm in C+L-Bands Elastic Optical Networks

SSRN

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SSRN 2023年

作者： Shen, Lingfei Feng, Nan Liu, Yunxuan Yan, Dan Lv, Jingjing Hu, Jinhua Ren, Danping Zhao, Jijun Hebei Key Laboratory of Security and Protection Information Sensing and Processing The School of Information and Electrical Engineering Hebei University of Engineering Handan056038 China Hebei Key Laboratory of Photonic Information Technology and Application The 54th research Institute of CETC Shijiazhuang050081 China

C+L-bands elastic optical networks (EONs) cope with growing traffic demands by leveraging the C and L bands of deployed optical fibers. However, the inter-band stimulated Raman scattering (ISRS) becomes more serious and degrades the quality of transmission (QoT) of services, which makes it difficult to establish lightpaths for services in the routing, modulation, band, and spectrum allocation (RMBSA). Therefore, the ISRS impact on services should be considered to guarantee the lightpaths establishment. In this paper, we propose a ISRS impact-reduced RMBSA algorithm that reduces the ISRS impact on services at the RMBSA process. In the routing phase, selecting the route with more available contiguous aligned frequency slots (FSs) and shorter transmission distance, the less ISRS impact of requests can be suffered and the spectrum resources of each link can be used as much as possible. In the band and spectrum allocation, allocation of higher-order modulation format requests to the C-band to reduce the ISRS impact from C-band to L-band, and centralized assignment of more required FSs requests to reduce the ISRS impact of these requests on other requests. Simulation results indicate that the proposed algorithm can effectively decrease the blocking probability, improve the resource utilization, and improve the request average optical signal-to-noise ratio. © 2023, The Authors. All rights reserved.

关键词： Blocking probability

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Online prediction of EEG based on KRLST algorithm

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High technology Letters 2021年第4期27卷 357-364页

作者： Lian Zhaoyang Duan Lijuan Chen Juncheng Qiao Yuanhua Miao Jun Faculty of Information Technology Beijing University of TechnologyBeijing 100124P.R.China Beijing Key Laboratory of Trusted Computing Beijing 100124P.R.China National Engineering Laboratory for Key Technologies of Information Security Level Protection Beijing 100124P.R.China Faculty of Sciences Beijing University of TechnologyBeijing 100124P.R.China Beijing Key Laboratory of Internet Culture and Digital Dissemination Research School of Computer ScienceBeijing Information Science and Technology UniversityBeijing 100101P.R.China

Kernel adaptive algorithm is an extension of adaptive algorithm in nonlinear,and widely used in the field of non-stationary signal *** the distribution of classic data sets seems relatively regular and simple in time *** distribution of the electroencephalograph(EEG)signal is more randomness and non-stationarity,so online prediction of EEG signal can further verify the robustness and applicability of kernel adaptive ***’s more,the purpose of modeling and analyzing the time series of EEG signals is to discover and extract valuable information,and to reveal the internal relations of EEG *** time series prediction of EEG plays an important role in EEG time series *** this paper,kernel RLS tracker(KRLST)is presented to online predict the EEG signals of motor imagery and compared with other 13 kernel adaptive *** experimental results show that KRLST algorithm has the best effect on the brain computer interface(BCI)dataset.

关键词： brain computer interface(BCI) kernel adaptive algorithm online prediction of electroencephalograph(EEG)

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RSFuzzer: Discovering Deep SMI Handler Vulnerabilities in UEFI Firmware with Hybrid Fuzzing

RSFuzzer: Discovering Deep SMI Handler Vulnerabilities in UE...

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IEEE Symposium on security and Privacy

作者： Jiawei Yin Menghao Li Yuekang Li Yong Yu Boru Lin Yanyan Zou Yang Liu Wei Huo Jingling Xue Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing Key Laboratory of Network Assessment Technology Chinese Academy of Sciences Beijing Key Laboratory of Network Security and Protection Technology Nanyang Technological University UNSW Sydney

System Management Mode (SMM) is a secure operation mode for x86 processors supported by Unified Extensible Firmware Interface (UEFI) firmware. SMM is designed to provide a secure execution environment to access highly privileged data or control low-level hardware (such as power management). The programs running in SMM are called SMM drivers and System Management Interrupt (SMI) handlers are the most important components of SMM drivers since they are the only components to receive and handle data from outside the SMM execution environment. Although SMM can serve as an extra layer of protection when the operating system is compromised, vulnerabilities in SMM drivers, especially SMI handlers, can invalidate this protection and cause severe damages to the device. Thus, early detection of SMI handler vulnerabilities is important for UEFI firmware *** this end, researchers have proposed to use hybrid fuzzing techniques for detecting SMI handler vulnerabilities. Particularly, Intel has developed a hybrid fuzzer called Excite and uses it to secure Intel products. Although existing hybrid fuzzing techniques can detect vulnerabilities in SMI handlers, their effectiveness is limited due to two major pitfalls: 1) They can only feed input through the most common input interface to SMI handlers, lacking the ability to utilize other input interfaces. 2) They have no awareness of variables shared by multiple SMI handlers, lacking the ability to explore code segments related to such variables. By addressing the challenges faced by existing works, we propose RSFuzzer, a hybrid greybox fuzzing technique which can learn input interface and format information and detect deeply hidden vulnerabilities which are triggered by invoking multiple SMI handlers. We implemented RSFuzzer and evaluated it on 16 UEFI firmware images provided by six vendors. The experiment results show that RSFuzzer can cover 617% more basic blocks and detect 828% more vulnerabilities on average than the stat

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