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Honeytoken-Detector: A Symbolic Execution-Based Honeypot Token Detection Tool 26

Honeytoken-Detector: A Symbolic Execution-Based Honeypot Tok...

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26th ACIS International Winter Conference on software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD-Winter 2023

作者： Liu, Yi Cai, Lizhi School of Information Science and Engineering East China University of Science and Technology Shanghai China Shanghai Development Center of Computer Software Technology Shanghai Key Laboratory of Computer Testing & Evaluating Shanghai China

ISBN: (纸本)9798350345865

Smart contracts have emerged as one of the most successful applications in the blockchain domain, playing a significant role in various blockchain ecosystems. Inspired by smart contracts, a multitude of cryptographic assets have been created. To standardize these assets, industry standards such as ERC20 (Ethereum Request for Comments 20), ERC721, and ERC1155 have been proposed. In recent years, smart contracts have frequently fallen victim to attacks. Honeypot contracts, disguised as ERC20-compliant tokens, are widely prevalent on the blockchain, enticing victims to make purchases. Such malicious smart contracts exhibiting deceptive behavior are collectively referred to as honeypot tokens. This paper focuses on ERC20-compliant smart contracts and defines six common types of honeypot issues. Building upon advancements in smart contract vulnerability detection, we propose an enhanced symbolic execution-based detection tool called Honeytoken-Detector. We conduct experiments on both contracts known to have similar issues and actual token contracts from the real world. The experimental results demonstrate the effectiveness of our tool in identifying vulnerabilities. © 2023 IEEE.

关键词： Smart contract

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Analysis for Microservice Architecture Application Quality Model and testing Method 26

Analysis for Microservice Architecture Application Quality M...

引用

26th ACIS International Winter Conference on software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD-Winter 2023

作者： Gong, Jiayu Cai, Lizhi Shanghai Development Center of Computer Software Technology Shanghai Key Laboratory of Computer Software Testing & Evaluation Shanghai China

ISBN: (纸本)9798350345865

In recent years, microservice architectures have benefited from low coupling and high cohesion characteristics, allowing flexible on-demand deployment of complex applications and simplifying the difficulty of developing and maintaining large software systems. However, there is limited research on quality models and testing methods for microservice architectures, which leads to the problem that microservice architecture applications are difficult to test and evaluate. This paper analyzes the main problems when microservice architecture applications face quality measurement and software testing, further improves and refines the quality model of microservice architecture applications on the basis of the existing ISO software product quality model, and introduces testing methods for microservice architecture applications, which can provide reference for the construction of microservice architecture applications. © 2023 IEEE.

关键词： software testing

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LDE-UNet: A Novel Model for Rapid COVID-19 Diagnosis via CT Image Segmentation

LDE-UNet: A Novel Model for Rapid COVID-19 Diagnosis via CT ...

引用

2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Wang, QiSong Wu, JiaRou Wang, YingZhuo Qu, HuiLi Nie, Shanling Yang, Hai East China University of Science and Technology Department of Computer Science and Engineering Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China The University of Sydney Faculty of Engineering Australia

ISBN: (纸本)9798350337488

The COVID-19 pandemic has had a profound impact on human society. It has highlighted the need for faster diagnostic methods. Research has shown that combining semantic segmentation with traditional medical approaches can significantly accelerate the process. To address this, leveraging COVID-19 CT images, our team designs a revolutionary semantic segmentation model called Level of Detail Enhancement U-Net (LDE-UNet), which shows the lesion area on CT images. By introducing the LDE block, the model has the unique advantage of overcoming the loss of data details during the downsampling process by emphasizing and transmitting details at the same level. Our SOTA model outperforms the second-best model by at least 0.7% in the most critical indicator precision. Compared with other models, LDE-UNet's strong reliability determines its ability to be used in the medical field to accelerate the localization and division of lesion areas on CT images by professional doctors, thus completing patient diagnosis faster. In addition, we also propose a standardized method for processing medical images. © 2023 IEEE.

关键词： COVID-19 Downsample LDE-UNet Semantic segmentation

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An Evaluation System for Large Language Models based on Open-Ended Questions

An Evaluation System for Large Language Models based on Open...

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IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)

作者： Zhiyuan Cao Zeyu Ma Mingang Chen Computer and Information Engineering College Shanghai Polytechnic University Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai Development Center of Computer software Technology Shanghai China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai Development Center of Computer software Technology Mathematical and Science College Shanghai Normal University Shanghai China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai Development Center of Computer software Technology Shanghai China

ISBN: (数字)9798350376982

ISBN: (纸本)9798350376999

We designed a large language model evaluation system based on open-ended questions. The system accomplished multidimensional evaluation of LLMs using open-ended questions, and it presented evaluation results with evaluation reports. Currently, the evaluation of large-scale language models often exists with two prominent limitations: (1) The evaluation methods are often single-minded, resulting in less credible results. (2) Most evaluations are based on datasets with closed-ended questions, treating generative large language models as discriminative models, which fails to adequately reflect the high output flexibility characteristic of these models. For these two limitations, we proposed an evaluation system for LLMs based on open-ended questions. Our experiments on the adapted open-source datasets demonstrated the effectiveness of this system. The code of the system was released on https://***/JerryMazeyu/GreatLibrarian.

关键词： Adaptation models Cloud computing Accuracy Codes Large language models System performance Reflection

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Automated Functionality and Security Evaluation of Large Language Models

Automated Functionality and Security Evaluation of Large Lan...

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IEEE International Conference on Smart Cloud (SmartCloud)

作者： Minjie Ding Ying Shen Mingang Chen Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai Development Center of Computer Software Technology Shanghai China

ISBN: (数字)9798350389500

ISBN: (纸本)9798350389517

Natural language processing (NLP) is rapidly developing. A series of Large Language Models (LLMs) have emerged, represented by ChatGPT, which have made significant breakthroughs in natural language understanding and generation, enabling fluent dialogue with humans, understanding human intentions, and completing complex tasks. However, in addition to the fairness and toxicity of traditional language models, some new problems, including hallucination, have also emerged in LLMs, making them hard to use. evaluating LLMs manually is challenging due to subjectivity and inefficiency. In this paper, we focused on the fuzzy matching, toxicity detection, and hallucination detection in the evaluation of LLMs automatically, and fine-tune the Mixtral-8x7B Model, which can be deployed in private cloud environment, and prove the effectiveness of our method through experiments.

关键词： Cloud computing Toxicology Accuracy Graphics processing units Chatbots Security Task analysis

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PSO-Based Black-Box Lane Detection Adversarial Attack

PSO-Based Black-Box Lane Detection Adversarial Attack

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Artificial Intelligence, Human-computer Interaction and Robotics (AIHCIR), International Conference on

作者： Hongtao Fang Ruiyun Wang Zeyu Ma Mingang Chen Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai Polytechnic University shanghai china Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai Normal University shanghai china Shanghai Key Laboratory of Computer Software Testing and Evaluating shanghai china

Autonomous driving has achieved rapid development and promising performance by employing machine learning algorithms to automatically execute various maneuvers. Lane detection is one of the foremost tasks for vehicles, with a direct impact on driving decisions. Lane detection is a crucial component of autonomous driving, working in conjunction with lane centering control (LCC) and adaptive cruise control (ACC) to enable advanced driving assistance functions. At first thought, lane detection may seem immune to attacks. However, is this really the case? In this paper, we conduct experiments on lane detection modules and reveal their susceptibility to attacks exploiting hypersensitivity. Specifically, an excessively sensitive lane detector may mistake small markings for valid lanes, causing the vehicle to follow the wrong path. Through experiments, we demonstrate the vulnerability of lane detection models to adversarial attacks, even under black-box conditions where a change in one pixel can induce errors. We discuss the contrast between gradient-based and heuristic search-based optimization methods for black-box attacks and demonstrate the superiority of heuristic approaches for this task. Leveraging particle swarm optimization (PSO), we carry out lane detection attacks to address the research gap for black-box lane attacks.

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IVIFUZZER: A Fuzzing method for vulnerability exploitation on the operating interface of the In-Vehicle Infotainment system

IVIFUZZER: A Fuzzing method for vulnerability exploitation o...

引用

Artificial Intelligence, Networking and Information Technology (AINIT), International Seminar on

作者： Jianhua Wu Xin Zhang Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai Development Center of Computer Software Technology Shanghai China

ISBN: (数字)9798350385557

ISBN: (纸本)9798350385564

As an important information system of intelligent and connected vehicles, most In-Vehicle Infotainment systems are set up and manage the vehicles through user interaction. This paper presents a Fuzzing method IVIFUZZER for vulnerability exploitation on the operation interface of the In-Vehicle Infotainment system. With the help of automated robotic arm and improved OCR technology, collecting system leakage information and directional detection of the identified input points, 6 zero day vulnerabilities were found. The experimental results show that the proposed IVIFUZZER method is effective.

关键词： Seminars Industries Optical character recognition Passwords Fuzzing Manipulators Information leakage

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Modif-SegUnet: Innovatively Advancing Liver Cancer Diagnosis and Treatment through Efficient and Meaningful Segmentation of 3D Medical Images

Modif-SegUnet: Innovatively Advancing Liver Cancer Diagnosis...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Wang, Chenhang Jin, Yujian Liang, Jiayi Yang, YuHan Nie, Shanling Hai, Yang East China University of Science and Technology Department of Computer Science and Engineering Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China The University of Sydney Faculty of Engineering Sydney Australia

ISBN: (纸本)9798350337488

Hepatocellular Carcinoma (HCC) holds a record of high incidence and severe global harm. In tasks of liver cancer segmentation based on 3D medical images, the majority of methods have endeavored to enhance the 3D U-net by integrating the latest modules from the field of computer Vision (such as the transformer), often overlooking the distinct characteristics of liver components. We introduced a novel deep learning architecture, Modif-SegUnet, to circumvent this limitation. This architecture extends the U-net model by incorporating a 3D-modifiable attention module, thereby fostering a heightened focus on distinguishing between normal liver sections and lesions. Furthermore, Modif-SegUnet ingeniously amalgamates the 3D-modifiable attention module with the Modif-transformer block, enabling efficient capture of relevant and valuable full-text information in CT images that contain liver or tumor regions. We subjected the proposed Modif-SegUnet to evaluation on the Liver Tumor Segmentation benchmark dataset. Experimental outcomes indicate that our methodology surpasses state-of-the-art approaches in liver tumor segmentation, suggesting potential pathways for advancing the diagnosis and treatment of liver cancer. © 2023 IEEE.

关键词： 3D medical image attention CT Liver cancer Segmentation transformer U-Net

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Learning Commonality, Divergence and Variety for Unsupervised Visible-Infrared Person Re-identification 38

Learning Commonality, Divergence and Variety for Unsupervise...

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Shi, Jiangming Yin, Xiangbo Zhang, Yachao Zhang, Zhizhong Xie, Yuan Qu, Yanyun Institute of Artificial Intelligence Xiamen University China School of Informatics Xiamen University China Shanghai Innovation Institute China East China Normal University China Shanghai Key Laboratory of Computer Software Evaluating and Testing China

Unsupervised visible-infrared person re-identification (USVI-ReID) aims to match specified persons in infrared images to visible images without annotations, and vice versa. USVI-ReID is a challenging yet underexplored task. Most existing methods address the USVI-ReID through cluster-based contrastive learning, which simply employs the cluster center to represent an individual. However, the cluster center primarily focuses on commonality, overlooking divergence and variety. To address the problem, we propose a Progressive Contrastive Learning with Hard and Dynamic Prototypes for USVI-ReID. In brief, we generate the hard prototype by selecting the sample with the maximum distance from the cluster center. We reveal that the inclusion of the hard prototype in contrastive loss helps to emphasize divergence. Additionally, instead of rigidly aligning query images to a specific prototype, we generate the dynamic prototype by randomly picking samples within a cluster. The dynamic prototype is used to encourage variety. Finally, we introduce a progressive learning strategy to gradually shift the model's attention towards divergence and variety, avoiding cluster deterioration. Extensive experiments conducted on the publicly available SYSU-MM01 and RegDB datasets validate the effectiveness of the proposed method. © 2024 Neural information processing systems foundation. All rights reserved.

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CIXG: A Comprehensive Approach to Driver Gene Identification and Causal Interpretation

CIXG: A Comprehensive Approach to Driver Gene Identification...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Liu, Yawen Chen, Yiwen Nie, Shanling Yang, Hai East China University of Science and Technology Department of Computer Science and Engineering Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China National University of Singapore Center for Continuing and Lifelong Education Singapore University of Sydney Faculty of Engineering Australia

ISBN: (纸本)9798350386226

With the ongoing advancements in science and technology and the increasing research focus on cancer-related issues, there has been a proliferation of omics-related resources for in-depth analysis and exploration. This burgeoning volume and complexity of biological data have fostered the integration of machine-learning techniques into biology. As a result, numerous machine-learning strategies have been established to identify driver mutations. Yet, many of these strategies produce complex models, complicating comprehension and thereby clouding the impact of input features on the resulting predictions. Our analysis presented the CIXG framework, which integrates a driver gene prediction module using XGBoost with a causality interpretation module anchored on CXPlain. This architecture enables quantifying each input feature's contribution to the prediction outcome and ensures precise predictions of driver genes. When benchmarked against the state-of-the-art (SOTA) method, CIXG demonstrated superior accuracy in pinpointing driver genes across pan-cancer studies and within the 32 specific cancer types. Importantly, our results underscored that mutation features chiefly influence CIXG's predictive prowess, with additional support from other omics features. © 2024 IEEE.

关键词： Adversarial machine learning

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