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RaCMC: Residual-Aware Compensation Network with Multi-Granularity Constraints for Fake News Detection 39

RaCMC: Residual-Aware Compensation Network with Multi-Granul...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Yu, Xinquan Sheng, Ziqi Lu, Wei Luo, Xiangyang Zhou, Jiantao School of Computer Science and Engineering Ministry of Education Key Laboratory of Information Technology Guangdong Province Key Laboratory of Information Security Technology Sun Yat-sen University Guangzhou510006 China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450002 China State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science Faculty of Science and Technology University of Macau 999078 China

ISBN: (纸本)157735897X

Multimodal fake news detection aims to automatically identify real or fake news, thereby mitigating the adverse effects caused by such misinformation. Although prevailing approaches have demonstrated their effectiveness, challenges persist in cross-modal feature fusion and refinement for classification. To address this, we present a residual-aware compensation network with multi-granularity constraints (RaCMC) for fake news detection, that aims to sufficiently interact and fuse cross-modal features while amplifying the differences between real and fake news. First, a multiscale residual-aware compensation module is designed to interact and fuse features at different scales, and ensure both the consistency and exclusivity of feature interaction, thus acquiring high-quality features. Second, a multi-granularity constraints module is implemented to limit the distribution of both the news overall and the image-text pairs within the news, thus amplifying the differences between real and fake news at the news and feature levels. Finally, a dominant feature fusion reasoning module is developed to comprehensively evaluate news authenticity from the perspectives of both consistency and inconsistency. Experiments on three public datasets, including Weibo17, Politifact and GossipCop, reveal the superiority of the proposed method. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Modal analysis

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Programmable logic controller memory management vulnerability analysis 3

Programmable logic controller memory management vulnerabilit...

引用

2022 3rd International Conference on Signal Processing and Computer Science, SPCS 2022

作者： Geng, Yangyang Ma, Rongkuan Wei, Qiang Wang, Wenhai State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou310000 China

Programmable logic controller (PLC) is omnipresent in industrial control system (ICS), which plays an imperative part in critical infrastructures. However, once a PLC is compromised by an attacker, it will create destructive effects on critical infrastructures. The memory systems in PLCs are considered significant targets for attackers to disrupt real-world physical processes. In this paper, we proposed five types of attacks on the PLC memory management mechanism. Furthermore, we evaluated the feasibility of our memory attack generation approach on six PLCs from three leading controller vendors. A total of 22 memory attack payloads are implemented with several vulnerabilities on the target PLCs. © Published under licence by IOP Publishing Ltd.

关键词： Programmable logic controllers

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An Intrusion Detection Feature Selection Method Based on Improved Mutual Information

An Intrusion Detection Feature Selection Method Based on Imp...

引用

IEEE Information Technology, Networking, Electronic and Automation Control Conference

作者： Zan Qi Jinlong Fei Jue Wang Xue Li State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

Feature selection methods for classification are crucial for intrusion detection techniques using machine learning. High-dimensional features in intrusion detection data affect computational complexity, consume more used resources and more time for data analysis, and the irrelevant and redundant features among them often hinder the performance of classifiers and mislead the classification task. Therefore, it is challenging to select more relevant features from intrusion detection data containing many such features. In this paper, we propose an efficient feature selection algorithm that first considers the correlation between features and the redundancy of pairs of features with respect to class labels based on an improved Pearson correlation coefficient, and later improves the evaluation function based on conditional mutual information to obtain a final subset of features with the goal of improving the classification rate and accuracy. The proposed feature selection method based on improved conditional mutual information is compared with three existing feature selection methods on the frequently studied public benchmark intrusion detection dataset NSL-KDD. The experimental results show that the features selected by the proposed method in this paper lead to a significant reduction in execution time while resulting in higher classification accuracy.

关键词： Correlation coefficient Correlation Redundancy Intrusion detection Machine learning Telecommunication traffic Feature extraction

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Research on Fuzzing Technology for JavaScript Engines 21

Research on Fuzzing Technology for JavaScript Engines

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5th International Conference on Computer Science and Application engineering, CSAE 2021

作者： Tian, Ye Qin, Xiaojun Gan, Shuitao State Key Laboratory of Mathematical Engineering and Advanced Computing Zheng zhou Wuxi China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China

ISBN: (纸本)9781450389853

JavaScript engine is the core component of web browsers, whose security issues are one of the critical aspects of the overall Web Eco-Security. Fuzzing technology, as an efficient software testing approach, has been widely applied to detecting vulnerabilities in different JavaScript engines, which is a security research hotspot at present. Based on systematical dissection of existing fuzzing methods, this paper reviews the development and technical ideas of JavaScript Engine Fuzzing combined with taxonomy, proposes a general framework of JavaScript Engine Fuzzing and analyzes the key techniques involved. Finally, we discuss the core issues that restrict efficiency in current research and present an outlook on the future trends of JavaScript Engine Fuzzing.. © 2021 Association for computing Machinery. All rights reserved.

关键词： Engines

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A Survey of Security Analysis on Industrial Control Devices via Rehosting

A Survey of Security Analysis on Industrial Control Devices ...

引用

Information Technology,Big Data and Artificial Intelligence (ICIBA), International Conference on IEEE International Conference on

作者： Houzhi Liu Rongkuan Ma Qiang Wei State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

At present, the network security of embedded devices has received more and more attention. However, the existing dynamic analysis tools can’t performance in embedded devices as common softwares. The main reason is that the internal state information of the embedded device can not be directly obtained, and the debugging interface usually shields the device after the manufacturer produces it. Thus, this paper introduces rehosting techniques for embedded systems and the differences between each way. Then we talk about the Deficiencies and future works in emulate execution.

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Research on Cross-site Scripting Attack Detection Technology Based on Few-shot Learning

Research on Cross-site Scripting Attack Detection Technology...

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IEEE Information Technology, Networking, Electronic and Automation Control Conference

作者： Dongzhe Lu Long Liu State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

With the intensification of informatization and mobility, various web security threats are emerging. Cross-site scripting (XSS) attack is the most common type of web attack. Most traditional detection methods have been difficult to adapt to the existing confusion variants of XSS attacks. In this paper, we extract features based on big data collected from 2017 to 2022. In order to improve the XSS detection effect of detection tools, we build machine learning models based on more than 210,000 positive and negative samples, among which CNN has the best performance. Furthermore, we propose a new algorithm that improves the traditional virtual sample generation technology based on prior knowledge in order to improve the generalization of the models. Experimental results show that in most cases, the performance of the algorithm in this paper is better than other VSG methods, and the ability to detect and discover unknown attacks is improved to a certain extent.

关键词： Machine learning algorithms Automation Cross-site scripting Perturbation methods Neural networks Machine learning Big Data

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Crop Enclave Interface for SGX Programs

Crop Enclave Interface for SGX Programs

引用

IEEE International Conference on Joint Cloud computing (JCC)

作者： Yaqi Fan Zhifeng Chen Qingbao Li Wenbo Deng State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

Existing memory attacks against SGX use the enclave interface, such as ECALLs and OCALLs, to inject malicious data into the enclave’s trusted memory to trigger memory corruption vulnerabilities therein. Therefore, enclave interface security becomes a key issue in defending against such attacks. However, a comprehensive static analysis of source SGX programs is currently lacking to obtain sufficient a priori knowledge to provide effective runtime interface protection for the enclave. In view of this, we identify 8 types of unsafe input data of enclave and design a new interface cropping method, SGXCrop. This method extracts critical interface information from source SGX programs, including ECALLs in use and unsafe input data, which are cropped at runtime of SGX programs. Tests in real SGX environment verify that the proposed method can effectively crop illegal ECALLs and unsafe input data.

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An Improved key Mismatch Attack on Kyber

An Improved Key Mismatch Attack on Kyber

引用

Cryptography, Security and Privacy (CSP), IEEE International Conference on

作者： Yaru Wang Haodong Jiang Zhi Ma State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

Recently, American National Institute of Standards and Technology (NIST) announced Kyber as the first KEM candidate to be standardized. The security of Kyber is based on the modular learning with errors (MLWE) problem, which achieves excellent efficiency and size. This work proposes an improved key mismatch on Kyber, which can reduce the number of queries required to recover the secret key. We first transform the problem of finding a certain parameter of ciphertexts into a quantum ordered search problem. Then we give the procedure of finding the value of a parameter in the ciphertexts by the quantum method. Finally, we instantiate this attack method on Kyber512, Kyber768 and Kyber1024. Compared with the existing attack algorithm, our improved attack reduces the number of queries for Kyber512, Kyber768 and Kyber1024 by 63%, 59% and 45%, respectively.

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Critical Variable Guided Mutation for Directed Greybox Fuzzing

Critical Variable Guided Mutation for Directed Greybox Fuzzi...

引用

Control, Electronics and Computer Technology (ICCECT), IEEE International Conference on

作者： Yubo He Long Liu The State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

Directed greybox fuzzing directs fuzzers to specified code areas and has gained great achievements in 1-day vulnerability detection. However, existing directed graybox fuzzers fail to generate the crash sample even if they found a testcase reaching the target site. There are mainly two questions that affect the effectiveness of directed greybox fuzzing: basic block-level target is coarse enough for 1-day vulnerability detection and the fuzzers follow a specific rule to select operators regardless of the vulnerability itself. This paper points out that only a few vulnerability-related variables are related to the vulnerability triggering. Based on the vulnerability-related variables, this paper proposes critical variable guided mutation, a mutation scheduling method to enhance the crash reproduction capability of directed greybox fuzzing. We implemented a prototype MDGF based on the critical variable guided mutation and evaluate it on real world programs. Evaluation of MDGF on various real-world programs showed that MDGF found vulnerabilities faster than the mainstream directed greybox fuzzers. The experimental results showed that the speed of MDGF is 6.18 times faster than that of AFLGo and 1.40 times faster than Beacon, and MDGF can find 1.71x more bugs than AFLGo.

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TS-GGNN: Combining Graph and Sequence Features for Vulnerability Detection in Source Code

TS-GGNN: Combining Graph and Sequence Features for Vulnerabi...

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International Conference on Communications, Information System and Computer engineering (CISCE)

作者： Xin Zhou Jianmin Pang Chunyan Zhang Feng Yue Junchao Wang Guangming Liu State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

Software vulnerability detection is crucial for maintaining the security and stability of software systems. In this paper, we propose a novel neural network model called TS-GGNN to address the problem of vulnerability detection in source code slices. The TS-GGNN model effectively captures both local and global features of vulnerable code by fusing sequence features with graph features. To achieve this, we utilize graph structure and sequence structure learning approaches to comprehensively extract valuable information from the source code slices. Our experiments are conducted on the SARD dataset, which consists of 61,638 code samples annotated for the presence or absence of vulnerabilities. The results demonstrate that TS-GGNN has the best vulnerability detection performance, with an accuracy of 99.4%, a precision of 98.81%, and an F1 score as high as 99.4% thereby validating the effectiveness of the TS-GGNN model in capturing features relevant to software vulnerabilities.

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