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Block-gram:Mining knowledgeable features for efficiently smart contract vulnerability detection

Digital Communications and Networks 2025年第1期11卷 1-12页

作者： Xueshuo Xie Haolong Wang Zhaolong Jian Yaozheng Fang Zichun Wang Tao Li Tianjin Key Laboratory of Network and Data Security Technology TianjinChina College of Computer Science Nankai UniversityTianjinChina Key Laboratory of Blockchain and Cyberspace Governance of Zhejiang Province China State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of SciencesChina

Smart contracts are widely used on the blockchain to implement complex transactions,such as decentralized applications on *** vulnerability detection of large-scale smart contracts is critical,as attacks on smart contracts often cause huge economic *** it is difficult to repair and update smart contracts,it is necessary to find the vulnerabilities before they are ***,code analysis,which requires traversal paths,and learning methods,which require many features to be trained,are too time-consuming to detect large-scale on-chain ***-based methods will obtain detection models from a feature space compared to code analysis methods such as symbol *** the existing features lack the interpretability of the detection results and training model,even worse,the large-scale feature space also affects the efficiency of *** paper focuses on improving the detection efficiency by reducing the dimension of the features,combined with expert *** this paper,a feature extraction model Block-gram is proposed to form low-dimensional knowledge-based features from ***,the metadata is separated and the runtime code is converted into a sequence of opcodes,which are divided into segments based on some instructions(jumps,etc.).Then,scalable Block-gram features,including 4-dimensional block features and 8-dimensional attribute features,are mined for the learning-based model ***,feature contributions are calculated from SHAP values to measure the relationship between our features and the results of the detection *** addition,six types of vulnerability labels are made on a dataset containing 33,885 contracts,and these knowledge-based features are evaluated using seven state-of-the-art learning algorithms,which show that the average detection latency speeds up 25×to 650×,compared with the features extracted by N-gram,and also can enhance the interpretability of the detection model.

关键词： Smart contract Bytecode&opcode Knowledgeable features Vulnerability detection Feature contribution

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key-based data augmentation with curriculum learning for few-shot code search

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Neural Computing and Applications 2025年第3期37卷 1475-1490页

作者： Zhang, Fan Peng, Manman Wu, Qiang Shen, Yuanyuan College of Computer Science and Electronic Engineering Hunan University Lushan South Road Hunan Province Changsha410082 China Hunan Provincial Key Laboratory of Blockchain Infrastructure and Application Hunan University Lushan South Road Hunan Province Changsha410082 China

Given a natural language query, code search aims to find matching code snippets from a codebase. Recent works are mainly designed for mainstream programming languages with large amounts of training data. However, code search is also needed for domain-specific programming languages, which have fewer training data, and it is a heavy burden to label a large amount of training data for each domain-specific language. To this end, we propose DAFCS, a data augmentation framework with curriculum learning for few-shot code search tasks. Specifically, we first collect unlabeled codes in the same programming language as the original codes, which can provide additional semantic signals to the original codes. Second, we employ an occlusion-based method to identify key statements in code fragments. Third, we design a set of new key-based augmentation operations for the original codes. Finally, we use curriculum learning to reasonably schedule augmented samples for training well-performing models. We conduct retrieval experiments on a public dataset and find that DAFCS surpasses state-of-the-art methods by 5.42% and 5.05% in the Solidity and SQL domain-specific languages, respectively. Our study shows that DAFCS, which adopts data augmentation and curriculum learning strategies, can achieve promising performance in few-shot code search tasks. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Code search Curriculum learning data augmentation Few shot learning

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Defending data Inference Attacks Against Machine Learning Models by Mitigating Prediction Distinguishability

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IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 2687-2704页

作者： Yang, Ziqi Zhu, Yiran Wan, Jie Xiang, ChuXiao Tang, Tong Wang, Yilin Xu, Ruite Wang, Lijin Zhang, Fan Xu, Jiarong Qin, Zhan Zhejiang University State Key Laboratory of Blockchain and Data Security Hangzhou310027 China Institute of Blockchain and Data Security Hangzhou310027 China Zhejiang University College of Computer Science and Technology Hangzhou310027 China Fudan University Department of Information Management and Business Intelligence Shanghai200437 China

Neural networks are vulnerable to data inference attacks, including the membership inference attack, the model inversion attack, and the attribute inference attack. In this paper, we propose PURIFIER to defend against membership inference attacks by quantifying the differences between dataset members and non-members in three dimensions: individual shape, statistical distribution, and prediction label. PURIFIER involves transforming the confidence scores produced by the target classifier, resulting in purified confidence scores that are indistinguishable across the dimensions above. We conduct experiments on widely-used datasets and models. The results show that Purifier offers robust defense against membership inference attacks with superior efficacy compared to prior defense techniques while maintaining minimal utility degradation (e.g., less than 0.7% classification accuracy drop of most datasets). Additionally, our extended experiments explore the effectiveness of PURIFIER in defending against the model inversion attack and the attribute inference attack. © 2004-2012 IEEE.

关键词： Adversarial machine learning

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Face Forgery Detection Based on Fine-Grained Clues and Noise Inconsistency

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第1期6卷 144-158页

作者： Zhang, Dengyong He, Ruiyi Liao, Xin Li, Feng Chen, Jiaxin Yang, Gaobo Changsha University of Science and Technology Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation the School of Computer and Communication Engineering Changsha410114 China Hunan University Changsha410082 China

Deepfake detection has gained increasing research attention in media forensics, and a variety of works have been produced. However, subtle artifacts might be eliminated by compression, and the convolutional neural networks (CNNs)-based detectors are invalidated for fake face images with compression. In this work, we propose a two-stream network for deepfake detection. We observed that high-frequency noise features and spatial features are inherently complementary to each other. Thus, both spatial features and high-frequency noise features are exploited for face forgery detection. Specifically, we design a double-frequency transformer module (DFTM) to guide the learning of spatial features from local artifact regions. To effectively fuse spatial features and high-frequency noise features, a dual-domain attention fusion module (DDAFM) is designed. We also introduce a local relationship constraint loss, which requires only image-level labels, for model training. We evaluate the proposed approach on five large-scale benchmark datasets, and extensive experimental results demonstrate the proposed approach outperforms most SOTA works. © 2024 IEEE.

关键词： Frequency domain analysis

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Threshold Huffman-Based Covert Timing Channel Detection

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International Journal of Network Security 2025年第1期27卷 61-71页

作者： Lin, Yuwei Chen, Yonghong Zhuang, Xiaolong Huang, Xuwen School of Computer Science and Technology Huaqiao University Fujian Xiamen361000 China Xiamen Key Laboratory of Data Security and Blockchain Technology Huaqiao University Fujian Xiamen361000 China

Network covert timing channels in computer networks evade effective detection by traditional security mechanisms such as firewalls, posing a significant threat to network security. However, most existing methods struggle to detect such channels when the window size is small. Additionally, the emergence of the new ϵ-κlibur channel has resulted in a decline in performance for detection methods such as GAS, SnapCatch, and DNN when detecting this particular channel. In this paper, we investigate a Huffman tree-based approach for detecting anomalous nodes in traffic and achieving the classification of channels through the design of weight intervals. Our method achieves an average detection rate of over 95% for each channel. Additionally, compared to GAS, SnapCatch, and DNN, our method improves the detection rates for the ϵ-κlibur channel by 11.75%, 8.46%, and 4.81%, respectively. The experimental results demonstrate that our proposed approach successfully detects ϵ-κlibur and achieves detection in a small window. © (2025), (Femto Technique Co., Ltd.). All rights reserved.

关键词： Computer system firewalls

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Controlled-source electromagnetic noise attenuation via a deep convolutional neural network and high-quality sounding curve screening mechanism

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Geophysics 2025年第3期90卷 WA125-WA140页

作者： Liu, Yecheng Li, Diquan Li, Jin Zhang, Xian Central South University Monitoring Ministry of Education Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Changsha China Hunan Provincial Key Laboratory of Non-ferrous Resources and Geological Hazard Detection Changsha China Central South University School of Geoscience and Info-physics Changsha China Hunan Normal University College of Information Science and Engineering Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Changsha China Hunan University of Finance and Economics School of Information Technology and Management Hunan Provincial Key Laboratory of Finance & Economics Big Data Science and Technology Changsha China

Strong noise is one of the biggest challenges in controlled-source electromagnetic (CSEM) exploration, which severely affects the quality of the recorded signal. We develop a novel and effective CSEM noise attenuation method that integrates a deep convolutional neural network (DCNN) with a high-quality sounding curve screening mechanism. This method first uses a DCNN to learn the large-scale noise characteristics of CSEM measured data and build a DCNN denoising model for attenuating the noise. Because some impulse noise remains after DCNN denoising, the high-quality sounding curve screening mechanism is innovatively applied to optimize the DCNN denoised data. The core of this method is the smoothness value (SV), which quantitatively evaluates the smoothness of the sounding curve (apparent resistivity curve). In effect, the mechanism segments the overall DCNN denoised data and calculates the corresponding sounding curve and its SV for each segment. The highest-quality sounding curve can be screened out by comparing the SVs of all curves as well as obtaining the highest-quality data segment present in the overall DCNN denoised data. Finally, our method is validated with synthetic and field data, demonstrating its feasibility and effectiveness in attenuating large-scale CSEM noise. The quality of CSEM data, which is heavily affected by noise, is significantly improved after processing by our method. © 2025 Society of Exploration Geophysicists. All rights reserved.

关键词： Deep neural networks

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GoPIM: GCN-Oriented Pipeline Optimization for PIM Accelerators 31

GoPIM: GCN-Oriented Pipeline Optimization for PIM Accelerato...

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31st IEEE International Symposium on High Performance Computer Architecture, HPCA 2025

作者： Yang, Siling He, Shuibing Wang, Wenjiong Yin, Yanlong Wu, Tong Chen, Weijian Zhang, Xuechen Sun, Xian-He Feng, Dan The State Key Laboratory of Blockchain and Data Security Zhejiang University China Zhejiang Lab China Institute of Blockchain and Data Security China Zhejiang Key Laboratory of Big Data Intelligent Computing China Washington State University Vancouver United States Illinois Institute of Technology United States Huazhong University of Science and Technology China Wuhan National Laboratory for Optoelectronics China

ISBN: (纸本)9798331506476

Graph convolutional networks (GCNs) are popular for a variety of graph learning tasks. ReRAM-based processing-in-memory (PIM) accelerators are promising to expedite GCN training owing to their in-situ computing capability. However, existing accelerators can be severely underutilized even with pipelines, due to the oversight of the skewed execution times of various GCN stages and the ignorance of skewed degrees of graph vertices. In this work, we propose GOPIM, a GCN-oriented pipeline optimization for PIM accelerators to expedite GCN training. First, GOPIM proposes an ML-based scheme that allocates crossbar resources to the most needed stages to streamline the overall pipeline. Second, GOPIM utilizes a selective vertex updating technique that evenly distributes vertices on crossbars by interleaved mapping. These techniques collectively reduce the overall execution time without losing much accuracy. We also provide a practical architecture design for GOPIM. Our experimental results show that, GoPIM achieves up to 191 × speedup and 16.1 × energy saving, compared to the state-of-the-art work. © 2025 IEEE.

关键词： Graph neural networks

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TCMS: A Multi-Sequence Log Parsing Method Based on Token Conversion

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IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 3028-3045页

作者： Wei, Mingkuan Wen, Jigang He, Shiming Xie, Kun Liang, Wei Xie, Gaogang Li, Kenli Zhu, Ziyu Hunan University Ministry of Education Key Laboratory of "Fusion Computing of Supercomputing and Artificial Intelligence" College of Computer Science and Electronics Engineering Changsha410012 China Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan411201 China Changsha University of Science and Technology School of Computer and Communication Engineering Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha410114 China Beijing100045 China Columbia University Electrical Engineering New YorkNY10027 United States

Detailed system operations are recorded in logs. To ensure system reliability, developers can detect system anomalies through log anomaly detection. Log parsing, which converts semi-structured log messages into structured data, is a crucial step in log anomaly detection and advanced program analysis and verification. Despite the availability of various log parsing tools, they generally suffer from low parsing accuracy and slow efficiency due to the ignorance of variable characteristics and the use of costly pairwise comparison methods. In this paper, we propose a TCMS framework to parse logs, consisting of two main technologies. First, by studying 16 public log datasets, we find that most log variable tokens are structured variable tokens. Based on this discovery, we propose a token conversion algorithm to improve parsing accuracy. This algorithm converts the changed parts in structured variable tokens into wildcards (‘’), preventing these tokens from being directly identified as constant tokens. Second, to improve efficiency, we propose the LogMLCS algorithm, which intelligently constructs a graph to facilitate the extraction of common parts from multiple log messages at once, instead of using pairwise comparisons. Comprehensive experiments conducted on 16 log datasets reveal that our TCMS outperforms seven other parsing methods, achieving the highest parsing accuracy at the fastest speed. Furthermore, experimental results from running a log anomaly detection algorithm in conjunction with different log parsing methods demonstrate that TCMS significantly boosts detection accuracy. For instance, on the OpenStack dataset, our TCMS-facilitated log anomaly detection algorithm achieves a perfect F1-score, precision, and recall of 100% each, surpassing the best peer method by 32.2, 0.8, and 19.5 percentage points, respectively. © 2004-2012 IEEE.

关键词： Accuracy Anomaly Detection Clustering Algorithms Peer To Peer Computing Electronic Mail Source Coding Particle Separators Codes Tokenization Itemsets Log Parsing Log Analysis Token Conversion Log MLCS Log Parsing Pairwise Comparisons Low Accuracy Public datasets Anomaly Detection Common Part Multiple Messages Long Short Term Memory Point Source Target Domain Logarithm Of The Number Captive Animals Multi Objective Optimization Problem Regular Expressions Biological Sequences Rule Based Methods Point In The Graph Kinds Of datasets Template Generation Number Of Templates Similar Template Parse Tree Syntax Errors Harmonic Average Of Precision Log Length Earliest Methods Common Subsequence Precision And Recall Leaf Node Part Of The Message

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MagShadow: Physical Adversarial Example Attacks via Electromagnetic Injection

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Liu, Ziwei Lin, Feng Ba, Zhongjie Lu, Li Ren, Kui Zhejiang University State Key Laboratory of Blockchain and Data Security School of Cyber Science and Technology China Institute of Blockchain and Data Security Hangzhou China

Physical adversarial examples (AEs) have become an increasing threat to deploying deep neural network (DNN) models in the real world. Popular approaches adopt sticking-based or projecting-based strategies that stick the printed adversarial patches to objects or directly project the AE onto objects. Although effective, these methods require access to target objects and generate visible artifacts, which reduces the attack's stealthiness. In this paper, we propose MagShadow, a new attack vector that leverages imperceptible electromagnetic (EM) signals to realize physical AEs. MagShadow utilizes the CCD camera sensor's susceptibility to EM injection attacks and induces fine-grained adversarial perturbations on the camera's captured image by injecting carefully-crafted signals with a low-cost portable device. As MagShadow directly manipulates the image sensor's output with EM signals, the attack requires no access to the target object and can keep stealthy. We study the feasibility of MagShadow in two typical DNN application scenarios (image classification and object detection) and design a framework to implement four different types of attacks, i.e., untargeted, targeted, hiding, and appearing attacks. Extensive real-world experiments on five different cameras are conducted, which demonstrate MagShadow's effectiveness against different popular DNN models (Inception v3, ResNet101, YOLO v3/v4). © 2004-2012 IEEE.

关键词： CCD cameras

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How Do Recommendation Models Amplify Popularity Bias? An Analysis from the Spectral Perspective 25

How Do Recommendation Models Amplify Popularity Bias? An Ana...

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18th ACM International Conference on Web Search and data Mining, WSDM 2025

作者： Lin, Siyi Gao, Chongming Chen, Jiawei Zhou, Sheng Hu, Binbin Feng, Yan Chen, Chun Wang, Can Zhejiang University Hangzhou China University of Science and Technology of China Hefei China Ant Group Hangzhou China State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China Institute of Blockchain and Data Security Zhejiang University Hangzhou China

ISBN: (纸本)9798400713293

Recommendation Systems (RS) are often plagued by popularity bias. When training a recommendation model on a typically long-tailed dataset, the model tends to not only inherit this bias but often exacerbate it, resulting in over-representation of popular items in the recommendation lists. This study conducts comprehensive empirical and theoretical analyses to expose the root causes of this phenomenon, yielding two core insights: 1) Item popularity is memorized in the principal spectrum of the score matrix predicted by the recommendation model;2) The dimension reduction phenomenon amplifies the relative prominence of the principal spectrum, thereby intensifying the popularity bias. Building on these insights, we propose a novel debiasing strategy that leverages a spectral norm regularizer to penalize the magnitude of the principal singular value. We have developed an efficient algorithm to expedite the calculation of the spectral norm by exploiting the spectral property of the score matrix. Extensive experiments across seven real-world datasets and three testing paradigms have been conducted to validate the superiority of the proposed method. © 2025 Copyright held by the owner/author(s).

关键词： popularity bias

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