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Dynamic Evolutionary Game-based Modeling,Analysis and Performance Enhancement of Blockchain Channels

IEEE/CAA Journal of Automatica Sinica 2023年第1期10卷 188-202页

作者： PeiYun Zhang MengChu Zhou ChenXi Li Abdullah Abusorrah IEEE the School of Computer Science Nanjing University of Information Science&TechnologyNanjing 210044China the Helen and John C.Hartmann Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA the School of Computer and Information Anhui Normal UniversityWuhu 241003China the Department of Electrical and Computer Engineering Faculty of Engineeringand Center of Research Excellence in Renewable Energy and Power SystemsKing Abdulaziz UniversityJeddah 21481Saudi Arabia

The recent development of channel technology has promised to reduce the transaction verification time in blockchain operations.When transactions are transmitted through the channels created by nodes,the nodes need to cooperate with each other.If one party refuses to do so,the channel is unstable.A stable channel is thus required.Because nodes may show uncooperative behavior,they may have a negative impact on the stability of such channels.In order to address this issue,this work proposes a dynamic evolutionary game model based on node behavior.This model considers various defense strategies'cost and attack success ratio under them.Nodes can dynamically adjust their strategies according to the behavior of attackers to achieve their effective defense.The equilibrium stability of the proposed model can be achieved.The proposed model can be applied to general channel networks.It is compared with two state-of-the-art blockchain channels:Lightning network and Spirit channels.The experimental results show that the proposed model can be used to improve a channel's stability and keep it in a good cooperative stable state.Thus its use enables a blockchain to enjoy higher transaction success ratio and lower transaction transmission delay than the use of its two peers.

关键词： Blockchain channel network evolutionary game malicious behavior secure computing stability analysis

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Android Malware Detection Method Based on Permission Complement and API Calls

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Chinese Journal of Electronics 2022年第4期31卷 773-785页

作者： YANG Jiyun TANG Jiang YAN Ran XIANG Tao College of Computer Science Chongqing University

The dynamic code loading mechanism of the Android system allows an application to load executable files externally at runtime. This mechanism makes the development of applications more convenient, but it also brings security issues. Applications that hide malicious behavior in the external file by dynamic code loading are becoming a new challenge for Android malware detection. To overcome this challenge, based on dynamic code loading mechanisms, three types of threat models,i.e. Model I, Model II, and Model III are defined. For the Model I type malware, its malicious behavior occurs in Dex Code, so the application programming interface(API)classes were used to characterize the behavior of the DexCode file. For the Model II type and Model III type malwares whose malicious behaviors occur in an external file,the permission complement is defined to characterize the behaviors of the external file. Based on permission complement and API calls, an Android malicious application detection method is proposed, of which feature sets are constructed by improving a feature selection method. Five datasets containing 15,581 samples are used to evaluate the performance of the proposed method. The experimental results show that our detection method achieves accuracy of 99.885% on general dataset, and performes the best on all evaluation metrics on all datasets in all comparison methods.

关键词： Android malicious application detection method threat models API calls Data security malicious behavior Android malware detection method Operating systems DexCode file executable files application programming interface classes Android (operating system) Ubiquitous and pervasive computing permission complement invasive software external file dynamic code loading mechanism application program interfaces feature selection method feature selection

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ArgusDroid: detecting Android malware variants by mining permission-API knowledge graph

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Science China(Information Sciences) 2023年第9期66卷 115-133页

作者： Yude BAI Sen CHEN Zhenchang XING Xiaohong LI College of Intelligence and Computing Tianjin University Research School of Computer Science Australian National University

Malware family variants make minor and relevant changes of behaviors based on the original malware. To analyze and detect family variants, security experts must not only understand malware behaviors but also further observe the correlation between the features of these behaviors. However, the recent data-driven based behavior features are too independent and sometimes too general to obtain a comprehensive profile of the changeable malicious behaviors of family variants derived from the original malware.Those features additionally suffer from limited semantic knowledge which narrows the comprehension of family variants. To this end, in this paper, we propose ArgusDroid that takes advantage of the knowledge graph(KG) to construct a permission-API knowledge graph based on the official Android document. Because each permission or API in the document is described by a specific sentence, we can easily acquire and comprehend the relationship between different features via the hyperlink in sentences or sentence similarity. ArgusDroid also extracts various feature sets from the knowledge graph and validates the detection performance on Android malware family variants based on these features. Extensive experiments by using machine learning and neural network classifiers for variant identification have been carried out. The experimental results demonstrate the effectiveness and usefulness of our obtained feature sets based on ArgusDroid, especially when using the classifiers convolutional neural network(CNN) and multi-layer perception(MLP). Furthermore,when compared to similar feature sets that aim to present relationships across different feature types, such as Axplorer, ArgusDroid generates the feature set which significantly improves malware variant detection by 0.3575 average F1.

关键词： malicious behavior Android document knowledge graph malware family variant machine learning

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“Half of the Node Records Are Forged?”: The Problemof Node Records Forgery in Ethereum Network

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Computer Modeling in Engineering & Sciences 2024年第2期138卷 1713-1729页

作者： Yang Liu Zhiyuan Lin Yuxi Zhang Lin Jiang Xuan Wang School of Computer Science and Technology Harbin Institute of Technology(Shenzhen)Shenzhen518055China Peng Cheng Laboratory Shenzhen518055China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies Shenzhen518055China

Ethereum, currently the most widely utilized smart contracts platform, anchors the security of myriad smartcontracts upon its own robustness. Its foundational peer-to-peer network facilitates a dependable node connectionmechanism, whereas an efficient data-sharing protocol constitutes as the bedrock of Blockchain network security.In this paper, we propose NodeHunter, an Ethereum network detector implemented through the application ofsimulation technology, which is capable of aggregating all node records within the network and the interconnectednessbetween them. Utilizing this connection information, NodeHunter can procure more comprehensive insightsfor network status analysis compared to preceding detection methodologies. Throughout a three-month period ofunbroken surveillance of the Ethereum network, we obtained an excess of two million node records along with overone hundred million node acquaintances. Analysis of the gathered data revealed that an alarming 49% or more ofthese node records were maliciously forged.

关键词： Blockchain ethereum peer-to-peer networks node discovery protocol malicious behavior

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