Open Network Environment Research Articles

Event-triggered sliding mode control for networked T–S fuzzy delayed systems against random injection attacks

This paper investigates the event-triggered sliding mode control (SMC) problem for networked T–S fuzzy delayed systems subject to random injection attacks, where the event-triggered mechanism (ETM) is introduced to alleviate data congestion and improve network efficiency. Under an open network environment, malicious attackers may randomly inject false information into the controller–actuator channel to compromise the integrity of the data. A new fuzzy sliding mode controller is constructed based on a non-parallel distributed compensation strategy, which introduces the distribution information of the attack probability. Then, the membership function dependent (MFD) analysis technique is adopted to reconstruct the membership functions in order to reduce the conservatism caused by the mismatch between the membership functions of fuzzy model and controller. Furthermore, the co-design conditions for the ETM and the fuzzy sliding mode controller are provided, which can ensure that the resulting closed-loop fuzzy system is mean-square exponentially ultimately bounded (EUB) and the sliding surface is reachable. Finally, a simulation experiment with tunnel diode circuit application is utilized to demonstrate the validity of the proposed SMC scheme.

P4flow: A software-defined networking approach with programmable switches for accounting and forwarding IPv6 packets with user-defined flow label tags

A comprehensive analysis of the HEP (High Energy Physics) experiment traffic across LHCONE (Large Hadron Collider Open Network Environment) and other networks, is essential for immediate network optimisation (for example by the NOTED project) and highly desirable for long-term network planning. Such an analysis requires two steps: tagging of network packets to indicate the type and owner of the traffic being carried and then processing of the tagged packets. The RNTWG (Research Network Technical Working Group) has defined a specification to identify the experiment and the application that originates a given network flow, named scitags (Scientific Network Tags) that is applied to the flow label field of the IPv6 header. We report here on the second step: processing of packets tagged according to this specification. We developed P4flow as a software-defined networking approach by using P4 (Programming Protocol-Independent Packet Processors), a language for programming the data plane of network devices to accounting and process IPv6 packets with a scitags-based stamp in the flow label field, to understand the network utilisation and the applications used by the WLCG (Worldwide Large Hadron Collider Computing Grid) sites. With P4flow, and exploiting the control plane capabilities provided by RARE/FreeRtr (an Open Source Network Operating System developed by the GÉANT community), we cannot only generate statistics concerning the traffic per experiment and application but can also, using an Intel Tofino P4-programmable ASIC Ethernet Switch, decide how to forward traffic matching defined flow labels. This latter capability is particularly interesting as we prepare for a future where LHC experiments will be sharing network links with other major science collaborations such as SKA and ITER.

An adaptive classification and updating method for unknown network traffic in open environments

In recent years, to address the classification problem of unknown traffic in open network environments, numerous deep learning (DL)-based classification techniques have been proposed. Among these techniques, the single threshold-based DL method has gained significant popularity. However, the classification boundary constructed by this method is inaccurate, thus leading to the misclassification of known and unknown traffic. In this paper, we propose an adaptive classification and updating method for accurate application-level classification of known and unknown traffic in open environments. We adaptively construct individualized and accurate decision boundaries for each class of known traffic, which avoids misclassification cases caused by a single threshold and achieves accurate separation of known and unknown traffic. To provide a more accurate feature representation for the construction of the decision boundary, we also introduce an additional angular loss function to guide the model in learning more accurate flow features. In addition, to cope with the constant emergence of new classes of traffic in real network environments, our proposed method adaptively updates the model to respond to changes in network traffic classes. The proposed method is evaluated on two datasets, and the experimental results show that our proposed method has excellent classification accuracy and outperforms the state-of-the-art unknown traffic classification methods.

Improving Kalman filter for cyber physical systems subject to replay attacks: An attack-detection-based compensation strategy

This paper investigates the problem of improving Kalman filtering for a class of multi-layer cyber physical systems (CPS) in the face of multiple consecutive replay attacks. CPS is vulnerable in an open network environment, and the replay attack model is described based on the number and duration of successful message transmissions. For all possible occurrences of replay attacks, a detection and compensation method is obtained with the help of the residual monitoring mechanism and an attack detection function. At the same time, in order to identify the attack has occurred in which, the register is integrated into the system. Furthermore, a novel Kalman filter with a compensation mechanism is designed to estimate the ideal states of CPS subject to replay attacks, and the Kalman filter gain is optimized based on the detection results. Finally, a simulation example is given to verify the effectiveness of the proposed improving Kalman filter.

Poisoning QoS-aware cloud API recommender system with generative adversarial network attack

With the proliferation and deepening of service-oriented architecture, more and more enterprises and organizations are exposing their computing functions and big data to the Internet in the form of cloud APIs to support service-oriented software development. This has resulted in a plethora of cloud APIs with similar functionality appearing on the Web, drowning users in a sea of cloud API choices. To solve this problem, quality of service (QoS)-aware recommender system is then widely applied to the selection of cloud APIs. Due to the dynamic and open network environment, the QoS-aware cloud API recommender systems are vulnerable to data poisoning attacks, where attackers inject poisoned data to skew the recommender system and make the recommendation direction follow the attacker's will. Given the lack of data poisoning attack methods and robustness analysis for existing QoS-aware cloud API recommender systems, in this work, we first built a general poisoning attack framework for QoS-aware cloud API recommender systems to elucidate and standardize the attack process. Then, we proposed a deep learning-based poison attack approach, which uses generative adversarial network (GAN) to learn the cloud API QoS data distribution of real users in an adversarial way, so as to generate high-quality fake user attack vectors. We conducted extensive experiments on real-world QoS datasets, and the experimental results show that our proposed GAN-based poisoning attack is effective and can better hide itself from being detected. In addition, we analyzed the data poisoning attack mechanism and the robustness of the cloud API recommender system based on four categories of twelve recommendation methods, thereby raising awareness about the security of cloud API recommendation and helping the recommender system defenders to develop more targeted defense strategies.

Comparative interaction patterns of groups in an open network environment: The role of facilitators in collaborative learning

AbstractBackgroundCollaborative learning can effectively promote the development of higher‐order competencies, where learners collaborate via social interactions to explore and solve complex problems. It has been found that the effective occurrence of collaborative learning requires appropriate external guidance and support. In particular, collaborative interactions in open learning environments are more complex and difficult, which makes it important to provide dynamic interventions for learners according to different collaborative situations. However, the role of facilitators and the characteristics and mechanisms of their interventions have yet to be thoroughly investigated.ObjectivesThe study aims to uncover the characteristics and patterns of interaction among groups with different collaborative performance and facilitator performance in an open learning environment, and to explore the differentiated role of facilitators in this process.MethodGroups are categorised as high or low based on their collaborative performance and facilitator performance. This study used lag sequential analysis (LSA) and content analysis to explore the interaction patterns of groups with different collaborative performance and facilitator performance. Also, the different roles of facilitators were analysed by comparing the differences in interaction patterns of different collaborative performance groups.ResultsThe study found that groups with different collaborative performances and facilitator performances showed significant differences in interaction patterns, and the two had crossover effects. There is also variability in the role of facilitators in groups with different collaborative performance profiles. Patterns of transition between different layers of interaction can indicate the level of groups' collaborative engagement. Specific interaction patterns exist that are conducive to enhancing collaborative learning.ConclusionsThe results of the study pointed out that groups with different collaborative performance and facilitator performance showed different characteristics of interaction patterns. The role of the facilitator in online collaborative learning is characterised by compensatory and merit‐promoting. High‐quality online collaboration is the result of multiple factors, including learners and facilitators working together. This study has practical implications for the design of instructional interventions for group collaboration in online learning communities.

Security and Privacy in Vehicular Digital Twin Networks: Challenges and Solutions

With the flourishing and advancement of autonomous vehicles, the vehicular digital twin (VDT) has become an emerging paradigm, consisting of inter-twin communication and intra-twin communication. VDT can effectively address the current shortcomings of the autonomous vehicle and provide enhanced and scalable services to the users. Nevertheless, strict security and privacy concerns arise, as VDT collects a wide range of privacy-sensitive information of the users in real-time and is exposed to an open network environment in both physical and virtual horizons. In this article, we investigate the security and privacy of VDT. We first describe the vehicular digital twin network architecture in detail and then investigate some VDT-related applications. After that, we discuss the security and privacy issues in VDT. Finally, we put forward several potential countermeasures and open research issues for VDT from the perspective of security and privacy. We expect this work will bring more attention to further investigation on security and privacy solutions for vehicular digital twin networks.

Hier-SFL: Client-edge-cloud collaborative traffic classification framework based on hierarchical federated split learning

Network traffic classification technology is widely noticed by internet service providers(ISP), in order to monitor network security status, improving knowledge of users’ traffic demands and then ulterior design policing and prioritization mechanisms. With the continuous development of deep learning technology, network traffic classification technology starts to use supervised learning paradigm to automate traffic identification by extracting some features. However, due to the continuous emergence of new private protocols, network services and access devices, the overall distribution of network traffic data is constantly changing. The training of network traffic classification models usually relies on the labeled data collected by academic organizations, institutions and companies. These data are usually generated in a fixed network environment, and the types of labels collected are limited. Models trained in this way are difficult to adapt to the open and complex network environment. Furthermore, the unevenly distributed computing resources affect the model training efficiency because the collected traffic data is usually concentrated in the end system but the computing power is generally concentrated in the edge computing nodes and cloud centers. Federated learning technology can coordinate data holders to train globally adapted models from discrete data. At the same time, split learning technology can realize more efficient model separation training. In this paper, we designed a new three-tier algorithm Hier-SFL combining and improving these two learning frameworks according to the common client-edge-cloud structure. Hier-SFL algorithm allocated the training task of neural network to each level network device with different computing resources. Also, it took the advantage of federated learning to periodically aggregate network parameters. Thus, we significantly improved the training efficiency of the deep learning model of traffic classification.

Blockchain-based dynamic trust access control game mechanism

The blockchain-based access control mechanism (BACM) is gradually becoming an essential paradigm for solving dynamic and trusted access control problems in the open network environment. However, since the current open network environment has such features as dynamic variability and the uncertainty of user identity, most of the existing BACM cannot solve the access control problems in the current open network environment in a dynamic, flexible, proactive, efficient, and fine-grained approach. In this paper, we propose a novel BACM scheme to address such problems. Specifically, we first design a new, proactive, and fine-grained access control model, by utilizing the dynamicity and fine-grain of the attribute-based access control model, flexibility shown by the trust evaluation mechanism in evaluating the trust level of users, and proactivity shown by the game evaluation mechanism in curbing malicious users who suddenly launch malicious access requests. Second, based on the above access control model, we propose a dynamic, flexible, and proactive BACM for the current open network environment, exploiting the trustworthiness and transparency that the smart contract and the transaction mechanism in blockchain technology show during program execution. Further, a double sliding storage window is built, guaranteeing accurate data acquisition by BACM while efficiently allowing it to acquire time-sensitive data during the permission management process. Meanwhile, a pre-authorization concept is introduced to improve the efficiency and flexibility of BACM in processing access control problems. Security analysis demonstrates that our proposed BACM scheme satisfies the simple security issue and the simple availability issue. Experiments on a real user trust record dataset demonstrate the high effectiveness of the proposed BACM scheme in evaluating and deciding on access requests and the superiorities over most existing schemes in dynamicity, fine granularity, flexibility, and proactivity.

A detection model of scaling attacks considering consumption pattern diversity in AMI

As an important branch of the Internet of Things, the smart grid has become a crucial field of modern information technology. It realizes the two-way information flow and power flow by integrating the advanced metering infrastructure (AMI) and distributed energy resources, which greatly improves users’ participation. However, owing to smart meters, the most critical components of AMI, are deployed in an open network environment, AMI is a potential target for data integrity attacks. Among various attack types, the scaling attack is the most typical one, because it can be used as a general expression for most of other ones. By launching a scaling attack, adversaries can randomly reduce hourly reported values in smart meters, thereby causing economic losses. A number of research efforts have been devoted to detecting data integrity attacks. Nonetheless, most of the existing investigations focus on all attack types, and little attention has been paid to a detection strategy specially designed for scaling attacks. Our contribution addresses this issue in this paper and hence, developing a detection model of scaling attacks considering consumption pattern diversity (SA2CPD), to ensure that scaling attacks can be effectively detected when users have multiple consumption patterns. To be specific, we leverage Kmeans to distinguish different consumption patterns, and then the consumption intervals can be extracted to binarize the data. We divide time periods in every day into two categories based on the binarization values, and use one of them with the greatest information gain to construct a decision tree for judgment. Both theoretical and simulation results based on the GEFCom2012 dataset show that our SA2CPD model has a higher F1 score than the decision tree model without considering consumption pattern diversity, the KNN model and the Naive Bayes model.

Research on anti-attack performance of a private cloud safety computer based on the Markov-Percopy dynamic heterogeneous redundancy structure

Abstract With the increasing computing demand of train operation control systems, the application of cloud computing technology on safety computer platforms of train control system has become a research hotspot in recent years. How to improve the safety and availability of private cloud safety computers is the key problem when applying cloud computing to train operation control systems. Because the cloud computing platform is in an open network environment, it can face many security loopholes and malicious network attacks. Therefore, it is necessary to change the existing safety computer platform structure to improve the attack resistance of the private cloud safety computer platform, thereby enhancing its safety and reliability. Firstly, a private cloud safety computer platform architecture based on dynamic heterogeneous redundant (DHR) structure is proposed, and a dynamic migration mechanism for heterogeneous executives is designed. Then, a generalized stochastic Petri net (GSPN) model of a private cloud safety computer platform based on DHR is established, and its steady-state probability is solved by using its isomorphism with the continuous-time Markov model (CTMC) to analyse the impact of different system structures and executive migration mechanisms on the system's anti-attack performance. Finally, through experimental verification, the system structure proposed in this paper can improve the anti-attack capability of the private cloud safety computer platform, thereby improving its safety and reliability.

Research on the logic monitoring method for the cloud computing based safety computer

Abstract With the development of railway construction in China, the computing demand of the train control system is increasing day by day. The application of cloud computing technology on the rail transit signal system has become a research hotspot in recent years. How to improve the safety and availability of the safety computer platform in the cloud computing environment is the key problem when applying cloud computing to the train operation control system. Since the cloud platform is in an open network environment, facing many security vulnerabilities and malicious network attacks, it is necessary to monitor the operation of computer programmes through edge safety nodes. Firstly, this paper encrypts the logical monitoring method, and then proposes a secure computer defence model based on the dynamic heterogeneous redundancy structure. Then the continuous time Markov chain (CTMC) is used to quantitatively solve the stable probability of the system, and the influence of different logical monitoring methods on the anti-attack performance of the system is analysed. Finally, the experiment proves that the dynamic heterogeneous redundancy structure composed of encryption logic monitoring can guarantee the safe and stable operation of the safety computer more effectively.

Security and Privacy for Healthcare Blockchains

Healthcare blockchains provide an innovative way to store healthcare information, execute healthcare transactions, and build trust for healthcare data sharing and data integration in a decentralized open healthcare network environment. Although the healthcare blockchain technology has attracted broad interests and attention in industry, government and academia, the security and privacy concerns remain the focus of debate when deploying blockchains for information sharing in the healthcare sector from business operation to research collaboration. This article focuses on the security and privacy requirements for medical data sharing using blockchain, and provides a comprehensive analysis of the security and privacy risks and requirements, accompanied by technical solution techniques and strategies. First, we discuss the security and privacy requirements and attributes required for electronic medical data sharing by deploying the healthcare blockchain. Second, we categorize existing efforts into three reference blockchain usage scenarios for electronic medical data sharing, and discuss the technologies for implementing these security and privacy properties in the three categories of usage scenarios for healthcare blockchain, such as anonymous signatures, attribute-based encryption, zero-knowledge proofs, verification techniques for smart contract security. Finally, we discuss other potential blockchain application scenarios in healthcare sector. We conjecture that this survey will help healthcare professionals, decision makers, and healthcare service developers to gain technical and intuitive insights into the security and privacy of healthcare blockchains in terms of concepts, risks, requirements, development and deployment technologies and systems.

CD-ABSE: Attribute-Based Searchable Encryption Scheme Supporting Cross-Domain Sharing on Blockchain

The network security situation is grim, and the problem of “information isolated island” is becoming increasingly prominent. In view of the low efficiency and insufficient security of data cross-domain sharing in the open network environment, a searchable data sharing scheme supporting cross-domain is proposed based on attribute encryption technology. Firstly, different types of nodes on the blockchain are used to realize the data sharing of users in different domains. Secondly, the flexible ciphertext-search function is realized through the search form of keyword strategy. Moreover, the scheme adopts the mode of storage under the chain, which reduces the operation pressure of the blockchain. At the same time, according to the characteristics of the blockchain, the traceability and tamper-proof of the access process can be realized. Finally, the analysis shows that the scheme can resist quantum attack and collusive attack while avoiding complex bilinear operation and meet the security of trapdoor search and indistinguishability under chosen-plaintext attack. Compared with other searchable attribute-based encryption schemes, the scheme has certain advantages in function and performance.

Blockchain data privacy protection modeling based on CP-ABE algorithm

Abstract With the proposal of the concept of ubiquitous power Internet of Things and the further promotion of blockchain strategy, modern information technologies such as the Internet of Things and cloud computing have been widely used in the power system. While the power grid is becoming more intelligent, it is also accompanied by the emergence of various security threats, and the power grid will face greater challenges and pressures. In this paper, aiming at the current situation of information security faced by the power grid blockchain under the cloud Internet of Things, a multi-authorization center ciphertext policy attribute encryption access control method is constructed for the power grid blockchain. It adds the function of visitor attribute update revocation, aggregates multi-dimensional data into ciphertext, and uses CP-ABE encryption to implement fine-grained access control at the dimension level. Through experimental analysis, in terms of security and functionality, this method can resist physical collision attacks and manage access rights flexibly. In terms of encryption and decryption efficiency, the encryption and decryption calculation speed of the proposed method is 6.2–15.6% higher than that of the traditional method, and the time consumption efficiency is more than 5% higher than that of other methods. It proves that this model has more advantages for open network environments such as power grid blockchain with high confidentiality requirements and large scale of users. It is of great significance to study the data security and privacy protection methods of the power grid blockchain.

Online Cyber-Attack Detection in the Industrial Control System: A Deep Reinforcement Learning Approach

In the open network environment, industrial control systems face huge security risks and are often subject to network attacks. The existing abnormal detection methods of industrial control networks have the problem of a low intelligence degree of adaptive detection and recognition. To overcome this problem, this article makes full use of the advantages of deep reinforcement learning in decision-making and builds a learning system with continuous learning ability. Specifically, industrial control network and deep reinforcement learning characteristics are applied to design a unique reward and learning mechanism. Moreover, an industrial control anomaly detection system based on deep reinforcement learning is constructed. Finally, we verify the algorithm on the gas pipeline industrial control dataset of Mississippi State University. The experimental results show that the convergence rate of this model is significantly higher than that of traditional deep learning methods. More importantly, this model can get a higher F1 score.

Cyber Security Protection of Power System Equipment Based on Chip-Level Trusted Computing

This article proposes a network security protection scheme for power system embedded devices based on chip-level trusted computing, gives the overall architecture of power equipment chip-level trusted computing, introduces the principle of trust chain transfer within power terminals, and proposes the method of using the on-chip SRAM inside the main control chip of power system embedded devices as the PUF to construct the trusted root. Furthermore, we propose a complete trust transfer mechanism from booting trust to the loading that trust on operating system and application, and give the corresponding program design framework and program logic. The chip-level trusted computing program designed in this article was applied in a distribution automation DTU device equipped with the FUXI chip that is developed by the China Southern Power Grid and the Sylix OS operating system. Finally, we carry out the security protection test. The validity of the designed program is verified by running malicious programs to attack the power equipment. The results show that the designed program can effectively detect and intercept illegal programs, and provide an effective solution for the security protection of power equipment in the open network environment.

Voter Authentication System Using Feature Level Fusion of Iris, Face and Palmprint

Abstract: The authentication scheme is an important cryptographic mechanism, through which two communication parties could authenticate each other in the open network environment. To satisfy the requirement of practical applications, many authenticationschemes using passwords and smart cards have been proposed. However,passwords might be divulged or forgotten, and smart cards might be shared, lost, or stolen. In contrast, biometric methods such as face or iris scans, have no such drawbacks. Three biometric traits are collected and stored into database at the time of enrollment. The Multimodel Biometric based user authentication systems are highly secured and efficient to use and place total trust on the authentication server where biometric verification data are stored in a database. Such systems are prone to dictionary attacks initiated at the server side. In thispaper, we propose an efficient approach based on multimodal biometrics-based user authentication and key exchange system. In this system,texture properties are extracted from the palm print, face and iris. Duringlogin procedure the mutual authentication is done between the user and server and a symmetric key is generated on both sides, which could be used for further secure communication between the user and the server can also be overcome. This system can be directly applied to strengthen existing password or biometric based systems without requiring additional computation.

Blockchain and deep learning based trust management for Internet of Vehicles

Internet of Vehicles (IoVs) works as the most advanced component of Intelligent Transportation Systems (ITSs). In IoVs, vehicles are able to communicate with nearby vehicles or RoadSide Units (RSUs). Traffic safety and efficiency can be provided by collecting and uploading real-time traffic information through vehicles, as well as broadcasting information by RSUs. However, there may be malicious vehicles in the network uploading false information, which will lead to serious traffic problems. To alleviate this problem, a trust management system based on blockchain technology is proposed in this paper. In this system, vehicles in the network firstly collect information about their surroundings and then upload valid information to nearby RSUs. To prevent malicious vehicles from uploading false messages, this paper designs a deep learning based verification model to calculate the trustworthiness of uploaded messages, and to further obtain the credibility scores of vehicles using the calculated results, and detect malicious vehicles accordingly. Moreover, a public blockchain framework is proposed and a Proof-Of-Trust (POT) consensus algorithm is designed. Vehicles are motivated to report true and valid information, and are penalized for uploading false data under this framework. Simulation results show that this mechanism can effectively detect malicious vehicles and motivate unfamiliar vehicles to upload true and reliable information to achieve trust management in the open and dynamic vehicular network environments.

EFFECTIVE LEDGER AND DECENTRALISATION IN HEALTHCARE USING BLOCKCHAIN

Healthcare block chains provide an innovative way to store healthcare information, execute healthcare transactions, and build trust for sharing health data in a decentralized open health network environment. Although the healthcare block chain technology has attracted broad interests and attention in industry, government and academia, the security and privacy concerns remain the focus of debate when deploying block chains for information sharing in the healthcare sector from business operation to research collaboration. This paper focuses on the security and privacy requirements for medical data sharing using block chain, and provides a comprehensive overview of security and privacy risks and requirements, along with technical solutions techniques. First, we discuss the security and privacy requirements and attributes required for electronic medical data sharing by deploying the healthcare block chain. Second, we categorize existing efforts into three benchmark block chain use cases for electronic medical information sharing and discuss the technologies to execute these security and privacy properties in the three categories of use cases for healthcare block chain, such as anonymous signatures, attribute-based encryption, zero-knowledge proofs, and verification techniques for smart contract Finally, we discuss other potential block chain application scenarios in healthcare We conjecture that this survey will help healthcare professionals, decision makers, and healthcare service developers to gain technical and intuitive insights into the security and privacy of healthcare block chains in terms of concepts, risks, requirements, development and deployment technologies and systems. Key Words: Asymmetric-key algorithm and hash function, SHA-256 algorithm.