基于深度残差网络的庞氏骗局合约检测方法

现有针对庞氏骗局智能合约的检测方法大多基于合约操作码特征和账户特征,对初步部署合约检测效果一般。对此,提出一种基于深度残差网络的庞氏骗局合约检测方法。首先,通过分析智能合约操作码特点,提出单点词嵌入编码算法(single word embedding coding algorithm, SWEC),对智能合约进行重新编码。然后,利用关键操作码提取方法,提取关键操作码(critical operation code, CO)及权重值,并以此设计关键操作码权重模块,改进深度残差网络用于合约检测。最后,在公开数据集上进行相关实验,实验结果表明,基于深度残差网络的庞氏骗局合约检测方法具有99.7%的查准率和99.9%的查全率,相比现有方法有较大提升,能够更加准确地识别庞氏骗局合约。     

Applying blockchain-based method to smart contract classification for CPS applications

Smart contract has been the core of blockchain systems and other blockchain-based systems since Blockchain 2.0. Various operations on blockchain are performed through the invocation and execution of smart contracts. This leads to extensive combinations between blockchain, smart contract, Internet of Things (IoT) and Cyber-Physical System (CPS) applications, and then many blockchain-based IoT or CPS applications emerge to provide multiple benefits to the economy and society. In this case, obtaining a better understanding of smart contracts will contribute to the easier operation, higher efficiency and stronger security of those blockchain-based systems and applications. Many existing studies on smart contract analysis are based on similarity calculation and smart contract classification. However, smart contract is a piece of code with special characteristics and most of smart contracts are stored without any category labels, which leads to difficulties of smart contract classification. As the back end of a blockchain-based Decentralized Application (DApp) is one or several smart contracts, DApps with labeled categories and open source codes are applied to achieve a supervised smart contract classification. A three-phase approach is proposed to categorize DApps based on various data features. In this approach, 5,659 DApps with smart contract source codes and pre-tagged categories are first obtained based on massive collected DApps and smart contracts from Ethereum, State of the DApps and DappRadar. Then feature extraction and construction methods are designed to form multi-feature vectors that could present the major characteristics of DApps. Finally, a fused classification model consisting of KNN, XGBoost and random forests is applied to the multi-feature vectors of all DApps for performing DApp classification. The experimental results show that the method is effective. In addition, some positive correlations between feature variables and categories, as well as several user behavior patterns of DApp calls, are found in this paper.     

An analysis of the fees and pending time correlation in Ethereum

Ethereum is a new blockchain-based platform that is also capable of running smart contracts. Despite its increasing popularity, there is a lack of studies on characterizing this system, in special the fees paid by users and the respective delay to confirm the transactions, that is, the pending time. In this sense, we study the main features of Ethereum transactions and evaluate the common belief—for blockchain systems that rely on proof of work—that users who pay higher fees will have their transactions confirmed faster. Specifically, we collect information about 7.2 million of transactions in Ethereum and correlate their pending time to several fee-related features. Moreover, we conduct our study evaluating different ranges of values for the features, such as default and unusual values adopted by users as well as clusters of users with similar behaviors. Our empirical analysis shows strong evidence that there is no clear correlation between fees-related features and the pending time. Overall, we conclude from our investigation that transaction's features, including gas and gas price defined by users, cannot determine the pending time of transactions.     

Modeling and performance analysis of multi‐service wireless CDMA cellular networks using smart antennas

In this paper, we present an analytical model to assess the blocking capacity of multi‐service code division multiple access (CDMA) systems. We include smart antenna systems in our model and show how the capacity of CDMA systems can be improved if smart antennas are employed at the base stations. Applying smart antennas can actually transform CDMA systems from being interference limited to being channel/code limited. To investigate this effect, we extend our model to include the limitation of channelization codes in CDMA‐based universal mobile telecommunication system (UMTS) systems. From the point of view of the call admission control (CAC) in a smart antenna CDMA system, we can either accept the capacity loss due to code limitation, or we can additionally apply space division multiple access (SDMA) techniques to re‐use channelization codes and thus re‐approach the capacity which is obtained if no code limitation is considered. Copyright © 2008 John Wiley & Sons, Ltd.     

Multi-input address incremental clustering for the Bitcoin blockchain based on Petri net model analysis

Bitcoin is a cryptocurrency based on blockchain. All historical Bitcoin transactions are stored in the Bitcoin blockchain, but Bitcoin owners are generally unknown. This is the reason for Bitcoin's pseudo-anonymity, therefore it is often used for illegal transactions. Bitcoin addresses are related to Bitcoin users' identities. Some Bitcoin addresses have the potential to be analyzed due to the behavior patterns of Bitcoin transactions. However, existing Bitcoin analysis methods do not consider the fusion of new blocks' data, resulting in low efficiency of Bitcoin address analysis. In order to address this problem, this paper proposes an incremental Bitcoin address cluster method to avoid re-clustering when new block data is added. Besides, a heuristic Bitcoin address clustering algorithm is developed to improve clustering accuracy for the Bitcoin Blockchain. Experimental results show that the proposed method increases Bitcoin address cluster efficiency and accuracy.     

移动社交网络中即时推荐好友的方法(英)

Differently from the general online social network(OSN),locationbased mobile social network(LMSN),which seamlessly integrates mobile computing and social computing technologies,has unique characteristics of temporal,spatial and social correlation.Recommending friends instantly based on current location of users in the real world has become increasingly popular in LMSN.However,the existing friend recommendation methods based on topological structures of a social network or non-topological information such as similar user profiles cannot well address the instant making friends in the real world.In this article,we analyze users' check-in behavior in a real LMSN site named Gowalla.According to this analysis,we present an approach of recommending friends instantly for LMSN users by considering the real-time physical location proximity,offline behavior similarity and friendship network information in the virtual community simultaneously.This approach effectively bridges the gap between the offline behavior of users in the real world and online friendship network information in the virtual community.Finally,we use the real user check-in dataset of Gowalla to verify the effectiveness of our approach.     

User’s behavior study for smart houses occupant prediction

This paper deals with the smart house occupant prediction issue based on daily life activities. Based on data provided by nonintrusive sensors and devices, our approach use supervised learning technics to predict the house occupant. We applied support vector machines classifier to build a behavior classification model and learn the users’ habits when they perform activities for predicting and identifying the house occupant among a group of inhabitants. We analyzed the publicly available dataset from the Washington State University smart apartment tesbed. We particulary studied the grooming, having breakfast and bed to toilet activities. The results showed a hight prediction precision and demonstrated that each user has his own manner to perform his daily activities and can be easily identified by just learning his habit.     

基于社会网络的语义Web服务发现模型

web服务发现模型是为了满足用户各种用途的web服务方式,以代码重入和松散耦合的方式以最优的组合实现可以提供用户需要的web服务模型。web服务发现模型是一种面向服务系统架构的一类基础性问题,但是又是解决用户不同需求十分有效的模型。通过对本体特性和发现模型的结构特点的研究,在本文中引入了本体解析的web服务标注算法和环境语义web发现模型。从而更好地实现web服务发现模型的选择用户所要求的服务的准确性,也为web服务的智能匹配做出了探索。     

A blockchain-enabled security management framework for mobile edge computing

Mobile edge computing (MEC) integrates mobile and edge computing technologies to provide efficient computing services with low latency. It includes several Internet of Things (IoT) and edge devices that process the user data at the network's edge. The architectural characteristic of MEC supports many internet-based services, which attract more number of users, including attackers. The safety and privacy of the MEC environment, especially user information is a significant concern. A lightweight accessing and sharing protocol is required because edge devices are resource constraints. This paper addresses this issue by proposing a blockchain-enabled security management framework for MEC environments. This approach provides another level of security and includes blockchain security features like temper resistance, immutable, transparent, traceable, and distributed ledger in the MEC environment. The framework guarantees secure data storage in the MEC environment. The contributions of this paper are twofold: (1) We propose a blockchain-enabled security management framework for MEC environments that address the security and privacy concerns, and (2) we demonstrate through simulations that the framework has high performance and is suitable for resource-constrained MEC devices. In addition, a smart contract-based access and sharing mechanism is proposed. Our research uses a combination of theoretical analysis and simulation experiments to demonstrate that the proposed framework offers high security, low latency, legitimate access, high throughput, and low operations cost.     

基于Bootstrap技术的Web服务QoS值的置信区间估计和预测

目前对Web服务QoS（Quality of Service）的预测研究,通常预测QoS的静态值,很少预测QoS值的置信区间.本文借助非参数统计学的Bootstrap技术,提出估计Web服务QoS值置信区间的方法;然后利用与当前Web用户相似的其他Web用户调用待预测Web服务的QoS历史数据,预测当前Web用户调用待预测Web服务的QoS值的置信区间.本文估计了WSDream数据集1中每个用户调用每个Web服务的QoS值的置信区间,实验发现这些置信区间的上下限近似服从重尾分布.通过随机选择WSDream数据集1中60%到90%的用户和Web服务作为训练集,预测另外10%到40%的用户和Web服务的QoS值,实验结果表明预测的QoS置信区间与估计的QoS置信区间的平均覆盖率超过70%,最高达76%.在服务选择或服务推荐时给用户提供一个估计的或预测的QoS置信区间,可以更好地满足用户的个性化需求.     

先验引导的特征金字塔阴影检测网络

阴影检测向来是计算机视觉领域的一个基础性挑战。它需要网络理解图像的全局语义和局部细节信息。本文提出了一种检测阴影区域的先验特征金字塔网络结构。该网络搭建了先验加权模块来提取图像中蕴含的阴影先验信息,通过使用阴影先验信息加权卷积特征,引导网络学习到阴影区域。同时,该网络还应用了特征融合模块来融合粗略的语义信息和自上而下路径中的精细特征,并且加入了后处理,进一步优化网络的预测结果。本文在两个公开的阴影检测基准数据集上进行了实验来评估其网络性能。实验表明,本文的方法能够更准确地检测到阴影,和过去最先进的方法相比也表现出色,在SBU数据集上正确率达到了96.6%,平衡检测错误因子为6.22。      

Detection of Abnormal Activities from Various Signals Based on Statistical Analysis

We perceive big data with massive datasets of complex and variegated structures in the modern era. Such attributes formulate hindrances while analyzing and storing the data to generate apt aftermaths. Privacy and security are the colossal perturb in the domain space of extensive data analysis. In this paper, our foremost priority is the computing technologies that focus on big data, IoT (Internet of Things), Cloud Computing, Blockchain, and fog computing. Among these, Cloud Computing follows the role of providing on-demand services to their customers by optimizing the cost factor. AWS, Azure, Google Cloud are the major cloud providers today. Fog computing offers new insights into the extension of cloud computing systems by procuring services to the edges of the network. In collaboration with multiple technologies, the Internet of Things takes this into effect, which solves the labyrinth of dealing with advanced services considering its significance in varied application domains. The Blockchain is a dataset that entertains many applications ranging from the fields of crypto-currency to smart contracts. The prospect of this research paper is to present the critical analysis and review it under the umbrella of existing extensive data systems. In this paper, we attend to critics' reviews and address the existing threats to the security of extensive data systems. Moreover, we scrutinize the security attacks on computing systems based upon Cloud, Blockchain, IoT, and fog. This paper lucidly illustrates the different threat behaviour and their impacts on complementary computational technologies. The authors have mooted a precise analysis of cloud-based technologies and discussed their defense mechanism and the security issues of mobile healthcare.

     

Task offloading based on deep learning for blockchain in mobile edge computing

Blockchain is an advanced technique to realize smart contracts, various transactions, and P2P crypto-currencies in the e-commerce society. However, the traditional blockchain does not consider a mobile environment to design a data offloading of the blockchain such that the blockchain results in high computational cost and huge data propagation delay. In this paper, to remedy the above problem, we propose a scalable blockchain and a task offloading technique based on the neural network of the mobile edge computing scenario. Experimental results show that our approach is very scalable in the mobile scenario.

     

Robust smart‐card‐based remote user password authentication scheme

Smart‐card‐based remote user password authentication schemes are commonly used for providing authorized users a secure method for remotely accessing resources over insecure networks. In 2009, Xu et al. proposed a smart‐card‐based password authentication scheme. They claimed their scheme can withstand attacks when the information stored on the smart card is disclosed. Recently, Sood et al. and Song discovered that the smart‐card‐based password authentication scheme of Xu et al. is vulnerable to impersonation and internal attacks. They then proposed their respective improved schemes. However, we found that there are still flaws in their schemes: the scheme of Sood et al. does not achieve mutual authentication and the secret key in the login phase of Song's scheme is permanent and thus vulnerable to stolen‐smart‐card and off‐line guessing attacks. In this paper, we will propose an improved and efficient smart‐card‐based password authentication and key agreement scheme. According to our analysis, the proposed scheme not only maintains the original secret requirement but also achieves mutual authentication and withstands the stolen‐smart‐card attack. Copyright © 2012 John Wiley & Sons, Ltd.     

Smart contract applications within blockchain technology: A systematic mapping study

With the advent of blockchain, smart contracts have become one of the most sought-after technologies because of the high customisability they add to transactions. This has given rise to many smart contract applications in areas ranging from financial services, life sciences and healthcare to energy resources and voting. However, due to their infancy, smart contracts still pose many challenges that encumber the stakeholders who interact with them: users, developers and the organisations that are built on top of smart contracts. This study aims to contribute to the body of knowledge of smart contracts within blockchain technology. Based on a systematic mapping study, we offer a broad perspective on their problems and corresponding solutions, present the research trends within the area and compile the 64 papers identified, grouped by top publication sources, channels, methods and approaches. We conclude that, since 2016, there has been an increasing trend towards the publication of blockchain-based smart contract articles at conferences and journals, mainly reflecting experiments and presenting methods, tools and models. According to the results, the most commonly discussed problems and solutions in the literature are related to the security, privacy and scalability of blockchain and the programmability of smart contracts.     

Blockchain-inspired lightweight trust-based system in vehicular networks

A decentralized application runs on the blockchain network without the intervention of a central authority. Transparency in transactions and security in vehicular networks are the issues for central systems. The proposed system uses blockchain-based smart contracts, which eliminate the requirement for any third-party verification. Additionally, with signature verification and reduced overhead, smart contracts also help in a fast and secure transaction. This study suggests a trust-based system paradigm where certificate authority (CA) is employed for vehicle registration. We also propose a blockchain-based system that provides efficient two-way authentication and key agreement through encryption and digital signatures. The analysis of the proposed model reveals that it is an efficient way of establishing distributed trust management, which helps in preserving vehicle privacy. The proposed scheme is tested in Automated Validation of Internet Security-sensitive Protocols (AVISPA), and security parameters verification in Network Simulator 2(NS2) also shows that the proposed scheme is more effective in comparison with existing schemes in terms of authentication cost, storage cost, and overhead.     

一种基于深度学习的强对抗性Android恶意代码检测方法

针对现有Android恶意代码检测方法容易被绕过的问题,提出了一种强对抗性的Android恶意代码检测方法.首先设计实现了动静态分析相结合的移动应用行为分析方法,该方法能够破除多种反分析技术的干扰,稳定可靠地提取移动应用的权限信息、防护信息和行为信息.然后,从上述信息中提取出能够抵御模拟攻击的能力特征和行为特征,并利用一个基于长短时记忆网络（Long Short-Term Memory,LSTM）的神经网络模型实现恶意代码检测.最后通过实验证明了本文所提出方法的可靠性和先进性.     

基于智能手表运动传感器的新型攻击及其防范

智能手表上搭载了加速度计、陀螺仪等运动传感器,且会随着手部运动而产生位移,使通过智能手表窃取用户外部键盘输入成为了可能。研究了智能设备上基于6轴加速度计/陀螺仪来推断用户键盘输入的可行性,建立了用户进行键盘输入时的运动模型来对手部运动进行分类识别。当用户执行敏感输入,如在数字键盘上输入密码时,可根据传感器信息推断出密码。经实验验证,该方法可有效识别4位PIN码,证明此安全问题值得人们重视。     

基于fastText的可视化作者归属模型

基于滑动窗口的方法,结合机器学习分类技术,可以判定文本的作者归属。但是此类方法需要精心挑选对应的文本特征,不同的文本特征选取可能会影响判定结果。针对以上问题,提出了一种基于快速文本分类(fastText)的文本作者归属判定模型。该模型融合滑动窗口的思想,引入词(字)向量、数据增强技术,从而充分利用文本信息、自动提取文本特征,并且以可视化的方式将结果呈现出来。使用该模型来检测《红楼梦》、《Roman de la Rose》的作者归属,实验结果表明《红楼梦》的前八十回与后四十回为不同作者所著、《Roman de la Rose》开篇4 058行(约50 000字)与后面17 724行(约218 000字)为不同作者所著。证明了Rolling-fastText模型判定文本作者归属的有效性。     

Wireless LAN Access Point Placement Based on User Mobility

In a wide area campus, a university provides Wireless Local Area Network (WLAN) for users to connect to the Internet. Most users take advantage of this WLAN benefit by using their laptops. However, the number of smart phone users is growing fast. Since a smart phone is able to get an Internet connection using WLAN, users can use their smart phones without having to pay for a cellular operator. Users tend to use their smart phones more, due to their higher mobility compared to a laptop. This capability enables new services in the market, such as Fixed-Mobile Convergence (FMC), which integrates a fixed network (traditional telephony, WLAN) and a mobile network (cellular) to provide seamless voice communications anytime, anywhere. These new applications require a WLAN connection availability nearly everywhere. However, due to limited budgets, a university can only install APs in places with a high connection demand. We propose a novel WLAN AP placement technique that takes user mobility into consideration. This new approach is more complete than previous approaches, which mainly focus on coverage area and throughput data. Our technique has been implemented in our university. The results show the suitability of the WLAN access point locations in our university campus based on user mobility and activities.