A Review on the Security of the Ethereum-Based DeFi Ecosystem

Decentralized finance(DeFi)is a general term for a series of financial products and services.It is based on blockchain technology and has attracted people’s attention because of its open,transparent,and intermediary free.Among them,the DeFi ecosystem based on Ethereum-based blockchains attracts the most attention.However,the current decentralized financial system built on the Ethereum architecture has been exposed to many smart contract vulnerabilities during the last few years.Herein,we believe it is time to improve the understanding of the prevailing Ethereum-based DeFi ecosystem security issues.To that end,we investigate the Ethereum-based DeFi security issues:1)inherited from the real-world financial system,which can be solved by macro-control;2)induced by the problems of blockchain architecture,which require a better blockchain platform;3)caused by DeFi invented applications,which should be focused on during the project development.Based on that,we further discuss the current solutions and potential directions ofDeFi security.According to our research,we could provide a comprehensive vision to the research community for the improvement of Ethereum-basedDeFi ecosystem security.

Analysis on unit maximum capacity of orthogonal multiple watermarking for multimedia signals in B5G wireless communications

Beyond-5G(B5G)aims to meet the growing demands of mobile traffic and expand the communication space.Considering that intelligent applications to B5G wireless communications will involve security issues regarding user data and operational data,this paper analyzes the maximum capacity of the multi-watermarking method for multimedia signal hiding as a means of alleviating the information security problem of B5G.The multiwatermarking process employs spread transform dither modulation.During the watermarking procedure,Gram-Schmidt orthogonalization is used to obtain the multiple spreading vectors.Consequently,multiple watermarks can be simultaneously embedded into the same position of a multimedia signal.Moreover,the multiple watermarks can be extracted without affecting one another during the extraction process.We analyze the effect of the size of the spreading vector on the unit maximum capacity,and consequently derive the theoretical relationship between the size of the spreading vector and the unit maximum capacity.A number of experiments are conducted to determine the optimal parameter values for maximum robustness on the premise of high capacity and good imperceptibility.

A Bitcoin Address Multi-Classification Mechanism Based on Bipartite Graph-Based Maximization Consensus

Bitcoin is widely used as the most classic electronic currency for various electronic services such as exchanges,gambling,marketplaces,and also scams such as high-yield investment projects.Identifying the services operated by a Bitcoin address can help determine the risk level of that address and build an alert model accordingly.Feature engineering can also be used to flesh out labeled addresses and to analyze the current state of Bitcoin in a small way.In this paper,we address the problem of identifying multiple classes of Bitcoin services,and for the poor classification of individual addresses that do not have significant features,we propose a Bitcoin address identification scheme based on joint multi-model prediction using the mapping relationship between addresses and entities.The innovation of the method is to(1)Extract as many valuable features as possible when an address is given to facilitate the multi-class service identification task.(2)Unlike the general supervised model approach,this paper proposes a joint prediction scheme for multiple learners based on address-entity mapping relationships.Specifically,after obtaining the overall features,the address classification and entity clustering tasks are performed separately,and the results are subjected to graph-basedmaximization consensus.The final result ismade to baseline the individual address classification results while satisfying the constraint of having similarly behaving entities as far as possible.By testing and evaluating over 26,000 Bitcoin addresses,our feature extraction method captures more useful features.In addition,the combined multi-learner model obtained results that exceeded the baseline classifier reaching an accuracy of 77.4%.

Ada-FFL:Adaptive computing fairness federated learning

As the scale of federated learning expands,solving the Non-IID data problem of federated learning has become a key challenge of interest.Most existing solutions generally aim to solve the overall performance improvement of all clients;however,the overall performance improvement often sacrifices the performance of certain clients,such as clients with less data.Ignoring fairness may greatly reduce the willingness of some clients to participate in federated learning.In order to solve the above problem,the authors propose Ada-FFL,an adaptive fairness federated aggregation learning algorithm,which can dynamically adjust the fairness coefficient according to the update of the local models,ensuring the convergence performance of the global model and the fairness between federated learning clients.By integrating coarse-grained and fine-grained equity solutions,the authors evaluate the deviation of local models by considering both global equity and individual equity,then the weight ratio will be dynamically allocated for each client based on the evaluated deviation value,which can ensure that the update differences of local models are fully considered in each round of training.Finally,by combining a regularisation term to limit the local model update to be closer to the global model,the sensitivity of the model to input perturbations can be reduced,and the generalisation ability of the global model can be improved.Through numerous experiments on several federal data sets,the authors show that our method has more advantages in convergence effect and fairness than the existing baselines.

Development strategies and improved photocatalytic CO_(2) reduction performance of metal halide perovskite nanocrystals

In recent years,photocatalytic CO_(2)reduction reaction(CRR) has attracted much scientific attention to overcome energy and environmental issues by converting CO_(2)into high-value-added chemicals utilizing solar energy.Metal halide perovskite(MHP) nanocrystals(NCs) are recognized as an ideal choice for CRR owing to their outstanding optoelectronic properties.Although great efforts have been devoted to designing more effective photocatalysts to optimize CRR performance,severe charge recombination,instability,and unsatisfactory activity have become major bottlenecks in developing perovskite-based photocatalysts.In this review,we mainly focus on the recent research progress in the areas of relevance.First,a brief insight into reaction mechanisms for CRR and structural features of MHPs are introduced.Second,efficient modification approaches for the improvement of the photocatalytic activity and stability of the perovskite-based catalysts are comprehensively reviewed.Third,the state-of-the-art achievements of perovskite-based photocatalysts for CRR are systematically summarized and discussed,which are focused on the modification approaches,structure design,and the mechanism of the CO_(2)reduction process.Lastly,the current challenges and future research perspectives in the design and application of perovskite materials are highlighted from our point of view to provide helpful insights for seeking breakthroughs in the field of CRR.This review may provide a guide for scientists interested in applying perovskite-based catalysts for solar-to-chemical energy conversion.

Mo/Fe bimetallic pyrophosphates derived from Prussian blue analogues for rapid electrocatalytic oxygen evolution

Efficient and stable oxygen evolution electrocatalysts are indispensable for industrial applications of water splitting and hydrogen production.Herein,a simple and practical method was applied to fabricate(Mo,Fe)P2O7@NF electrocatalyst by directly growing Mo/Fe bimetallic pyrophosphate derived from Prussian blue analogues on three-dimensional porous current collector.In alkaline media,the developed material possesses good hydrophilic features and exhibits best-in-class oxygen evolution reaction(OER)performances.Surprisingly,the(Mo,Fe)P_(2)O_(7)@NF only requires overpotentials of 250 and 290 mV to deliver 100 and 600 mA cm^(-2)in 1 mol L^(-1)KOH,respectively.Furthermore,the(Mo,Fe)P_(2)O_(7)@NF shows outstanding performances in alkaline salty water and 1 mol L^(-1)high purity KOH.A worthwhile pathway is provided to combine bimetallic pyrophosphate with commercial Ni foam to form robust electrocatalysts for stable electrocatalytic OER,which has a positive impact on both hydrogen energy application and environmental restoration.

Introduction to the Special Issue on Blockchain Security

Motivated by the fast evolvement of blockchain technology,cloud-edge platforms,intelligent transportation systems,smart grid,vehicular networks,location based services,and other IoT applications have achieved significant breakthrough during recent years.Nowadays,blockchain based researches and projects are super-hot topics and focuses for both research and industrial communities.However,most of the current blockchain projects still suffer from insufficient security concerns.The defects of the underlying protocol make the node communication vulnerable to be hijacked,and further exacerbate the fork problem;smart contracts can hardly be fully tested before deployment because of the evolving blockchain platforms,while the smart contracts updating is impossible(or very complicated);current data privacy protection techniques are either inefficient or inaccurate;sharing and cross-chain schemes brought new security problems together with its TPS promotion.The intrinsic security vulnerabilities make the current blockchain based systems and architectures prone to be assaulted,and further do harm to the confidence of the investment on the blockchain industrial.Worse still,blockchain turns out to play a critical role in a lot of existing security solutions,which would be useless if the blockchain is insecure.

System Architecture and Key Technologies of Network Security Situation Awareness System YHSAS

Network Security Situation Awareness System YHSAS acquires,understands and displays the security factors which cause changes of network situation,and predicts the future development trend of these security factors.YHSAS is developed for national backbone network,large network operators,large enterprises and other large-scale network.This paper describes its architecture and key technologies:Network Security Oriented Total Factor Information Collection and High-Dimensional Vector Space Analysis,Knowledge Representation and Management of Super Large-Scale Network Security,Multi-Level,Multi-Granularity and Multi-Dimensional Network Security Index Construction Method,Multi-Mode and Multi-Granularity Network Security Situation Prediction Technology,and so on.The performance tests show that YHSAS has high real-time performance and accuracy in security situation analysis and trend prediction.The system meets the demands of analysis and prediction for large-scale network security situation.

Structural engineering of cathodes for improved Zn-ion batteries

Aqueous zinc-ion batteries(ZIBs) are attracting considerable attention because of their low cost,high safety and abundant anode material resources.However,the major challenge faced by aqueous ZIBs is the lack of stable and high capacity cathode materials due to their complicated reaction mechanism and slow Zn-ion transport kinetics.This study reports a unique 3 D ’flower-like’ zinc cobaltite(ZnCo_(2)O_(4-x)) with enriched oxygen vacancies as a new cathode material for aqueous ZIBs.Computational calculations reveal that the presence of oxygen vacancies significantly enhances the electronic conductivity and accelerates Zn^(2+) diffusion by providing enlarged channels.The as-fabricated batteries present an impressive specific capacity of 148.3 mAh g^(-1) at the current density of 0.05 A g^(-1),high energy(2.8 Wh kg^(-1)) and power densities(27.2 W kg^(-1)) based on the whole device,which outperform most of the reported aqueous ZIBs.Moreover,a flexible solid-state pouch cell was demonstrated,which delivers an extremely stable capacity under bending states.This work demonstrates that the performance of Zn-ion storage can be effectively enhanced by tailoring the atomic structure of cathode materials,guiding the development of low-cost and eco-friendly energy storage materials.

An Intrusion Detection Algorithm Based on Feature Graph

With the development of Information technology and the popularization of Internet,whenever and wherever possible,people can connect to the Internet optionally.Meanwhile,the security of network traffic is threatened by various of online malicious behaviors.The aim of an intrusion detection system(IDS)is to detect the network behaviors which are diverse and malicious.Since a conventional firewall cannot detect most of the malicious behaviors,such as malicious network traffic or computer abuse,some advanced learning methods are introduced and integrated with intrusion detection approaches in order to improve the performance of detection approaches.However,there are very few related studies focusing on both the effective detection for attacks and the representation for malicious behaviors with graph.In this paper,a novel intrusion detection approach IDBFG(Intrusion Detection Based on Feature Graph)is proposed which first filters normal connections with grid partitions,and then records the patterns of various attacks with a novel graph structure,and the behaviors in accordance with the patterns in graph are detected as intrusion behaviors.The experimental results on KDD-Cup 99 dataset show that IDBFG performs better than SVM(Supprot Vector Machines)and Decision Tree which are trained and tested in original feature space in terms of detection rates,false alarm rates and run time.

Adversarial Attacks on Content-Based Filtering Journal Recommender Systems

Recommender systems are very useful for people to explore what they really need.Academic papers are important achievements for researchers and they often have a great deal of choice to submit their papers.In order to improve the efficiency of selecting the most suitable journals for publishing their works,journal recommender systems(JRS)can automatically provide a small number of candidate journals based on key information such as the title and the abstract.However,users or journal owners may attack the system for their own purposes.In this paper,we discuss about the adversarial attacks against content-based filtering JRS.We propose both targeted attack method that makes some target journals appear more often in the system and non-targeted attack method that makes the system provide incorrect recommendations.We also conduct extensive experiments to validate the proposed methods.We hope this paper could help improve JRS by realizing the existence of such adversarial attacks.

Multi-Candidate Voting Model Based on Blockchain

Electronic voting has partially solved the problems of poor anonymity and low efficiency associated with traditional voting.However,the difficulties it introduces into the supervision of the vote counting,as well as its need for a concurrent guaranteed trusted third party,should not be overlooked.With the advent of blockchain technology in recent years,its features such as decentralization,anonymity,and non-tampering have made it a good candidate in solving the problems that electronic voting faces.In this study,we propose a multi-candidate voting model based on the blockchain technology.With the introduction of an asymmetric encryption and an anonymity-preserving voting algorithm,votes can be counted without relying on a third party,and the voting results can be displayed in real time in a manner that satisfies various levels of voting security and privacy requirements.Experimental results show that the proposed model solves the aforementioned problems of electronic voting without significant negative impact from an increasing number of voters or candidates.

A Convolution-Based System for Malicious URLs Detection

Since the web service is essential in daily lives,cyber security becomes more and more important in this digital world.Malicious Uniform Resource Locator(URL)is a common and serious threat to cybersecurity.It hosts unsolicited content and lure unsuspecting users to become victim of scams,such as theft of private information,monetary loss,and malware installation.Thus,it is imperative to detect such threats.However,traditional approaches for malicious URLs detection that based on the blacklists are easy to be bypassed and lack the ability to detect newly generated malicious URLs.In this paper,we propose a novel malicious URL detection method based on deep learning model to protect against web attacks.Specifically,we firstly use auto-encoder to represent URLs.Then,the represented URLs will be input into a proposed composite neural network for detection.In order to evaluate the proposed system,we made extensive experiments on HTTP CSIC2010 dataset and a dataset we collected,and the experimental results show the effectiveness of the proposed approach.

A Novel Search Engine for Internet of Everything Based on Dynamic Prediction

In recent years, with the rapid development of sensing technology and deployment of various Internet of Everything devices, it becomes a crucial and practical challenge to enable real-time search queries for objects, data, and services in the Internet of Everything. Moreover, such efficient query processing techniques can provide strong facilitate the research on Internet of Everything security issues. By looking into the unique characteristics in the IoE application environment, such as high heterogeneity, high dynamics, and distributed, we develop a novel search engine model, and build a dynamic prediction model of the IoE sensor time series to meet the real-time requirements for the Internet of Everything search environment. We validated the accuracy and effectiveness of the dynamic prediction model using a public sensor dataset from Intel Lab.

Location Privacy in Device-Dependent Location-Based Services:Challenges and Solution

With the evolution of location-based services(LBS),a new type of LBS has already gain a lot of attention and implementation,we name this kind of LBS as the Device-Dependent LBS(DLBS).In DLBS,the service provider(SP)will not only send the information according to the user’s location,more significant,he also provides a service device which will be carried by the user.DLBS has been successfully practised in some of the large cities around the world,for example,the shared bicycle in Beijing and London.In this paper,we,for the first time,blow the whistle of the new location privacy challenges caused by DLBS,since the service device is enabled to perform the localization without the permission of the user.To conquer these threats,we design a service architecture along with a credit system between DLBS provider and the user.The credit system tie together the DLBS device usability with the curious behaviour upon user’s location privacy,DLBS provider has to sacrifice their revenue in order to gain extra location information of their device.We make the simulation of our proposed scheme and the result convince its effectiveness.

Evaluating the Topology Coverage of BGP Monitors

BGP monitors are currently the main data resource of AS-level topology measurement,and the integrity of measurement result is limited to the location of such BGP monitors.However,there is currently no work to conduct a comprehensive study of the range of measurement results for a single BGP monitor.In this paper,we take the first step to describe the observed topology of each BGP monitor.To that end,we first investigate the construction and theoretical up-limit of the measured topology of a BGP monitor based on the valley-free model,then we evaluate the individual parts of the measured topology by comparing such theoretical results with the actually observed data.We find that:1)for more than 90%of the monitors,the actually observed peer-peer links merely takes a small part of all theoretical visible links;2)increasing the BGP monitors in the same AS may improve the measurement result,but with limited improvement;and 3)deploying multiple BGP monitors in different ASs can significantly improve the measurement results,but non-local BGP monitors can hardly replace the local AS BGP monitors.We also propose a metric for monitor selection optimization,and prove its effectiveness with experiment evaluation.

Effects of oxygen potential and flux composition on dephosphorization and rephosphorization of molten steel

The dephosphorization experiments of low phosphorus containing steel by CaO-based and BaO-based fluxes were carried out. The effects of the oxygen potential in molten steel and the BaO content in the slag on dephosphorization and rephosphorization of molten steel were analyzed. The results showed that the dephosphorization ratio of more than 50% and the ultra-low phosphorus content of less than 0.005% in steel were obtained by the three kinds of dephosphorization fluxes as the oxygen potential of molten steel higher than 400×10^-6. Rephosphorization of molten steel was serious as the oxygen content of molten steel lower than 10×10^-6. BaO-based fluxes can improve the dephosphorization effect and reduce the phosphorus pick-up effectively under the condition of weak deoxidization of molten steel （the oxygen potential is about 100×10^-6）, but can not prevent rephosphorization under the condition of deep deoxidization of molten steel （the oxygen potential less than 10×10^-6）.

DPIF:A Framework for Distinguishing Unintentional Quality Problems From Potential Shilling Attacks

Maliciously manufactured user profiles are often generated in batch for shilling attacks.These profiles may bring in a lot of quality problems but not worthy to be repaired.Since repairing data always be expensive,we need to scrutinize the data and pick out the data that really deserves to be repaired.In this paper,we focus on how to distinguish the unintentional data quality problems from the batch generated fake users for shilling attacks.A two-steps framework named DPIF is proposed for the distinguishment.Based on the framework,the metrics of homology and suspicious degree are proposed.The homology can be used to represent both the similarities of text and the data quality problems contained by different profiles.The suspicious degree can be used to identify potential attacks.The experiments on real-life data verified that the proposed framework and the corresponding metrics are effective.

Empirical Studies of the Factors Influencing Farmers' Enthusiasm for Growing Grain

To ensure the future food self-sufficiency in China,it is necessary to mobilize producers' enthusiasm for growing grain. Theoretically,it is mainly influenced by economic interests,land scale,farmers' characteristics and agricultural support policy. Through field research of farmers,we use model to explore these influencing factors. The results show that the dwindling arable land making grain cultivation fail to form economies of scale is a key factor restricting farmers' enthusiasm for growing grain; farmers' age and the share of agricultural labor in the family are important factors restricting their grain growing; agricultural support policy can stimulate the enthusiasm for growing grain to a certain extent. Therefore,there is a need to promote the concentration of land and improve various agricultural support policies,in order to improve the economic benefits of growing grain and encourage qualified young workers to engage in grain production activities.

Improved Dota2 Lineup Recommendation Model Based on a Bidirectional LSTM

In recent years,e-sports has rapidly developed,and the industry has produced large amounts of data with specifications,and these data are easily to be obtained.Due to the above characteristics,data mining and deep learning methods can be used to guide players and develop appropriate strategies to win games.As one of the world’s most famous e-sports events,Dota2 has a large audience base and a good game system.A victory in a game is often associated with a hero’s match,and players are often unable to pick the best lineup to compete.To solve this problem,in this paper,we present an improved bidirectional Long Short-Term Memory(LSTM)neural network model for Dota2 lineup recommendations.The model uses the Continuous Bag Of Words(CBOW)model in the Word2 vec model to generate hero vectors.The CBOW model can predict the context of a word in a sentence.Accordingly,a word is transformed into a hero,a sentence into a lineup,and a word vector into a hero vector,the model applied in this article recommends the last hero according to the first four heroes selected first,thereby solving a series of recommendation problems.