Peer-To-Peer Networking and Applications最新文献

Crowdfunding refers to the online collection of certain capital from a vast number of individuals or groups that each contribute a relatively small amount. Recently, the credibility of crowdfunding platforms has been undermined by fraudulent projects, inadequate fund management, and other forms of disorder. The decentralization and anti-tampering features of blockchain provide the possibility to solve the above problems, and many studies have proposed blockchain-based crowdfunding schemes. However, the existing state-of-the-art methods do not provide user authentication, transaction auditing, and identity management in a privacy-preserving way. Accordingly, this paper presents a novel blockchain-based crowdfunding system called CrowdChain. Initially, the distributed identity and BLS signature are employed to establish a user authentication mechanism, enabling CrowdChain to withstand Sybil attacks while preserving the non-linkability of user identities. Secondly, the physically unclonable function (PUF) is used to generate keys associated with digital identities that are not stored in external devices to resist physical attacks. Subsequently, a crowdfunding mechanism is constructed utilizing zero-knowledge proofs to facilitate streamlined auditing procedures while safeguarding the confidentiality of transactions. Additionally, the formal security analysis proves the security of the CrowdChain scheme. The system prototype is implemented on the Hyperledger Fabric. Empirical evidence indicates the viable efficiency of CrowdChain.

Ensuring effective and reliable communication within underwater sensor networks (UWSNs) is a formidable challenge due to their unique characteristics, which include offshore exploration, underwater surveillance and monitoring. UWSNs have proven to be a promising approach in various fields, including research investigations, surveillance operations and underwater disaster response. To advance this field, numerous researchers have dedicated themselves to developing new protocols tailored to UWSNs or refining existing protocols, all with the goal of improving research. One important aspect that continues to attract the attention of researchers is the reliability factor in the underwater environment, leading to constant efforts to improve the overall efficiency of the network and optimize energy consumption. In this work, a machine learning based node reliability calculation algorithm (MRNQ) has been proposed, which takes into account numerous parameters such as the success rate, transmission time, node efficiency, and the network efficiency. The proposed approach outperforms CSLT and TMHCV across key metrics with notable percentage improvements. It achieves a 5.16% higher packet delivery rate, a 22.06% reduction in packet drop rates, a 42.4% extension in network lifetime, and a 0.87676% improvement in malicious node detection.

Beaconing is a fundamental task in IoT networks, where each node tries to locally broadcast a packet to all its neighbors. Unfortunately, the problem of Minimum Latency Beaconing Schedule (MLBS), which tries to obtain the fastest and collision-free schedule, is not well studied when the IoT devices employ the duty-cycled working mode. The existing works have rigid assumptions that there exists a single channel and can only work in a centralized fashion. Aiming at making the work more practical and general, in this paper, we investigate the first distributed method for the MLBS problem in Multi-channel asynchronous Duty-cycled IoT networks (MLBSMD problem). The MLBSMD problem is first formulated and proved to be NP-hard. To avoid collisions locally, several special structures are designed which can work in (varvec{O}(varvec{Delta }^{2})) time, where (varvec{Delta }) denotes the maximum node degree in the network. Then, a distributed beaconing scheduling method that can compute a low-latency and collision-free schedule is proposed with a theoretical bound, taking the active time slots of each node into account. Finally, the extensive simulation results demonstrate the effectiveness of the proposed algorithm in terms of latency.

Network intrusion detection systems (NIDSs) have evolved into a significant subject in cybersecurity research, mainly due to the growth of cyberattacks and intelligence, which also led to the usage of machine learning (ML) to advance and enhance NIDSs. A NIDS is the first line of defense in any environment, and it detects external and internal attacks. Recently, intrusion mechanisms have become more sophisticated and challenging to detect. Researchers have applied techniques such as ML to detect intruders and secure networks. This paper proposes a novel approach called SRFE (Stepwise Recursive Feature Elimination) to improve the performance and efficiency of predictive models for NIDSs. Our approach depends primarily on recursive feature elimination, which operates on a simple yet effective principle. We experimented with four classification algorithms, namely Support Vector Machine (SVM), Naive Bayes (NB), J48, and Random Forest (RF), on the most widely used dataset in the cybersecurity domain (NSL-KDD). The approach is mainly built on the features’ significance ranking using the Information Gain (IG) method. We conduct multiple experiments according to three scenarios. Each scenario contains various rounds, and in each round, we train the classifiers to eliminate the three lowest-ranked features stepwise. Our experiments show that the RF and J48 classifiers outperform other binary classifiers with an accuracy of 99.80% and 99.66%, respectively. Furthermore, both classifiers obtained the best results in the multiclass classification task; J48 achieved an accuracy of 99.53% in round number seven, and the RF achieved 99.69% in the fifth round.

The Intelligent Transport System (ITS) is very prominent due to its connection with the Internet of Things (IoT), which enhances passenger security and comfort. The Vehicular Ad-hoc Network (VANET) is a component of ITS. It manages the techniques used for routing and privacy in autonomous cars. The increasing number of autonomous cars has exceeded the capacity of current wireless networks for transmission. It is expected that the proposed 6G wireless network can meet VANET criteria. Very little research has investigated the privacy concerns of VANETs in 6G networking connections. This work presents a method for dealing with authentic and privacy concerns for automobiles in VANETs. Our solution strengthens the vehicle's connectivity system by detecting malicious attacks like replay attacks, DoS attacks, and impersonification attacks. The proposed system uses batch authentication to reduce traffic and workload on the network. The proposed system employs both ID-based authentication and deep learning methods. Where the role of ID-based authentication is to check for access in the network, deep learning takes on the role of identifying all the malicious packets in the system. Our result also shows that the proposed system can identify malicious packets with an accuracy of 98.25% and works successfully in 6G networking communication.

With the continuous development of information technology, drones have become the supporting technology for sustainable smart cities. Currently, the blockchain that guarantees the information security of the unmanned aerial vehicle (UAV) network has become the focus of academic attention. However, due to the small size of the drone, and its limited storage and battery capacity, it is difficult to support the sustainable work of the UAV blockchain network. Therefore, this paper proposes the concept of sustainable blockchain (SusChain) and empowers the UAV blockchain network to better apply it to sustainable smart cities. In particular, we have introduced and improved the Ultra-Low Storage Overhead-Practical Byzantine Fault Tolerance (ULS-PBFT) consensus in the UAV blockchain network, making it a sharding scheme with extremely low storage overhead and energy consumption. Meanwhile, we design a reptation-and-matching-based UAV clustering scheme to ensure that each shard and SusChain have a high consensus success rate. The simulation results show that SusChain has a significant advantage in the key indicators of sustainability. In specific cases, it has a 9–227%, 11–58%, and 27–56% improvement effect in consensus security, consensus delay, and energy consumption, compared to other sharding schemes.

Blockchain technology and non-fungible tokens have gained significant attention and widespread adoption due to their unique characteristics and potential for innovation in numerous industries, including education. In recent years, there has been a significant increase in digital learning resources such as multimedia-rich learning content, audio content, and online course content. The advancement of these learning resources has introduced several copyright challenges, such as copyright ownership, fair use of resources, licensing agreements, open educational resources and Creative Commons licenses, digital piracy, and international considerations. Copyright infringement in educational learning resources poses a significant concern that impacts content creators hugely and raises concerns regarding the quality and reliability of the content. This work proposes a copyright protection framework named EduCopyRight-Chain for educational resources using the Ethereum blockchain and non-fungible tokens to overcome concerning challenges. This work also presents a sharding approach within the proposed framework to enhance scalability. Additionally, this work proposes wallet generation, network joining, educational resource tokenization, and a verification approach by utilizing blockchain and non-fungible tokens. The proposed framework uses a Proof-of-Authority consensus mechanism to validate transactions over the peer-to-peer network, and an inter-planetary file system is used for the decentralized storage of associated records. This framework uses Remix IDE, MetaMask wallet, and the Sepolia test network. We use the BlockSim simulation toolkit to conduct experiments to evaluate the performance of the proposed framework in terms of throughput, latency, response time, and standard deviation. The proposed framework achieves an average throughput of 354.26 TPS, a latency of 62.2 milliseconds, a response time of 124.1 milliseconds, and a standard deviation of 144.2 milliseconds. This work also conducts a comparative analysis to assess security features and limitations between the proposed framework and related work. Our observation reveals that the proposed EduCopyRight-Chain framework has better features.

With the widespread implementation of artificial intelligence techniques and self-driving systems in smart cars, providing excellent coverage of wireless sensor networks is critical for stable and effective tasks. Coverage control is an essential task for the design of wireless sensor networks. However, considering the influence of network resources and coverage features, several normal optimization methods are hard to carry out, yet heuristic iterative algorithms could generate an estimated ideal feasible solution for this issue. We present an artificial bee colony algorithm based on random dual strategies, called RDABC. Specifically, RDABC modifies the optimization direction through alternating between dual search techniques with the goal to find further excellent feasible solution. At the same time, through incorporating cross-mutation strategy to improve variety, and increase the algorithm’s optimization efficiency. According to simulation experiments, RDABC outperforms four well-known algorithms in terms of coverage optimization. As a whole, RDABC optimizes the location and deployment of wireless sensors, enhances the overall performance and stability of intelligent transportation systems, and simplifies vehicle monitoring and traffic sign tasks.

There has been a significant rise in the ways the internet caters to day-to-day usage in everyday lives. Significant presence in connecting IoTs, helping via online education, entertaining through online games, taking business decisions, and many more. Therefore, all these activities generate an abundance of data and require its management as well. There is a need to secure these networks from malicious attackers to prevent any harmful acts. Network security is still an attractive topic to conduct research on. In this paper, the Net Flow-based dataset NF-UNSWNB15-v2 has been considered for the experimentation and tried to resolve problems in building IDS. Problems like handling a large number of features have been addressed by utilizing FOX optimization with a V-shaped transfer function for binarization purposes and selecting the optimal features. Further classifying it using Light-GBM and evaluating the results for the binary and multi-class classifications. The proposed model selects minimum number of features for both binary and multi-class classification as compared to the other existing methods. Further evaluating on various parameters, the proposed approach performs satisfactorily and improvement in detection rate for various attacks like DoS, Exploits, Fuzzers etc. has been observed.

Cloud-based telemedicine uses powerful data processing technology to improve remote healthcare services. This helps make healthcare more accessible, efficient, and beneficial for both patients and healthcare providers. However, the security and privacy of patients sensitive data, especially when outsourcing to the cloud, remain significant concerns. To address this issue, patient data is stored in encrypted format on the cloud server. A searchable encryption mechanism is employed to enable efficient search on the encrypted data without compromising information confidentiality. While most searchable encryption schemes support conjunctive field keyword search in both single receiver and multi-receiver scenarios, they often result in partial information leakage related to the searched keywords. Additionally, a new scheme based on the Lagrange polynomial concept was developed to support conjunctive field-free search in a single receiver scenario. However, it is unsuitable for multi-receiver scenarios and suffers from certification management challenges. To overcome these limitations, we propose an innovative approach called "Multi-receiver Certificateless Public-key Searchable Encryption: Field-free Subset Conjunctive and Disjunctive." Our scheme is constructed upon reciprocal maps and leverages Lagrange polynomials as a fundamental tool. It offers several advantages, including cipher-index indistinguishability against chosen keyword attacks, utilizing the hardness of the decisional linear Diffie-Hellman assumption. Theoretical and experimental analyses demonstrate that our proposed scheme achieves comparable performance to existing works in terms of computational efficiency and communication overhead.