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CToMP: a cycle-task-oriented memory protection scheme for unmanned systems

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Science China(information Sciences) 2024年第6期67卷 399-417页

作者： Chengyan MA Ning XI Di LU Yebo FENG Jianfeng MA school of Cyber Engineering Xidian University school of computer Science and technology Xidian University Department of computer and information Science University of Oregon

Memory corruption attacks(MCAs) refer to malicious behaviors of system intruders that modify the contents of a memory location to disrupt the normal operation of computing systems, causing leakage of sensitive data or perturbations to ongoing processes. Unlike general-purpose systems, unmanned systems cannot deploy complete security protection schemes, due to their limitations in size, cost and performance.MCAs in unmanned systems are particularly difficult to defend against. Furthermore, MCAs have diverse and unpredictable attack interfaces in unmanned systems, severely impacting digital and physical sectors. In this paper, we first generalize, model and taxonomize MCAs found in unmanned systems currently, laying the foundation for designing a portable and general defense approach. According to different attack mechanisms,we found that MCAs are mainly categorized into two types — return2libc and return2shellcode. To tackle return2libc attacks, we model the erratic operation of unmanned systems with cycles and then propose a cycle-task-oriented memory protection(CToMP) approach to protect control flows from tampering. To defend against return2shellcode attacks, we introduce a secure process stack with a randomized memory address by leveraging the memory pool to prevent Shellcode from being executed. Moreover, we discuss the mechanism by which CTo MP resists the return-oriented programming(ROP) attack, a novel variant of return2libc attacks. Finally, we implement CTo MP on CUAV V5+ with Ardupilot and Crazyflie. The evaluation and security analysis results demonstrate that the proposed approach CTo MP is resilient to various MCAs in unmanned systems with low footprints and system overhead.

关键词： unmanned system memory corruption attack memory protection system security randomized memory address

A deep learning model enabled multi-event recognition for distributed optical fiber sensing

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Science China(information Sciences) 2024年第3期67卷 291-307页

作者： Yujiao LI Xiaomin CAO Wenhao NI Kuanglu YU Institute of information Science School of Computer and Information TechnologyBeijing Jiaotong University Beijing Key Laboratory of Advanced information Science and Network technology Beijing Jiaotong University

Fiber optic sensors that utilize backscattered light offer distributed real-time measurements and have been seen tremendous improvements in sensing distance and spatial resolution over the last decades.However, these improvements in sensor capabilities lead to a significant increase in the amount of data that needs to be processed. Traditional processing schemes are no longer adequate, so the development of novel signal processing methods is critical. Phase-sensitive optical time domain reflectometry(Φ-OTDR) is now applied in various applications for multi-event recognition, and it would usually be difficult, sometimes even unrealistic to label all the acquired samples due to its real-time and seamless monitoring nature. To fully take advantage of the information contained within the large number of unlabeled samples, which were formerly not utilized and hence wasted, we propose a semi-supervised model to boost the event classification performance of Φ-OTDR. The model extracts respectively the temporal features and the spatial bidirectional features together with a dual attention mechanism. Its classification accuracy has been improved up to 96.9%with only 1230 labeled samples. In addition, our model shows significant advantages when the number of labeled samples is reduced. Importantly, our method improves the accuracy of multi-event classification without any modification to the optical setup.

关键词： $\Phi$-OTDR event recognition semi-supervised learning mean teacher MT-ACNN-SA-BiLSTM

A Cover-Independent Deep Image Hiding Method Based on Domain Attention Mechanism

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computers, Materials & Continua 2024年第3期78卷 3001-3019页

作者： Nannan Wu Xianyi Chen James Msughter Adeke Junjie Zhao school of computer and Software Nanjing University of Information Science and TechnologyNanjing210044China school of Electronics and information Engineering Nanjing University of Information Science and TechnologyNanjing210044China

Recently,deep image-hiding techniques have attracted considerable attention in covert communication and high-capacity information hiding.However,these approaches have some limitations.For example,a cover image lacks self-adaptability,information leakage,or weak concealment.To address these issues,this study proposes a universal and adaptable image-hiding method.First,a domain attention mechanism is designed by combining the Atrous convolution,which makes better use of the relationship between the secret image domain and the cover image domain.Second,to improve perceived human similarity,perceptual loss is incorporated into the training process.The experimental results are promising,with the proposed method achieving an average pixel discrepancy(APD)of 1.83 and a peak signal-to-noise ratio(PSNR)value of 40.72 dB between the cover and stego images,indicative of its high-quality output.Furthermore,the structural similarity index measure(SSIM)reaches 0.985 while the learned perceptual image patch similarity(LPIPS)remarkably registers at 0.0001.Moreover,self-testing and cross-experiments demonstrate the model’s adaptability and generalization in unknown hidden spaces,making it suitable for diverse computer vision tasks.

关键词： Deep image hiding attention mechanism privacy protection data security visual quality

Modifying Lyapunov exponent of chaotic map by self-cascading

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Science China Technological Sciences 2024年第7期67卷 2203-2214页

作者： YI ChenLong LI ChunBiao LI YongXin XIA Ming HUA ZhongYun school of Electronic and information Engineering Nanjing University of Information Science & Technology school of Artificial Intelligence Nanjing University of Information Science & Technology school of computer Science and technology Harbin Institute of Technology Shenzhen Graduate School

The self-cascade(SC) method is an effective technique for chaos enhancement and complexity increasing in chaos maps.Additionally, the controllable self-cascade(CSC) method allows for more accurate control of Lyapunov exponents of the discrete map. In this work, the SC and CSC systems of the original map are derived, which enhance the chaotic performance while preserving the fundamental dynamical characteristics of the original map. Higher Lyapunov exponent of chaotic sequences corresponding to higher frequency are obtained in SC and CSC systems. Meanwhile, the Lyapunov exponent could be linearly controlled with greater flexibility in the CSC system. The verification of the numerical simulation and theoretical analysis is carried out based on the platform of CH32.

关键词： self-cascade controllable self-cascade chaos enhancement chaos control

QARF: A Novel Malicious Traffic Detection Approach via Online Active Learning for Evolving Traffic Streams

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Chinese Journal of Electronics 2024年第3期33卷 645-656页

作者： Zequn NIU Jingfeng XUE Yong WANG Tianwei LEI Weijie HAN Xianwei GAO school of computer Science and technology Beijing Institute of Technology school of Space information Space Engineering University

In practical abnormal traffic detection scenarios,traffic often appears as drift,imbalanced and rare labeled streams,and how to effectively identify malicious traffic in such complex situations has become a challenge for malicious traffic detection.Researchers have extensive studies on malicious traffic detection with single challenge,but the detection of complex traffic has not been widely noticed.Queried adaptive random forests(QARF) is proposed to detect traffic streams with concept drift,imbalance and lack of labeled instances.QARF is an online active learning based approach which combines adaptive random forests method and adaptive margin sampling strategy.QARF achieves querying a small number of instances from unlabeled traffic streams to obtain effective training.We conduct experiments using the NSL-KDD dataset to evaluate the performance of QARF.QARF is compared with other state-of-the-art methods.The experimental results show that QARF obtains 98.20% accuracy on the NSL-KDD dataset.QARF performs better than other state-of-the-art methods in comparisons.

关键词： Training Streams Random forests

Expression Complementary Disentanglement Network for Facial Expression Recognition

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Chinese Journal of Electronics 2024年第3期33卷 742-752页

作者： Shanmin WANG Hui SHUAI Lei ZHU Qingshan LIU College of computer Science and technology Nanjing University of Aeronautics and Astronautics College of computer and Software Nanjing University of Information Science and Technology school of information and Control Nanjing University of Information Science and Technology

Disentangling facial expressions from other disturbing facial attributes in face images is an essential topic for facial expression recognition.Previous methods only care about facial expression disentanglement(FED)itself,ignoring the negative effects of other facial attributes.Due to the annotations on limited facial attributes,it is difficult for existing FED solutions to disentangle all disturbance from the input face.To solve this issue,we propose an expression complementary disentanglement network(ECDNet).ECDNet proposes to finish the FED task during a face reconstruction process,so as to address all facial attributes during disentanglement.Different from traditional reconstruction models,ECDNet reconstructs face images by progressively generating and combining facial appearance and matching geometry.It designs the expression incentive(EIE) and expression inhibition(EIN) mechanisms,inducing the model to characterize the disentangled expression and complementary parts precisely.Facial geometry and appearance,generated in the reconstructed process,are dealt with to represent facial expressions and complementary parts,respectively.The combination of distinctive reconstruction model,EIE,and EIN mechanisms ensures the completeness and exactness of the FED task.Experimental results on RAF-DB,AffectNet,and CAER-S datasets have proven the effectiveness and superiority of ECDNet.

关键词： Geometry Image recognition Annotations Face recognition Task analysis Image reconstruction Facial features

YOLOv5ST:A Lightweight and Fast Scene Text Detector

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computers, Materials & Continua 2024年第4期79卷 909-926页

作者： Yiwei Liu Yingnan Zhao Yi Chen Zheng Hu Min Xia school of computer and Science Nanjing University of Information Science and TechnologyNanjing210044China school of Automation Nanjing University of Information Science and TechnologyNanjing210044China

Scene text detection is an important task in computer vision.In this paper,we present YOLOv5 Scene Text(YOLOv5ST),an optimized architecture based on YOLOv5 v6.0 tailored for fast scene text detection.Our primary goal is to enhance inference speed without sacrificing significant detection accuracy,thereby enabling robust performance on resource-constrained devices like drones,closed-circuit television cameras,and other embedded systems.To achieve this,we propose key modifications to the network architecture to lighten the original backbone and improve feature aggregation,including replacing standard convolution with depth-wise convolution,adopting the C2 sequence module in place of C3,employing Spatial Pyramid Pooling Global(SPPG)instead of Spatial Pyramid Pooling Fast(SPPF)and integrating Bi-directional Feature Pyramid Network(BiFPN)into the neck.Experimental results demonstrate a remarkable 26%improvement in inference speed compared to the baseline,with only marginal reductions of 1.6%and 4.2%in mean average precision(mAP)at the intersection over union(IoU)thresholds of 0.5 and 0.5:0.95,respectively.Our work represents a significant advancement in scene text detection,striking a balance between speed and accuracy,making it well-suited for performance-constrained environments.

关键词： Scene text detection YOLOv5 lightweight object detection

A Comprehensive Overview of CFN From a Commonsense Perspective

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Machine Intelligence Research 2024年第2期21卷 239-256页

作者： Ru Li Yunxiao Zhao Zhiqiang Wang Xuefeng Su Shaoru Guo Yong Guan Xiaoqi Han Hongyan Zhao Key Laboratory of Computational Intelligence and Chinese information Processing of Ministry of Education School of Computer and Information TechnologyShanxi UniversityTaiyuan 030006China

Chinese FrameNet(CFN)is a scenario commonsense knowledge base(CKB)that plays an important role in research on Chinese language understanding.It is based on the theory of frame semantics and English FrameNet(FN).The CFN knowledge base contains a wealth of scenario commonsense knowledge,including frames,frame elements,and frame relations,as well as annotated instances with rich scenario-related labels on Chinese sentences and discourses.In this paper,we conduct a comprehensive overview of CFN from a commonsense perspective,covering topics such as scenario commonsense representation,CFN resources,and its applications.We also summarize recent breakthroughs and identify future research directions.First,we introduce the concept of scenario commonsense,including its definitions,examples,and representation methods,with a focus on the relationship between scenario commonsense and the frame concept in CFN.In addition,we provide a comprehensive overview of CFN resources and their applications,highlighting the newly proposed frame-based discourse representation and a human-machine collaboration framework for expanding the CFN corpus.Furthermore,we explore emerging topics such as expanding the CFN resource,improving the interpretability of machine reading comprehension,and using scenario CKBs for text generation.

关键词： Chinese FrameNet(CFN) commonsense scenario commonsense frame knowledge

2P3FL:A Novel Approach for Privacy Preserving in Financial Sectors Using Flower Federated Learning

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computer Modeling in Engineering & Sciences 2024年第8期140卷 2035-2051页

作者： Sandeep Dasari Rajesh Kaluri school of computer Science Engineering and information Systems Vellore Institute of TechnologyVellore632014India

The increasing data pool in finance sectors forces machine learning(ML)to step into new complications.Banking data has significant financial implications and is confidential.Combining users data from several organizations for various banking services may result in various intrusions and privacy leakages.As a result,this study employs federated learning(FL)using a flower paradigm to preserve each organization’s privacy while collaborating to build a robust shared global model.However,diverse data distributions in the collaborative training process might result in inadequate model learning and a lack of privacy.To address this issue,the present paper proposes the imple-mentation of Federated Averaging(FedAvg)and Federated Proximal(FedProx)methods in the flower framework,which take advantage of the data locality while training and guaranteeing global convergence.Resultantly improves the privacy of the local models.This analysis used the credit card and Canadian Institute for Cybersecurity Intrusion Detection Evaluation(CICIDS)datasets.Precision,recall,and accuracy as performance indicators to show the efficacy of the proposed strategy using FedAvg and FedProx.The experimental findings suggest that the proposed approach helps to safely use banking data from diverse sources to enhance customer banking services by obtaining accuracy of 99.55%and 83.72%for FedAvg and 99.57%,and 84.63%for FedProx.

关键词： Federated learning FedAvg FedProx flower framework privacy preservation financial sectors