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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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Some Solutions for Writing Current Reduction and High Density Application of Phase Change Memory

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Some Solutions for Writing Current Reduction and High Densit...

2006 8th International Conference on Solid-State and Integrated Circuit Technology

作者： Yinyin Lin Jie Feng Yaifei Cai Hangbing Lv Feifei Liao Xin Liu Tingao Tang Baowei Qiao Yunfeng Lai Yi Zhang Bingchu Cai Bomy Chen Department of Microelectronics ASIC and System State Key Lab.Fudan University Research Institute of Micro/Nanometer Technology Shanghai Jiao Tong University Silicon Storage Technology Inc.1171 Sonora CourtSunnyvaleCA94086USA

＜正＞High writing current is the bottle-neck of PCM application and efforts focus on merely materials doping to improve crystal resistivity,novel structure to decrease active area *** some solutions of our group are ...

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Denial-of-Service or Fine-Grained Control: Towards Flexible Model Poisoning Attacks on Federated Learning

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arXiv 2023年

作者： Zhang, Hangtao Yao, Zeming Zhang, Leo Yu Hu, Shengshan Chen, Chao Liew, Alan Li, Zhetao School of Cyber Science and Engineering Huazhong University of Science and Technology China Swinburne University of Technology Australia Griffith University Australia National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab. Hubei Key Laboratory of Distributed System Security China Hubei Engineering Research Center on Big Data Security China RMIT University Australia Xiangtan University China

Federated learning (FL) is vulnerable to poisoning attacks, where adversaries corrupt the global aggregation results and cause denial-of-service (DoS). Unlike recent model poisoning attacks that optimize the amplitude of malicious perturbations along certain prescribed directions to cause DoS, we propose a Flexible Model Poisoning Attack (FMPA) that can achieve versatile attack goals. We consider a practical threat scenario where no extra knowledge about the FL system (e.g., aggregation rules or updates on benign devices) is availab.e to adversaries. FMPA exploits the global historical information to construct an estimator that predicts the next round of the global model as a benign reference. It then fine-tunes the reference model to obtain the desired poisoned model with low accuracy and small perturbations. Besides the goal of causing DoS, FMPA can be naturally extended to launch a fine-grained controllab.e attack, making it possible to precisely reduce the global accuracy. Armed with precise control, malicious FL service providers can gain advantages over their competitors without getting noticed, hence opening a new attack surface in FL other than DoS. Even for the purpose of DoS, experiments show that FMPA significantly decreases the global accuracy, outperforming six state-of-the-art attacks. © 2023, CC BY.

关键词： Federated learning

Securely Fine-tuning Pre-trained Encoders Against Adversarial Examples

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arXiv 2024年

作者： Zhou, Ziqi Li, Minghui Liu, Wei Hu, Shengshan Zhang, Yechao Wan, Wei Xue, Lulu 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 Computer Science and Technology Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia

With the evolution of self-supervised learning, the pre-training paradigm has emerged as a predominant solution within the deep learning landscape. Model providers furnish pre-trained encoders designed to function as versatile feature extractors, enabling downstream users to harness the benefits of expansive models with minimal effort through fine-tuning. Nevertheless, recent works have exposed a vulnerability in pre-trained encoders, highlighting their susceptibility to downstream-agnostic adversarial examples (DAEs) meticulously crafted by attackers. The lingering question pertains to the feasibility of fortifying the robustness of downstream models against DAEs, particularly in scenarios where the pre-trained encoders are publicly accessible to the attackers. In this paper, we initially delve into existing defensive mechanisms against adversarial examples within the pre-training paradigm. Our findings reveal that the failure of current defenses stems from the domain shift between pre-training data and downstream tasks, as well as the sensitivity of encoder parameters. In response to these challenges, we propose Genetic Evolution-Nurtured Adversarial Fine-tuning (Gen-AF), a two-stage adversarial fine-tuning approach aimed at enhancing the robustness of downstream models. Gen-AF employs a genetic-directed dual-track adversarial fine-tuning strategy in its first stage to effectively inherit the pre-trained encoder. This involves optimizing the pre-trained encoder and classifier separately while incorporating genetic regularization to preserve the model’s topology. In the second stage, Gen-AF assesses the robust sensitivity of each layer and creates a dictionary, based on which the top-k robust redundant layers are selected with the remaining layers held fixed. Upon this foundation, we conduct evolutionary adaptability fine-tuning to further enhance the model’s generalizability. Our extensive experiments, conducted across ten self-supervised training methods and six

关键词： Topology

New parameter-free mobility model: Opportunity priority selection model

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arXiv 2018年

作者： Liu, Erjian Yan, Xiao-Yong Key Lab. of Integrated Transportation Big Data Application Technology for Transportation Industry Beijing Jiaotong University Beijing100044 China Institute of Transportation System Science and Engineering Beijing Jiaotong University Beijing100044 China Big Data Research Center University of Electronic Science and Technology of China Chengdu611731 China

Predicting human mobility patterns has many practical applications in urban planning, traffic engineering, infectious disease epidemiology, emergency management and location-based services. Developing a universal model capable of accurately predicting the mobility fluxes between locations is a fundamental and challenging problem in regional economics and transportation science. Here, we propose a new parameter-free model named opportunity priority selection model as an alternative in human mobility prediction. The basic assumption of the model is that an individual will select destination locations that present higher opportunity benefits than the location opportunities of the origin and the intervening opportunities between the origin and destination. We use real mobility data collected from a number of cities and countries to demonstrate the predictive ability of this simple model. The results show that the new model offers universal predictions of intracity and intercity mobility patterns that are consistent with real observations, thus suggesting that the proposed model better captures the mechanism underlying human mobility than previous models. Copyright © 2018, The Authors. All rights reserved.

关键词： Forecasting

Two-Stage OD Flow Prediction for Emergency in Urban Rail Transit

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IEEE Transactions on Intelligent Transportation systems 2024年第1期25卷 920-928页

作者： Zhu, Guangyu Ding, Jiacun Wei, Yun Yi, Yang Xu, Sendren Sheng-Dong Wu, Edmond Q. Beijing Jiaotong University Key Lab. of Transport Industry of Big Data Application Technologies for Comprehensive Transport The Beijing Research Center of Urban Traffic Information Sensing and Service Technologies Beijing100044 China Beijing Mass Transit Railway Operation Corporation Ltd. Beijing100014 China Yangzhou University College of Information Engineering Yangzhou225127 China National Taiwan University of Science and Technology Automation and Control Center The Graduate Institute of Automation and Control Taipei106335 Taiwan Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China The Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai200240 China

Urban rail transit (URT) is vulnerable to natural disasters and social emergencies including fire, storm and epidemic (such as COVID-19), and real-time origin-destination (OD) flow prediction provides URT operators with important information to ensure the safety of URT system. However, hindered by the high dimensionality of OD flow and the lack of supportive information reflecting the real-time passenger flow changes, study in this area is at the beginning stage. A novel model consisting of two stages is proposed for OD flow prediction. The first stage predicts the inflows of all stations by Long Short-Term Memory (LSTM) in real time, where the dimension is reduced compared with predicting OD flows directly. In the second stage, the notion of separation rate, namely, the proportion of inbound passengers bounding for another station, is estimated. Finally, The OD flow is predicted by multiplying the inflow and separation rate. Experiments based on Hangzhou Metro dataset show the proposed model outperforms the contrast model in weighted mean average error (WMAE) and weighted mean square error (WMSE). Results also suggest that the proposed prediction model performs better on weekdays than on weekends, and with greater accuracy on larger OD flows. © 2000-2011 IEEE.

关键词： Forecasting

JSRNN: Joint Sampling and Reconstruction Neural Networks for High Quality Image Compressed Sensing

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arXiv 2022年

作者： Zeng, Chunyan Ye, Jiaxiang Wang, Zhifeng Zhao, Nan Wu, Minghu Hubei Key Lab. for High-efficiency Utiliz. of Solar Ener. and Oper. Control of Energy Storage System Hubei University of Technology Wuhan430068 China School of Educational Information Technology Hubei Research Center for Educational Informationization Central China Normal University Wuhan430079 China

Most Deep Learning (DL) based Compressed Sensing (DCS) algorithms adopt a single neural network for signal reconstruction, and fail to jointly consider the influences of the sampling operation for reconstruction. In this paper, we propose unified framework, which jointly considers the sampling and reconstruction process for image compressive sensing based on welldesigned cascade neural networks. Two sub-networks, which are the sampling sub-network and the reconstruction sub-network, are included in the proposed framework. In the sampling sub-network, an adaptive full connected layer instead of the traditional random matrix is used to mimic the sampling operator. In the reconstruction sub-network, a cascade network combining stacked denoising autoencoder (SDA) and convolutional neural network (CNN) is designed to reconstruct signals. The SDA is used to solve the signal mapping problem and the signals are initially reconstructed. Furthermore, CNN is used to fully recover the structure and texture features of the image to obtain better reconstruction performance. Extensive experiments show that this framework outperforms many other state-ofthe-art methods, especially at low sampling rates. Copyright © 2022, The Authors. All rights reserved.

关键词： Compressed sensing

WISERNet: Wider Separate-then-reunion Network for Steganalysis of Color Images

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arXiv 2018年

作者： Zeng, Jishen Tan, Shunquan Liu, Guangqing Li, Bin Huang, Jiwu Guangdong Key Laboratory of Intelligent Information Processing National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China College of Computer Science and Software Engineering Shenzhen University Guangdong Key Lab. of Intelligent Info. Processing and Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen518060 China Peng Cheng Laboratory Shenzhen518052 China

Until recently, deep steganalyzers in spatial domain have been all designed for gray-scale images. In this paper, we propose WISERNet (the wider separate-then-reunion network) for steganalysis of color images. We provide theoretical rationale to claim that the summation in normal convolution is one sort of "linear collusion attack" which reserves strong correlated patterns while impairs uncorrelated noises. Therefore in the bottom convolutional layer which aims at suppressing correlated image contents, we adopt separate channel-wise convolution without summation instead. Conversely, in the upper convolutional layers we believe that the summation in normal convolution is beneficial. Therefore we adopt united normal convolution in those layers and make them remarkably wider to reinforce the effect of "linear collusion attack". As a result, our proposed wide-and-shallow, separate-then-reunion network structure is specifically suitable for color image steganalysis. We have conducted extensive experiments on color image datasets generated from BOSSBase raw images and another large-scale dataset which contains 100,000 raw images, with di_erent demosaicking algorithms and downsampling algorithms. The experimental results show that our proposed network outperforms other state-of-the-art color image steganalytic models either hand-crafted or learned using deep networks in the literature by a clear margin. Specifically, it is noted that the detection performance gain is achieved with less than half the complexity compared to the most advanced deeplearning steganalyzer as far as we know, which is scarce in the literature. Copyright © 2018, The Authors. All rights reserved.

关键词： Steganography

Characteristics of Si-Sb-Te Films for Phase Change Memory

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MRS Online Proceedings Library (OPL) 2011年第1期918卷 0918-H08-04-0918-H08-04页

作者： Jie Feng Yin ZHANG Baowei Qiao Yanfei Cai Yinyin Lin Tingao Tang Bingchu Cai Bomy Chen jfeng@*** Shanghai Jiao Tong University Key lab. for thin film and microfabrication of Ministry of Education Inst. of Micro/Nano Sci.& Tech 1954 Huasan Road Shanghai N/A 200030 China People's Republic of 86-21-62823631 zy13225@*** Shanghai Jiao Tong University Key lab. for thin film and microfabrication of Ministry of Education Inst. of Micro/Nano Sci.& Tech 1954 Huasan Road Shanghai N/A 200030 China People's Republic of qbwhappy@*** Shanghai Jiao Tong University Key lab. for thin film and microfabrication of Ministry of Education Inst. of Micro/Nano Sci.& Tech 1954 Huasan Road Shanghai N/A 200030 China People's Republic of yylin@*** Fudan University State Key Laboratory of ASIC £¦ System Shanghai N/A 200433 China People's Republic of tatang@*** Fudan University State Key Laboratory of ASIC £¦ System Shanghai N/A 200433 China People's Republic of bccai@*** Shanghai Jiao Tong University Key lab. for thin film and microfabrication of Ministry of Education Inst. of Micro/Nano Sci.& Tech 1954 Huasan Road Shanghai N/A 200030 China People's Republic of bchen@*** Silicon Storage Technology Inc. 1171 Sonora Court Sunnyvale CA 94086 United States

The novel phase change materials Si-Sb-Te films were prepared. The crystallization temperature of films increases with the increasing of Si concentration. Phase separation was observed in the Si-Sb-Te films, the dominant phase is Sb2Te3. The melting temperature of Si-Sb-Te decreased to ~550°C lower than 640°C of Ge2Sb2Te5. The decrease of film thickness of Si-Sb-Te films is less than 2% after annealing at 400°C, which is less than ~7% of the film thickness change of Ge2Sb2Te5 film. The crystalline resistivity of Si-Sb-Te films increased and the ratio of amorphous/crystalline resistivity of Si-Sb-Te films increased also comparing with Ge2Sb2Te5 film, which is benefit to reduce the writing current and keep higher on/off ratio of phase change memory. Reversible switch was performed in the devices with Si-Sb-Te films. The device with Si14.3Sb28.6Te57.2 film can be programmed with a 100 ns SET pulse and a 20 ns RESET pulse. The Reset current is only 1.37mA for a 10μm-sized device.

关键词： phase transformation film devices