Chaotic CS Encryption:An Efficient Image Encryption Algorithm Based on Chebyshev Chaotic System and Compressive Sensing

Images are the most important carrier of human information. Moreover, how to safely transmit digital imagesthrough public channels has become an urgent problem. In this paper, we propose a novel image encryptionalgorithm, called chaotic compressive sensing (CS) encryption (CCSE), which can not only improve the efficiencyof image transmission but also introduce the high security of the chaotic system. Specifically, the proposed CCSEcan fully leverage the advantages of the Chebyshev chaotic system and CS, enabling it to withstand various attacks,such as differential attacks, and exhibit robustness. First, we use a sparse trans-form to sparse the plaintext imageand then use theArnold transformto perturb the image pixels. After that,we elaborate aChebyshev Toeplitz chaoticsensing matrix for CCSE. By using this Toeplitz matrix, the perturbed image is compressed and sampled to reducethe transmission bandwidth and the amount of data. Finally, a bilateral diffusion operator and a chaotic encryptionoperator are used to perturb and expand the image pixels to change the pixel position and value of the compressedimage, and ultimately obtain an encrypted image. Experimental results show that our method can be resistant tovarious attacks, such as the statistical attack and noise attack, and can outperform its current competitors.

A Secure Interaction and Control Method for InterClouds Services

Inter-clouds services build information bridges over computing clouds so as to provide the convenience for electronic commerce and other information services. However, the interactions between clouds have challenges in information disclosure, services spoofing and information inconsistent. This paper proposes a secure interaction and control method for inter-clouds services, which uses information encryption, digital signature,time logic control means, to protect users' privacy and keep the integrity and consistency of cloud customers' request information, and prevent the spoofing of information requests. The results show that the method could prevent the spoofing well in a stable network environment and improve the security of clouds sessions.

An Effective Cloud Workflow Scheduling Approach Combining PSO and Idle Time Slot-Aware Rules

Workflow scheduling is a key issue and remains a challenging problem in cloud computing.Faced with the large number of virtual machine(VM)types offered by cloud providers,cloud users need to choose the most appropriate VM type for each task.Multiple task scheduling sequences exist in a workflow application.Different task scheduling sequences have a significant impact on the scheduling performance.It is not easy to determine the most appropriate set of VM types for tasks and the best task scheduling sequence.Besides,the idle time slots on VM instances should be used fully to increase resources'utilization and save the execution cost of a workflow.This paper considers these three aspects simultaneously and proposes a cloud workflow scheduling approach which combines particle swarm optimization(PSO)and idle time slot-aware rules,to minimize the execution cost of a workflow application under a deadline constraint.A new particle encoding is devised to represent the VM type required by each task and the scheduling sequence of tasks.An idle time slot-aware decoding procedure is proposed to decode a particle into a scheduling solution.To handle tasks'invalid priorities caused by the randomness of PSO,a repair method is used to repair those priorities to produce valid task scheduling sequences.The proposed approach is compared with state-of-the-art cloud workflow scheduling algorithms.Experiments show that the proposed approach outperforms the comparative algorithms in terms of both of the execution cost and the success rate in meeting the deadline.

DIV： Dynamic Integrity Validation Framework for Detecting Compromises on Virtual Machine Based Cloud Services in Real Time

with the increasing popularity of cloud services,attacks on the cloud infrastructure also increase dramatically.Especially,how to monitor the integrity of cloud execution environments is still a difficult task.In this paper,a real-time dynamic integrity validation(DIV) framework is proposed to monitor the integrity of virtual machine based execution environments in the cloud.DIV can detect the integrity of the whole architecture stack from the cloud servers up to the VM OS by extending the current trusted chain into virtual machine's architecture stack.DIV introduces a trusted third party(TTP) to collect the integrity information and detect remotely the integrity violations on VMs periodically to avoid the heavy involvement of cloud tenants and unnecessary information leakage of the cloud providers.To evaluate the effectiveness and efficiency of DIV framework,a prototype on KVM/QEMU is implemented,and extensive analysis and experimental evaluation are performed.Experimental results show that the DIV can efficiently validate the integrity of files and loaded programs in real-time,with minor performance overhead.

A User-Centric Approach to Activity Recognition and Guidance in Semantic Smart Home

Wireless smart home system is to facilitate people's lives and it trend to adopt a more intelligent way to provide services. It is very desirable in the recent SH market for the system to recognize users' behaviors and automatically response the corresponding activities to satisfy users' actual demands. However, activity models in the existing approaches are usually defined separately through knowledge-driven methods. These approaches cause that the activity models can't be matched with the services dynamically. To address the problem, we develop the semantic association model and a novel approach of activity recognition and guidance is presented. In our approach, the smart devices and users' requirements are described by semantic models. When the requirements are detected and understood, smart gateway can provide appropriate services, achieving activity assistance. The semantic association model allows all related elements in smart home connect with each other logically. The approach has been implemented and the results show that the success rate of the approach based on semantic association model is higher than 33% at average as compared to the approach based on predefined models. The proposed approach can effectively help people who are in trouble with learning or remembering in the common life.

The Construction and Application of Work and Production Resuming Behavior System Dynamics Evolutionary Game Regulation and Control Model

The resumption of production after the“suspension”caused by the COVID-19 has emerged as an urgent problem for many enterprises and the government.The resumption of production is actually a dynamic evolution problem from 0 to 1(100%).This paper constructs a general game model and a dynamic replication system for the resumption of production and government support,and gives theorems for the construction of the model.It analyzes the evolution mechanism and scenario conditions for the convergence of enterprise strategies to the“resumption of production”strategy,takes the resumption of production of hog farmers as an example to carry out a study on the regulation of countermeasures to resume hog production,and explores systemic countermeasures and suggestions for the rapid convergence of farmers’strategies to the“resumption of work and production”strategy.The study found that the production resuming behavior system dynamics evolution game regulation model provides a systematic model and method for the study of resumption countermeasures,a general regulation model for the resumption ratio from 0 to 1(100%),and a systematic idea,method and model for exploring the“precise strategy”system to promote the rapid resumption of production.

Research on the Electric Energy Metering Data Sharing Method in Smart Grid Based on Blockchain

Enabling data sharing among smart grid power suppliers is a pressing challenge due to technical hurdles in verifying,storing,and synchronizing energy metering data.Access and sharing limitations are stringent for users,power companies,and researchers,demanding significant resources and time for permissions and verification.This paper proposes a blockchain-based architecture for secure and efficient sharing of electric energy metering data.Further,we propose a data sharing model based on evolutionary game theory.Based on the Lyapunov stability theory,the model’s evolutionary stable strategy(ESS)is analyzed.Numerical results verify the correctness and practicability of the scheme proposed in this paper,and provide a new method for realizing convenient,safe and fast data sharing.

Comprehensive Information Security Evaluation Model Based on Multi-Level Decomposition Feedback for IoT

The development of the Internet of Things(IoT)calls for a comprehensive in-formation security evaluation framework to quantitatively measure the safety score and risk(S&R)value of the network urgently.In this paper,we summarize the architecture and vulnerability in IoT and propose a comprehensive information security evaluation model based on multi-level decomposition feedback.The evaluation model provides an idea for information security evaluation of IoT and guides the security decision maker for dynamic protection.Firstly,we establish an overall evaluation indicator system that includes four primary indicators of threat information,asset,vulnerability,and management,respectively.It also includes eleven secondary indicators of system protection rate,attack detection rate,confidentiality,availability,controllability,identifiability,number of vulnerabilities,vulnerability hazard level,staff organization,enterprise grading and service continuity,respectively.Then,we build the core algorithm to enable the evaluation model,wherein a novel weighting technique is developed and a quantitative method is proposed to measure the S&R value.Moreover,in order to better supervise the performance of the proposed evaluation model,we present four novel indicators includes residual risk,continuous conformity of residual risk,head-to-tail consistency and decrease ratio,respectively.Simulation results show the advantages of the proposed model in the evaluation of information security for IoT.

Lightweight Mutual Authentication Scheme for Protecting Identity in Insecure Environment

Many improved authentication solutions were put forward, on purpose of authenticating more quickly and securely.However, neither the overuse of hash function,or additional symmetric encryption, can truly increase the overall security. Instead,extra computation cost degraded the performance.They were still vulnerable to a variety of threats, such as smart card loss attack and impersonation attack, due to hidden loopholes and flaws. Even worse, user's identity can be parsed in insecure environment, even became traceable. Aiming to protect identity, a lightweight mutual authentication scheme is proposed. Redundant operations are removed,which make the verification process more explicit. It gains better performance with average cost compared to other similar schemes.Cryptanalysis shows the proposed scheme can resist common attacks and achieve user anonymity.Formal security is further verified by using the widely accepted Automated Validation of Internet Security Protocols and Applications(AVISPA) tool.

An Adversarial Smart Contract Honeypot in Ethereum

A smart contract honeypot is a special type of smart contract.This type of contract seems to have obvious vulnerabilities in contract design.If a user transfers a certain amount of funds to the contract,then the user can withdraw the funds in the contract.However,once users try to take advantage of this seemingly obvious vulnerability,they will fall into a real trap.Consequently,the user’s investment in the contract cannot be retrieved.The honeypot induces other accounts to launch funds,which seriously threatens the security of property on the blockchain.Detection methods for honeypots are available.However,studying the manner by which to defend existing honeypots is insufficient to fight against honeypots.The new honeypots that may appear in the future from the perspective of an attacker must also be predicted.Therefore,we propose a type of adversarial honeypot.The code and behavioral features of honeypots are obtained through a comparative analysis of the 158,568 nonhoneypots and 352 honeypots.To build an adversarial honeypot,we try to separately hide these features and make the honeypot bypass the existing detection technology.We construct 18 instances on the basis of the proposed adversarial honeypot and use an open-source honeypot detection tool to detect these instances.The experimental result shows that the proposed honeypot can bypass the detection tool with a 100%ratio.Therefore,this type of honeypot should be given attention,and defensive measures should be proposed as soon as possible.

Network Traffic Generation Based on Statistical Packet-Level Characteristics

Network traffic is very important for testing network equipment, network services, and security products. A new method of generating traffic based on statistical packet-level characteristics is proposed. In every time unit, the generator determines the sent packets number, the type and size of every sent packet according to the statistical characteristics of the original traffic. Then every packet, in which the protocol headers of transport layer, network layer and ethernet layer are encapsulated, is sent via the responding network interface card in the time unit. The results in the experiment show that the correlation coefficients between the bandwidth, the packet number, packet size distribution, the fragment number of the generated network traffic and those of the original traffic are all more than 0.96. The generated traffic and original traffic are very highly related and similar.

Security-Critical Components Recognition Algorithm for Complex Heterogeneous Information Systems

With the skyrocketing development of technologies,there are many issues in information security quantitative evaluation(ISQE)of complex heterogeneous information systems(CHISs).The development of CHIS calls for an ISQE model based on security-critical components to improve the efficiency of system security evaluation urgently.In this paper,we summarize the implication of critical components in different filed and propose a recognition algorithm of security-critical components based on threat attack tree to support the ISQE process.The evaluation model establishes a framework for ISQE of CHISs that are updated iteratively.Firstly,with the support of asset identification and topology data,we sort the security importance of each asset based on the threat attack tree and obtain the security-critical components(set)of the CHIS.Then,we build the evaluation indicator tree of the evaluation target and propose an ISQE algorithm based on the coefficient of variation to calculate the security quality value of the CHIS.Moreover,we present a novel indicator measurement uncertainty aiming to better supervise the performance of the proposed model.Simulation results show the advantages of the proposed algorithm in the evaluation of CHISs.

Research on Staged Practice Teaching System Based on Systems Engineering

The practical teaching management system is an important factor to ensure the quality of teaching.The training of systematic thinking needs a systematic teaching system,and a systematic process is a gradual process and a standardized process.This paper discusses the process of establishing a standardized practice teaching system step by step through a progressive way.At the same time,taking test practice as an example,it describes how the practice teaching system enables students to gradually adapt to and benefit from the management of the system.Through project practice,students not only enhance their practical ability,but also learn the standardized practice process.These skills can help students adapt to the work pace of the enterprise faster,and make students’practical ability meet the requirements of the enterprise.

A survey of automated International Classification of Diseases coding:development,challenges,and applications

The International Classification of Diseases(ICD)is an international standard and tool for epidemiological in-vestigation,health management,and clinical diagnosis with a fundamental role in intelligent medical care.The assignment of ICD codes to health-related documents has become a focus of academic research,and numerous studies have developed the process of ICD coding from manual to automated work.In this survey,we review the developmental history of this task in recent decades in depth,from the rules-based stage,through the traditional machine learning stage,to the neural-network-based stage.Various methods have been introduced to solve this problem by using different techniques,and we report a performance comparison of different methods on the pub-licly available Medical Information Mart for Intensive Care dataset.Next,we summarize four major challenges of this task:(1)the large label space,(2)the unbalanced label distribution,(3)the long text of documents,and(4)the interpretability of coding.Various solutions that have been proposed to solve these problems are analyzed.Further,we discuss the applications of ICD coding,from mortality statistics to payments based on disease-related groups and hospital performance management.In addition,we discuss different ways of considering and evaluat-ing this task,and how it has been transformed into a learnable problem.We also provide details of the commonly used datasets.Overall,this survey aims to provide a reference and possible prospective directions for follow-up research work.

Pulmonary tuberculosis detection model of chest X-ray images using convolutional neural network

The primary screening for pulmonary tuberculosis mainly relies on X-ray imaging all over the world. In recent years, the incidence of pulmonary tuberculosis has rebounded. This paper proposes a convolutional neural networks(CNN) based model on the tuberculosis detection of chest X-ray images, which is used for the automatic screening of pulmonary tuberculosis. Compared with the conventional CNN, this model can be used to detect the details of images and the areas of the disease quickly and accurately. There is an improvement in the learning speed and accuracy rate of our method, so it can better complete the work of anomaly detection and it can provide more effective auxiliary decision information for the practitioners.

Automatic detection of breast nodule in the ultrasound images using CNN

Breast cancer is the most common cancer among women worldwide.Ultrasound is widely used as a harmless test for early breast cancer screening.The ultrasound network(USNet) model is presented.It is an improved object detection model specifically for breast nodule detection on ultrasound images.USNet improved the backbone network,optimized the generation of feature maps,and adjusted the loss function.Finally,USNet trained with real clinical data.The evaluation results show that the trained model has strong nodule detection ability.The mean average precision(mAP) value can reach 0.734 9.The nodule detection rate is 95.11%,and the in situ cancer detection rate is 79.65%.At the same time,detection speed can reach 27.3 frame per second(FPS),and the video data can be processed in real time.

Comparison of three data mining methods in predicting 5-year survival of colorectal cancer patients

The prediction of colorectal cancer(CRC) survivability has always been a challenging research issue. Considering the importance of predicting CRC patients’ survival rates, we compared the performance of three data mining methods: decision trees(DTs), artificial neural networks(ANNs) and support vector machines(SVMs), for predicting 5-year survival of CRC patients to assist clinicians in making treatment decisions. The CRC dataset used to build the prediction model comes from the surveillance, epidemiology, and end results(SEER) program. The 5-fold cross-validation and random forest algorithm were respectively utilized for measuring the model predictive accuracy and the importance of features. Experimental results show that the predictive accuracy of ANNs(0.73) and SVMs(0.75) were higher than that of DTs, and they also have the best result in the area under the receiver operating characteristic(ROC) curve(area under curve(AUC)=0.82). This result may indicate high predictive power of ANNs and SVMs for predicting 5-year survival of CRC patients.

An efficient confidentiality protection solution for pub/sub system

Publish/subscribe(pub/sub)systems are widely used in large-scale messaging systems due to their asynchronous and decoupled nature.With the population of pub/sub cloud services,the privacy protection problem of pub/sub systems has started to emerge,and events and subscriptions are exposed when executing event matching on untrustworthy cloud brokers.However,as the number of subscriptions increases,the effectiveness of the previous confidentiality protection approaches declines drastically.In this paper,we propose SBM(scalable blind matching),an effective confidentiality protection scheme for pub/sub systems.To the best of our knowledge,SBM is the first scheme that applies order-preserving encryption algorithm to protect the system’s confidentiality and ensure its scalability.In this scheme,SBM-I is highly effective in subscription matching but is unable to achieve ideal security IND-OCPA,whereas SBM-II is suggested to ensure system security and SGX is used to reduce interaction and boost ciphertext matching performance.The experiment demonstrates that this method has better matching performance compared to others:the average matching time of SBM-I is 3–4 orders of magnitude faster than the matching algorithm MP and SGX-based algorithm SCBR when the number of subscriptions is 500,000,and the average matching time of SBM-II is 40 times faster than MP and 24 times than SCBR.

LMR-CBT: learning modality-fused representations with CB-Transformer for multimodal emotion recognition from unaligned multimodal sequences

Learning modality-fused representations and processing unaligned multimodal sequences are meaningful and challenging in multimodal emotion recognition.Existing approaches use directional pairwise attention or a message hub to fuse language,visual,and audio modalities.However,these fusion methods are often quadratic in complexity with respect to the modal sequence length,bring redundant information and are not efficient.In this paper,we propose an efficient neural network to learn modality-fused representations with CB-Transformer(LMR-CBT)for multimodal emotion recognition from unaligned multi-modal sequences.Specifically,we first perform feature extraction for the three modalities respectively to obtain the local structure of the sequences.Then,we design an innovative asymmetric transformer with cross-modal blocks(CB-Transformer)that enables complementary learning of different modalities,mainly divided into local temporal learning,cross-modal feature fusion and global self-attention representations.In addition,we splice the fused features with the original features to classify the emotions of the sequences.Finally,we conduct word-aligned and unaligned experiments on three challenging datasets,IEMOCAP,CMU-MOSI,and CMU-MOSEI.The experimental results show the superiority and efficiency of our proposed method in both settings.Compared with the mainstream methods,our approach reaches the state-of-the-art with a minimum number of parameters.