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Gradient Boosting-Accelerated Evolution for Multiple-Fault Diagnosis

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Gradient Boosting-Accelerated Evolution for Multiple-Fault D...

Design, Automation and Test in Europe Conference and Exhibition

作者： Hongfei Wang Chenliang Luo Deqing Zou Hai Jin Wenjie Cai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Wuhan China Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security School of Cyber Science and Engineering Wuhan China Huazhong University of Science and Technology Wuhan China Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan China College of Public Administration Wuhan China

ISBN: (数字)9783981926385

ISBN: (纸本)9798350348606

Logic diagnosis is a key step in yield learning. Multiple faults diagnosis is challenging because of several reasons, including error masking, fault reinforcement, and huge search space for possible fault combinations. This work proposes a two-phase method for multiple-fault diagnosis. The first phase efficiently reduces the potential number of fault candidates through machine learning. The second phase obtains the final diagnosis results, by formulating the task as an combinational optimization problem that is later iteratively solved using binary evolution computation. Experiments shows that our method outperforms two existing methods for multiple-fault diagnosis, and achieves better diagnosability (improved by $1.87\times$ ) and resolution (improved by $1.42\times$ ) compared with a state-of-the-art commercial diagnosis tool.

关键词： Fault diagnosis Adaptation models Computational modeling Fitting Filtering algorithms Boosting Computational efficiency

Detecting JVM JIT Compiler Bugs via Exploring Two-Dimensional Input Spaces

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Detecting JVM JIT Compiler Bugs via Exploring Two-Dimensiona...

International Conference on Software Engineering (ICSE)

作者： Haoxiang Jia Ming Wen Zifan Xie Xiaochen Guo Rongxin Wu Maolin Sun Kang Chen Hai Jin School of Cyber Science and Engineering Huazhong University of Science and Technology China Hubei Key Laboratory of Distributed System Security Services Computing Technology and System Lab Cluster and Grid Computing Lab. Hubei Engineering Research Center on Big Data Security National Engineering Research Center for Big Data Technology and System School of Informatics Xiamen University China School of Computer Science and Technology Huazhong University of Science and Technology China

Java Virtual Machine (JVM) is the fundamental software system that supports the interpretation and execution of Java bytecode. To support the surging performance demands for the increasingly complex and large-scale Java programs, Just-In-Time (JIT) compiler was proposed to perform sophisticated runtime optimization. However, this inevitably induces various bugs, which are becoming more pervasive over the decades and can often cause significant consequences. To facilitate the design of effective and efficient testing techniques to detect JIT compiler bugs. This study first performs a preliminary study aiming to understand the characteristics of JIT compiler bugs and the corresponding triggering test cases. Inspired by the empirical findings, we propose JOpFuzzer, a new JVM testing approach with a specific focus on JIT compiler bugs. The main novelty of JOpFuzzer is embodied in three aspects. First, besides generating new seeds, JOpFuzzer also searches for diverse configurations along the new dimension of optimization options. Second, JOpFuzzer learns the correlations between various code features and different optimization options to guide the process of seed mutation and option exploration. Third, it leverages the profile data, which can reveal the program execution information, to guide the fuzzing process. Such nov-elties enable JOpFuzzer to effectively and efficiently explore the two-dimensional input spaces. Extensive evaluation shows that JOpFuzzer outperforms the state-of-the-art approaches in terms of the achieved code coverages. More importantly, it has detected 41 bugs in OpenJDK, and 25 of them have already been confirmed or fixed by the corresponding developers.

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Reliability Analysis of Multi-Terminal VSC-HVDC Based on Semi-Markov Process

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Reliability Analysis of Multi-Terminal VSC-HVDC Based on Sem...

Power Electronics and Application Symposium (PEAS), IEEE International

作者： Kang Li Meng Huang Xiaoming Zha Hubei Key Laboratory of Power Equipment & System Security for Integrated Energy of School (of Wuhan University) School of Electrical Engineering and Automation (of Wuhan University) Wuhan China

Reliability analysis of VSC- HVDC is the premise of safe and stable operation of power system. Firstly, based on the analysis of MMC structure, an MMC reliability model considering subcomponent fault parameters was constructed; Secondly, based on the actual operation mode of the five terminal VSC- HVDC, a multi-state transfer model is established. Aiming at the problem that the distribution of state transfer time is limited in the traditional Markov process, a semi-Markov based reliability solution method for the five-terminal VSC-HVDC is established; Finally, taking Zhoushan five-terminal VSC-HVDC as an example, the reliability of MMC is analyzed, and the steady-state probability, average unconditional dwell time and other indicators of the system are solved, which can provide some data basis for the reliability analysis of VSC-HVDC.

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Poisoned LangChain: Jailbreak LLMs by LangChain

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arXiv 2024年

作者： Wang, Ziqiu Liu, Jun Zhang, Shengkai Yang, Yang Wuhan University of Technology Wuhan China Key Laboratory of Intelligent Sensing System and Security Ministry of Education School of Artificial Intelligence Hubei University Wuhan China

With the development of Natural Language Processing (NLP), Large Language Models (LLMs) are becoming increasingly popular. LLMs are integrating more into everyday life, raising public concerns about their security vulnerabilities. Consequently, the security of large language models is becoming critically important. Currently, the techniques for attacking and defending against LLMs are continuously evolving. One significant method type of attack is the jailbreak attack, which designed to evade model safety mechanisms and induce the generation of inappropriate content. Existing jailbreak attacks primarily rely on crafting inducement prompts for direct jailbreaks, which are less effective against large models with robust filtering and high comprehension abilities. Given the increasing demand for real-time capabilities in large language models, real-time updates and iterations of new knowledge have become essential. Retrieval-Augmented Generation (RAG), an advanced technique to compensate for the model’s lack of new knowledge, is gradually becoming mainstream. As RAG enables the model to utilize external knowledge bases, it provides a new avenue for jailbreak attacks. In this paper, we conduct the first work to propose the concept of indirect jailbreak and achieve Retrieval-Augmented Generation via LangChain. Building on this, we further design a novel method of indirect jailbreak attack, termed Poisoned-LangChain (PLC), which leverages a poisoned external knowledge base to interact with large language models, thereby causing the large models to generate malicious non-compliant *** tested this method on six different large language models across three major categories of jailbreak issues. The experiments demonstrate that PLC successfully implemented indirect jailbreak attacks under three different scenarios, achieving success rates of 88.56%, 79.04%, and 82.69% respectively. Experimental results and other resources: https://***/CAM-FSS/jailbreak-langchai

关键词： Knowledge based systems

Are we there yet? An Industrial Viewpoint on Provenance-based Endpoint Detection and Response Tools

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arXiv 2023年

作者： Dong, Feng Li, Shaofei Jiang, Peng Li, Ding Wang, Haoyu Huang, Liangyi Xiao, Xusheng Chen, Jiedong Luo, Xiapu Guo, Yao Chen, Xiangqun 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 China School of Computer Science Peking University China Arizona State University United States Sangfor Technologies Inc. China The Hong Kong Polytechnic University Hong Kong

Provenance-Based Endpoint Detection and Response (P-EDR) systems are deemed crucial for future APT defenses. Despite the fact that numerous new techniques to improve P-EDR systems have been proposed in academia, it is still unclear whether the industry will adopt P-EDR systems and what improvements the industry desires for P-EDR systems. To this end, we conduct the first set of systematic studies on the effectiveness and the limitations of P-EDR systems. Our study consists of four components: a one-to-one interview, an online questionnaire study, a survey of the relevant literature, and a systematic measurement study. Our research indicates that all industry experts consider P-EDR systems to be more effective than conventional Endpoint Detection and Response (EDR) systems. However, industry experts are concerned about the operating cost of P-EDR systems. In addition, our research reveals three significant gaps between academia and industry: (1) overlooking client-side overhead;(2) imbalanced alarm triage cost and interpretation cost;and (3) excessive server-side memory consumption. This paper's findings provide objective data on the effectiveness of P-EDR systems and how much improvements are needed to adopt P-EDR systems in industry. Copyright © 2023, The Authors. All rights reserved.

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NODLINK: An Online system for Fine-Grained APT Attack Detection and Investigation

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arXiv 2023年

作者： Li, Shaofei Dong, Feng Xiao, Xusheng Wang, Haoyu Shao, Fei Chen, Jiedong Guo, Yao Chen, Xiangqun Li, Ding School of Computer Science Peking University China Arizona State University United States Case Western Reserve University United States Sangfor Technologies Inc. China 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 China

Advanced Persistent Threats (APT) attacks have plagued modern enterprises, causing significant financial losses. To counter these attacks, researchers propose techniques that capture the complex and stealthy scenarios of APT attacks by using provenance graphs to model system entities and their dependencies. Particularly, to accelerate attack detection and reduce financial losses, online provenance-based detection systems that detect and investigate APT attacks under the constraints of timeliness and limited resources are in dire need. Unfortunately, existing online systems usually sacrifice detection granularity to reduce computational complexity and produce provenance graphs with more than 100,000 nodes, posing challenges for security admins to interpret the detection results. In this paper, we design and implement NODLINK, the first online detection system that maintains high detection accuracy without sacrificing detection granularity. Our insight is that the APT attack detection process in online provenance-based detection systems can be modeled as a Steiner Tree Problem (STP), which has efficient online approximation algorithms that recover concise attack-related provenance graphs with a theoretically bounded error. To utilize the frameworks of the STP approximation algorithm for APT attack detection, we propose a novel design of in-memory cache, an efficient attack screening method, and a new STP approximation algorithm that is more efficient than the conventional one in APT attack detection while maintaining the same complexity. We evaluate NODLINK in a production environment. The open-world experiment shows that NODLINK outperforms two state-of-the-art (SOTA) online provenance analysis systems by achieving magnitudes higher detection and investigation accuracy while having the same or higher throughput. Copyright © 2023, The Authors. All rights reserved.

关键词： Approximation algorithms

Study on Improvement of Receiving Grid DC transmission feeding location Selection Method Using Principal Component Analysis 7

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Study on Improvement of Receiving Grid DC transmission feedi...

7th IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2024

作者： Zhong, Qidi Ma, Yulong Wang, Shuai Shi, Lei Bai, Siyou Li, Zihan Peng, Xiaotao State Grid Economic and Technological Research Institute Beijing China State Grid Ningxia Electric Power Company Yichuan China Wuhan University Hubei Key Laboratory of Power Equipment & System Security for Integrated Energy School of Electrical Engineering and Automation Wuhan China Wuhan University School of Electrical Engineering and Automation Wuhan China

ISBN: (纸本)9798350339161

Multiple DC transmission feeding in receiving power grid brings huge risk to the stable operation of the power grid. It is of great significance to plan the DC feeding point economically and reasonably under the premise of ensuring the stable operation of the receiving end power grid. The article first calculates the multi-infeed interaction influence factor of the AC/DC receiving end system, divides the receiving end power grid into various regions, and then uses the principal component analysis method to calculate the characteristic root of the MIIF matrix, determines the corresponding characteristic vector with the largest characteristic root, and analyzes the nodes The voltage is disturbed by the voltage changes of other nodes, and the best DC feeding point in each region is selected to determine the optimal DC feeding point plan of the receiving end power grid. The calculation of the feeding point is selected through the traditional method and only the cluster analysis. The comparison of examples shows that the principal component analysis method can quickly and concisely determine the DC feeding point of the complex receiving end power grid. © 2024 IEEE.

关键词： Principal component analysis

DarkSAM: Fooling Segment Anything Model to Segment Nothing

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arXiv 2024年

作者： Zhou, Ziqi Song, Yufei Li, Minghui Hu, Shengshan Wang, Xianlong Zhang, Leo Yu Yao, Dezhong Jin, Hai National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Cluster and Grid Computing Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia

Segment Anything Model (SAM) has recently gained much attention for its outstanding generalization to unseen data and tasks. Despite its promising prospect, the vulnerabilities of SAM, especially to universal adversarial perturbation (UAP) have not been thoroughly investigated yet. In this paper, we propose DarkSAM, the first prompt-free universal attack framework against SAM, including a semantic decoupling-based spatial attack and a texture distortion-based frequency attack. We first divide the output of SAM into foreground and background. Then, we design a shadow target strategy to obtain the semantic blueprint of the image as the attack target. DarkSAM is dedicated to fooling SAM by extracting and destroying crucial object features from images in both spatial and frequency domains. In the spatial domain, we disrupt the semantics of both the foreground and background in the image to confuse SAM. In the frequency domain, we further enhance the attack effectiveness by distorting the high-frequency components (i.e., texture information) of the image. Consequently, with a single UAP, DarkSAM renders SAM incapable of segmenting objects across diverse images with varying prompts. Experimental results on four datasets for SAM and its two variant models demonstrate the powerful attack capability and transferability of DarkSAM. Our codes are available at: https://***/CGCL-codes/DarkSAM. Copyright © 2024, The Authors. All rights reserved.

关键词： Frequency domain analysis

Why Does Little Robustness Help? A Further Step Towards Understanding Adversarial Transferability

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Why Does Little Robustness Help? A Further Step Towards Unde...

IEEE Symposium on security and Privacy

作者： Yechao Zhang Shengshan Hu Leo Yu Zhang Junyu Shi Minghui Li Xiaogeng Liu Wei Wan Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security School of Cyber Science and Engineering Huazhong University of Science and Technology School of Information and Communication Technology Griffith University School of Software Engineering Huazhong University of Science and Technology Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology

ISBN: (数字)9798350331301

ISBN: (纸本)9798350331318

Adversarial examples for deep neural networks (DNNs) are transferable: examples that successfully fool one white-box surrogate model can also deceive other black-box models with different architectures. Although a bunch of empirical studies have provided guidance on generating highly transferable adversarial examples, many of these findings fail to be well explained and even lead to confusing or inconsistent advice for practical *** this paper, we take a further step towards understanding adversarial transferability, with a particular focus on surrogate aspects. Starting from the intriguing "little robustness" phenomenon, where models adversarially trained with mildly perturbed adversarial samples can serve as better surrogates for transfer attacks, we attribute it to a trade-off between two dominant factors: model smoothness and gradient similarity. Our research focuses on their joint effects on transferability, rather than demonstrating the separate relationships alone. Through a combination of theoretical and empirical analyses, we hypothesize that the data distribution shift induced by off-manifold samples in adversarial training is the reason that impairs gradient *** on these insights, we further explore the impacts of prevalent data augmentation and gradient regularization on transferability and analyze how the trade-off manifests in various training methods, thus building a comprehensive blueprint for the regulation mechanisms behind transferability. Finally, we provide a general route for constructing superior surrogates to boost transferability, which optimizes both model smoothness and gradient similarity simultaneously, e.g., the combination of input gradient regularization and sharpness-aware minimization (SAM), validated by extensive experiments. In summary, we call for attention to the united impacts of these two factors for launching effective transfer attacks, rather than optimizing one while ignoring the other, and emphasize the

关键词： Training Privacy Buildings Closed box Minimization Data augmentation Robustness