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Who Needs the Most Research Effort? Investigating the Importance of Smart Contract Weaknesses 5th

Who Needs the Most Research Effort? Investigating the Impo...

引用

5th International conference on Blockchain and Trustworthy systems, BlockSys 2023

作者： Chen, Shijian Wang, Yanlin Zheng, Zibin School of Software Engineering Sun Yat-sen University Zhuhai519000 China

ISBN: (纸本)9789819981007

Smart contracts have witnessed widespread adoption across various industries since the introduction of Ethereum. Also, smart contracts’ vulnerabilities have gradually attracted academic attention, e.g., Oyente was developed to detect security bugs in contracts. However, it remains unclear which smart contract vulnerabilities require more research attention. To fill the gap, we recruit eight researchers to collect 281 Ethereum-related security events from SlowMist and Rekt, and analyze the root causes (e.g., the code weakness) behind each occurrence based on SWC Registry. Our results reveal that only 10 SWC weaknesses lead to security events, causing a total loss of USD 74 Billion. Moreover, Reentrancy exhibits the highest frequency. Our results provide guidance for research efforts allocated in the academic community. © 2024, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Smart contract

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AccMILP: An Approach for Accelerating Neural Network Verification Based on Neuron Importance 28th

AccMILP: An Approach for Accelerating Neural Network Verific...

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28th International conference on engineering of Complex computer systems (ICECCS)

作者： Zheng, Fei Xu, Qingguo Lei, Zhou Miao, Huaikou Shanghai Univ Sch Comp Engn & Sci Shanghai Peoples R China Shanghai Key Lab Comp Software Evaluating & Testi Shanghai Peoples R China

ISBN: (纸本)9783031664557;9783031664564

Deep Neural Networks (DNNs) have found successful applications in various non-safety-critical domains. However, given the inherent lack of interpretability in DNNs, ensuring their prediction accuracy through robustness verification becomes imperative before deploying them in safety-critical applications. Neural Network Verification (NNV) approaches can broadly be categorized into exact and approximate solutions. Exact solutions are complete but time-consuming, making them unsuitable for large network architectures. In contrast, approximate solutions, aided by abstraction techniques, can handle larger networks, although they may be incomplete. this paper introduces AccMILP, an approach that leverages abstraction to transform NNV problems into Mixed Integer Linear Programming (MILP) problems. AccMILP considers the impact of individual neurons on target labels in DNNs and combines various relaxation methods to reduce the size of NNV models while ensuring verification accuracy. the experimental results indicate that AccMILP can reduce the size of the verification model by approximately 30% and decrease the solution time by at least 80% while maintaining performance equal to or greater than 60% of MIPVerify. In other words, AccMILP is well-suited for the verification of large-scale DNNs.

关键词： Neural network Formal verification Mixed integer linear programming Neuron importance

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Online Learning Framework for Radio Link Failure Prediction in FANETs 18

Online Learning Framework for Radio Link Failure Prediction ...

引用

18th conference on computer Science and Intelligence systems, FedCSIS 2023

作者： Danilchenko, Kiril Lazmi, Nir Segal, Michael University of Waterloo Department of Electrical and Computer Engineering WaterlooON Canada School of Electrical and Computer Engineering Ben-Gurion University of the Negev Beer-Sheva Israel

ISBN: (纸本)9788396744784

In this paper, we consider the problem of prediction of Radio Link Failures (RLF) in flying ad hoc networks (FANETs). Many environmental factors that influence the quality of radio wave propagation are dynamic, and thus, drones must continually learn and update their radio link quality prediction model while they operate online. Online machine learning algorithms can be used to build adaptive RLF predictors without requiring a pre-deployment effort. To predict the RLF, we use an online machine learning algorithm and information gathering by message-passing from the neighbors. We propose an algorithm called ML-Net (Machine Learning and Network algorithm) to predict RLF. To the best of our knowledge, the combination of online machine learning algorithms together with the message-passing algorithm has not been used before. the proposed methodology outperforms the state-of-the-art online machine learning algorithms. © 2023 Polish Information Processing Society.

关键词： Forecasting

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engineering Smart Contracts with Symboleo: Progress Report 2024 34

Engineering Smart Contracts with Symboleo: Progress Report 2...

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34th Annual International conference on Collaborative Advances in software and Computing, CASCON 2024

作者： Mylopoulos, John Alfuhaid, Sofana Amyot, Daniel Anda, Amal Ahmed Logrippo, Luigi Meloche, Regan Rahimi-Kian, Ashkan Rajpal, Sahil Roveri, Marco Sivakumar, Durga Sousa-Dias, Daniel School of EECS University of Ottawa OttawaONK1N 6N5 Canada King Abdulaziz University Faculty of Computing and Information Technology Jeddah Saudi Arabia Université du Québec en Outaouais Département d'informatique et ingénierie GatineauQCJ8X 3X7 Canada IEMS Solution Ltd. Communitech KitchenerONN2G 1H6 Canada University of Trento Dept. of Information Engineering and Computer Science Trento Italy

ISBN: (纸本)9798331504830

Legal contracts have been used for millennia around the world as a foundation for business transactions. Smart con-tracts are cyber-physical systems that deploy Internet-of-things (IoT) and blockchain technologies to monitor, automate, and control the execution of legal contracts. Because of the conceptual complexity of legal concepts and the technologies deployed, developing smart contracts is a time-consuming and error-prone task. the aim of this project is to develop concepts, tools and methods for engineering smart contracts. the contributions of the project consist of a specification language, called Symboleo, and related tools that are intended to make the engineering of smart contracts more systematic and cost effective. this paper summarizes earlier outcomes of the project and reports on new results obtained during the past year. © 2024 IEEE.

关键词： Smart contract

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Spyware Alert System and Image Steganography for Military Application 5th

Spyware Alert System and Image Steganography for Military Ap...

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5th International conference on Deep Learning, Artificial Intelligence and Robotics, ICDLAIR 2023

作者： Sushma, Nandigama Kiran, Gandikota Prashanth Satya Sai Vinay, Esa Chavva, Subba Reddy Department of IT VR Siddhartha Engineering College Vijayawada India

ISBN: (纸本)9783031609343

Cybersecurity is increasingly threatened by the growing prevalence of spyware and other malicious software intended to enter computer systems and modify data. this research paper proposes a ground- breaking amalgamation of steganography techniques with spyware detection procedures to counter these risks and secure against unwanted access and covert data exfiltration. this study proposes a novel strategy to strengthen military cybersecurity by utilizing advanced image steganography techniques and creating a specialized Spyware Alert System. In the context of military operations, the Spyware Alert System uses cutting-edge threat detection algorithms and anomaly analysis to give real-time alerts and effective solutions against spyware threats. the paper also incorporates image steganography, a potent method for encrypting sensitive information and hiding it within digital photographs to protect the security and integrity of mission-critical data. By ensuring the secrecy and accuracy of critical information during military operations, this comprehensive solution strives to strengthen military data protection. the article provides a thorough approach for fending against the constantly changing panorama of cyber threats by outlining the aims, methodology, and anticipated contributions to military cybersecurity. this project aims to improve the security and resilience of military systems in the face of persistent digital attackers by pioneering this innovative amalgamation of technologies. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Military photography

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Ensemble Based Detection Model for DDoS Attacks in SDNs Using Advanced Feature Selection 17

Ensemble Based Detection Model for DDoS Attacks in SDNs Usin...

引用

17th International conference on Signal Processing and Communication systems, ICSPCS 2024

作者： Alashhab, Abdussalam Ahmed Edrah, Aisha Mohd Zahid, Mohd Soperi Siddikur Rahman, Md. Department of Computer and Information Sciences Universiti Teknologi Petronas Seri Iskandar Malaysia Faculty of Information Technology Alasmarya Islamic University Zliten Libya Western University Faculty of Engineering Department of Electrical and Computer Engineering LondonONN6A 5B9 Canada Universiti Teknologi Petronas Department of Electrical and Electronics Engineering Seri Iskandar Malaysia

ISBN: (纸本)9798350389630

software-Defined Networking (SDN) enhances flexibility, scalability, and innovation by decoupling the control plane from the data plane, managed through streamlined controller operations. However, Distributed Denial of Service (DDoS) attacks pose significant cybersecurity threats to SDNs, disrupting services by flooding targeted systems with traffic from multiple sources. Real-time detection of these attacks remains challenging, as traditional methods often lack the ability to accurately identify complex attack patterns due to limited feature sets. To address this, we propose an ensemble-based model that combines three classifiers (SGD, EBM, and MLP) for effective DDoS attack detection and mitigation in SDNs. Our approach integrates a reliable feature selection methodology, leveraging Principal Component Analysis (PCA) to identify the most informative features for attack detection. this enhancement significantly improves the accuracy and efficiency of the ensemble model. Evaluated using the CIC-DDoS2019 and InSDN datasets, our model demonstrates substantial improvements in detection rates and computational efficiency. Results show that the proposed approach achieves 99% accuracy, underscoring its potential as a resilient solution for DDoS attack detection in SDNs. © 2024 IEEE.

关键词： Denial-of-service attack

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LS-DYNA-Based Car Frontal Fascia Simulation During Collision 14th

LS-DYNA-Based Car Frontal Fascia Simulation During Collision

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14th EAI International conference on Broadband Communications, Networks, and systems, BROADNETS 2024

作者： Prasanna, Jalapudi Laxmi Kumar, Kandukuri Vasantha Dhanraj, Burragalla Madduri, Uday Kumar Department of Mechanical Engineering JNTUH University College of Engineering Jagtial505501 India Department of Mechanical Engineering Vardhaman College of Engineering Hyderabad501218 India

ISBN: (纸本)9783031811708

the bumper beam assembly of an automobile is crucial for absorbing impact energy and shielding occupants from front and rear collisions. Crash testing is a type of destructive testing that is typically used to verify that cars or related components meet safe design requirements for crashworthiness including crash compatibility. Utilizing computer software to simulate car wrecks has become a vital strategy for cutting expenses and development times for automobiles. this article examines the frontal fascia of cars in a simulated crash test. the simulation of a car collision test is reported in this paper. this work seeks to verify the findings of simulating a frontal impact collision of an automobile. In order to lessen the forces encountered during the crash, it is also intended to change some of the materials that make up the parts. To assess the vehicle's collision characteristics, computer models were utilized. this particular model was a version of the Chevrolet C1500 pickup truck. the simulation is conducted using LS-DYNA software. the automobile sector uses it extensively to analyze car design. It foretells a car's actions in an accident with accuracy. the simulation's results were then verified by contrasting them with the outcomes of an identical test conducted by the NCAC (National Crash Analysis Center). © ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Crashworthiness

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Continuous Integration of Neural Networks in Autonomous systems 8th

Continuous Integration of Neural Networks in Autonomous Sy...

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8th International conference on engineering of computer-Based systems, ECBS 2023

作者： Steffen, Bruno Zohren, Jonas Pazarci, Utku Kullmann, Fiona Weißenfels, Hendrik Technische Universität Dortmund Dortmund44149 Germany

ISBN: (纸本)9783031492518

the perception of the autonomous driving software of the FS223, a low-level sensor fusion of Lidar and Camera data requires the use of a neural network for image classification. To keep the neural network up to date with updates in the training data, we introduce a Continuous Integration (CI) pipeline to re-train the network. the network is then automatically validated and integrated into the code base of the autonomous system. the introduction of proper CI methods in these high-speed embedded software applications is an application of state-of-the-art MLOps techniques that aim to provide rapid generation of production-ready models. It further serves the purpose of professionalizing the otherwise script-based software production, which is re-done almost completely every year as the teams change from one year to the next. © 2024, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Integration

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Multi-modal Graph Attention Network for Video Recommendation 5

Multi-modal Graph Attention Network for Video Recommendation

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5th IEEE International conference on computer and Communication engineering Technology, CCET 2022

作者： Liu, Huizhi Li, Chen Tian, Lihua Xi'an Jiaotong University School of Software Engineering Xi'an China

ISBN: (数字)9781665467353

ISBN: (纸本)9781665467353

In view of the problems of cold start and data interaction in recommendation systems, and most current recommendation algorithms ignore the diversity of data types, the combination of multimodal data and knowledge graph is bound to improve the pertinence of video recommendation. In this paper, we propose Multi-modal Knowledge Graph Attention Network (MMKGV) model, and all the entity nodes of the knowledge graph are innovatively introduced into multimodal information. the high-order recursive node information dissemination and information aggregation are carried out on the multimodal knowledge graph through the graph attention network. In the model, the triplet function of the knowledge graph is used to construct the triplet inference relationship, and the vector representation generated by the final aggregation is used for recommendation. through extensive experiments on two public datasets TikTok and Kwai, the results show that the MMKGV can effectively improve the effect of video recommendation compared with other comparison algorithms. © 2022 IEEE.

关键词： Recommender systems

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Residuality theory, random simulation, and attractor networks

Residuality Theory, random simulation, and attractor network...

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13th International conference on Ambient systems, Networks and Technologies, ANT 2022 / 5th International conference on Emerging Data and Industry 4.0, EDI40 2022

作者： O'Reilly, Barry M Black Tulip Technology Department of Engineering and Innovation Open University Milton Keynes United Kingdom

this article reviews earlier articles on the topic of residuality theory and places residuality theory in the context of the complexity sciences, relating the major concepts of residuality theory in terms of Kauffman Networks, complex networks, random simulations, and attractors. this paper aims to outline the logic of the theory and to expand on the use of matrices for encouraging emergent component decomposition, at the same time as proposing a related theory of software engineering that allows comparison between residuality theory and other approaches. © 2022 Elsevier B.V.. All rights reserved.

关键词： antifragility Residuality resilience software engineering

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