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Crop Enclave Interface for SGX Programs

Crop Enclave Interface for SGX Programs

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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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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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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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Feature-based identification method of MCU firmware IO interface

Feature-based identification method of MCU firmware IO inter...

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IEEE Information Technology, Networking, Electronic and Automation Control Conference

作者： Yuanbo Sun Weiyu Dong State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

Device simulation is an important method of embedded device security analysis, due to the extensive and heterogeneous nature of the current peripherals, the existing simulation technology for peripheral simulation is mostly fuzzy, to find the input and output that meet the firmware requirements as the main goal. In order to construct a template based on IO interface identification to extend the peripheral simulation scheme, this paper identifies the IO interface without firmware source code based on the characteristics of the IO configuration process in MCU firmware. Through experimental comparison, this method has a certain effect in MCU firmware interface recognition.

关键词： Analytical models Automation Source coding Registers Character recognition Object recognition Security

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ALEmu: A Framework for Application-layer Programs Emulation of Embedded Devices

ALEmu: A Framework for Application-layer Programs Emulation ...

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International Conference on Computer engineering and Applications (ICCEA)

作者： Heng He Xiaobing Xiong Yuntian Zhao State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

The difference between real devices and virtual environments causes a low success rate of application-layer program emulation when the firmware is operating in full-system emulation during the dynamic analysis of the firmware of embedded devices. In this paper, we propose ALEmu, an emulation framework for application-layer programs, which can effectively improve the emulation success rate of application-layer programs in embedded device firmware through automatic preprocessing of target programs, building configuration libraries, and hooking external program calls. When we test ALEmu on a variety of real-world devices, including routers and IP cameras, we find that it performs more successfully and accurately than the current state-of-the-art full-system emulation frameworks like Firmadyne and FirmAE.

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Vulnerability model and attack path prediction of the UEFI firmware platform based on risk propagation 11

Vulnerability model and attack path prediction of the UEFI f...

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2021 11th International Workshop on Computer Science and engineering, WCSE 2021

作者： Jiao, Weihua Li, Qingbao Chen, Zhifeng Cao, Fei State Key Laboratory of Mathematical Engineering and Advanced Computing China

ISBN: (纸本)9789811817915

Targeted at the situation of rampant attack on UEFI Platform Firmware, this paper systematically analyzes the Security mechanisms of UEFI platform firmware. Then the vulnerability factors of UEFI firmware are described by modeling language, and a vulnerability model of UEFI firmware platform based on risk propagation (VMURP) is proposed. This paper introduces an improved PageRank algorithm to this model to reduce the influence of subjective factors which influences the accuracy of model. Based on VMURP model, an innovative method is proposed which using security configuration vector and attack vector to evaluate attack paths. Then, we use this method and VMURP to predict the most possible attack path of specific UEFI firmware platform. Finally, verify the rationality of the model and the validity of the prediction by experimental analysis. This study is helpful to quickly evaluate the vulnerability of UEFI firmware platform and predict possible attacks, gives platform managers more targeted guidance and suggestions to strengthen the security mechanisms. © 2021 11th International Workshop on Computer Science and engineering, WCSE 2021. All Rights Reserved.

关键词： Forecasting

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A Survey on Knowledge Graph Representation Learning with Text Information 3

A Survey on Knowledge Graph Representation Learning with Tex...

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3rd World Symposium on Software engineering, WSSE 2021

作者： Wang, Ling Lu, Jicang Zhou, Gang State Key Laboratory of Mathematical Engineering and Advanced Computing China

ISBN: (纸本)9781450384094

Knowledge graph representation learning provides a lot of help for subsequent tasks such as knowledge graph completion, information retrieval, and intelligent question answering. By representing the knowledge graph as low-dimensional dense vectors, these tasks can improve efficiency significantly. However, limited by the sparseness and huge scale of the actual structure, representation learning only from a structural perspective can no longer meet the research needs. To improve the performance, researchers introduced auxiliary information into representation learning. This paper focuses on models using text as auxiliary information, dividing all text-combined models into two categories: Closed-world assumption models and Open-world assumption models. The former is limited by the model's demand for the structural representation of the graph itself, and can only predict the entities and relationships that already exist in the knowledge graph. The latter can handle entities that have not previously been seen during model training, and connect brand-new entities to the knowledge graph, which is more in line with the dynamic trend of the knowledge graph in real world. Open-world assumption models can be further subdivided into multiple types according different joint functions, such as alignment function, fusion function, score function and transformation function. This paper summarizes existing methods in detail and looks forward to future possible research directions. © 2021 ACM.

关键词： Knowledge graph

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A Patch Analysis Method Based on Multi-firmware Comparison 2021

A Patch Analysis Method Based on Multi-firmware Comparison

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2021 International Symposium on Electrical, Electronics and Information engineering, ISEEIE 2021

作者： Xiao, Ruiqing Zhu, Yuefei Hu, Anxiang Zhu, Xiaoya Liu, Shengli State Key Laboratory of Mathematical Engineering and Advanced Computing China

ISBN: (纸本)9781450389839

A major user need is the completion of security audits by locating vulnerability functions using vulnerability information published by firmware manufacturers. However, it is difficult to manually analyze the relatively large number of patch functions involved in firmware updates. A patch analysis method based on multi-firmware comparison is presented in this study. This method narrows the search for suspected patch functions for vulnerability based on the similarities and differences between different firmware series involved in the vulnerability. Suspected patch functions in multiple firmware programs are first located and filtered based on intervals. Finally, a prototype system is implemented. The filtering results for the prototype system are used to manually locate two one-day vulnerabilities. The experimental results demonstrate that the two methods proposed in this study, namely, patch function location in multiple firmware programs and interval-based patch function filtering, effectively narrow the search for suspected patch functions and reduce the difficulty of manual analysis. © 2021 ACM.

关键词： Firmware

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Cross-Network User Matching Based on Association Strength 11th

Cross-Network User Matching Based on Association Strength

引用

11th International Conference on Computer engineering and Networks, CENet2021

作者： Jiang, Qiuyan Gong, Daofu Liu, Fenlin State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China Qingdao Agricultural University Qingdao266001 China

ISBN: (纸本)9789811665530

Cross-network user matching is the one of the fundamental problems in social network fusion and analysis. This paper proposes an unsupervised algorithm based on association strength to address this problem. Specifically, the algorithm firstly computes the association strength between nodes based on user behavior characteristics in the social network graph. Then, the node sequence is obtained by the biased walk method based on the association strength, and word2vec method is utilized to learn the node feature vector. Finally, cross-network user matching is achieved based on the distance of feature vectors. The highlight of this paper is that the bias walking based on the association strength can select nodes as the context to form a specific sequence, and then do benefits on feature vectors learning. The experimental results show that the proposed algorithm can effectively match user without any prior knowledge. Furthermore, the matching precision is improved compared to the baseline method. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Behavioral research

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MDFA: A Malware Detection Framework Based on Attention Mechanism with Bi-LSTM 4

MDFA: A Malware Detection Framework Based on Attention Mecha...

引用

4th International Conference on Applied Machine Learning, ICAML 2022

作者： Qi, Xuyan Wang, Huanwei Tang, Yonghe Liu, Wei Xie, Jiangtao State Key Laboratory of Mathematic Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781665462655

As the existing malware intelligent detection methods have shortcomings and low accuracy of manual feature extraction and feature processing, a Malware Detection Framework with Attention mechanism based on Bi-directional Long Short-Term Memory (MDFA) was proposed. To training this method, We have collected 15, 893 malware and 10, 959 benign program samples and constructed a dataset. Then, the sample data was labeled and sanitized by determining the results based on VirusTotal analysis. Finally, the Bi-directional Long Short-Term Memory model injected into the attention mechanism was built and trained to complete the malware detection. Experimental results show that the accuracy of MDFA was improved by at least 2. 3%, and the recall rate was improved significantly compared with the existing methods, verifying the effectiveness of MDFA. © 2022 IEEE.

关键词： Long short-term memory

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