International Journal of Network Management最新文献

In the digital age, the expansion of cyberspace has resulted in increasing complexity, making clear cyberspace visualization crucial for effective analysis and decision-making. Current cyberspace visualizations are overly complex and fail to accurately reflect node importance. To address the challenge of complex cyberspace visualization, this study introduces the integrated centrality metric (ICM) for constructing a metaphorical map that accurately reflects node importance. The ICM, a novel node centrality measure, demonstrates superior accuracy in identifying key nodes compared to degree centrality (DC), k-shell centrality (KC), and PageRank values. Through community partitioning and point-cluster feature generalization, we extract a network's hierarchical structure to intuitively represent its community and backbone topology, and we construct a metaphorical map that offers a clear visualization of cyberspace. Experiments were conducted on four original networks and their extracted backbone networks to identify core nodes. The Jaccard coefficient was calculated considering the results of the three aforementioned centrality measures, ICM, and the SIR model. The results indicate that ICM achieved the best performance in both the original networks and all extracted backbone networks. This demonstrates that ICM can more precisely evaluate node importance, thereby facilitating the construction of metaphorical maps. Moreover, the proposed metaphorical map is more convenient than traditional topological maps for quickly comprehending the complex characteristics of networks.

As the internet of vehicles (IoV) technology develops, it promotes the intelligent interaction among vehicles, roadside units, and the environment. Nevertheless, it also brings vehicle information security challenges. In recent years, vehicle data sharing is suffering to algorithm substitution attacks (ASA), which means backdoor adversaries can carry out filtering attacks through data sharing. Therefore, this paper designs a blockchain-based proxy re-encryption (PRE) scheme with cryptographic reverse firewall (BIBPR-CRF) for IoV. In our proposal, CRF can promise the internal safety of vehicle units. More specifically, it can prevent ASA attacks while ensuring chosen plaintext attack (CPA)-security. Meanwhile, the PRE algorithm can provide the confidential sharing and secure operation of data. Moreover, we use a consortium blockchain service center (CBSC) to store the first ciphertext and re-encrypt it with smart contracts on the blockchain, which can avoid single point of failure and achieve higher efficiency compared to proxy servers. Finally, we evaluate the performance of BIBPR-CRF with regard to communication cost, computational cost, and energy consumption. Our proposal is the most fitting for IoV application, in contrast with the other three schemes.

The development of multiple attack methods by external attackers in recent years poses a huge challenge to the security and efficient operation of software-defined networks (SDN), which are the core of operational controllers and data storage. Therefore, it is critical to ensure that the normal process of network interaction between SDN servers and users is protected from external attacks. In this paper, we propose a risk-aware SDN defense framework based on safe reinforcement learning (SRL) to counter multiple attack actions. Specifically, the defender uses SRL to maximize the utility by choosing to provide a honeypot service or pseudo-honeypot service within predefined security constraints, while the external attacker maximizes the utility by choosing an anti-honeypot attack or masquerade attack. To describe the system risk in detail, we introduce the risk level function to model the simultaneous dynamic attack and defense processes. Simulation results demonstrate that our proposed risk-aware scheme improves the defense utility by 17.5% and 142.4% and reduces the system risk by 42.7% and 59.6% compared to the QLearning scheme and the Random scheme, respectively.

Blockchain is an innovative technology for storing data in decentralized, distributed, and secure chain blocks. Consortium blockchains are commonly used in transactions where transactions between organizations are also achieved by the blockchain. In the classic consortium blockchain system, entire nodes are added to each other in the process of transaction consensus. This leads to lower confidentiality in protecting transaction data within the organizations in the consortium. The throughput of the existing consortium blockchain system is still low. To solve the above problems, the paper proposes a two-tier consortium blockchain with transaction privacy based on sharding technology. First, a trust value assessment is carried out to select the nodes of the blockchain. The duo-head observation strategy uses these trust values to identify the nonmalicious node. Finally, the consensus separation approach based on the guarantee mechanism strategy with the shard nodes is presented. This approach is used to select reliable nodes for the blocks to be stored. The proposed consortium blockchain approach evaluation is done in terms of latency, throughput, and transactions per second metrics. As a result of the evaluations, the proposed model with 32 shards possesses 143,891 $��\mathrm{tx}�/�s�$ of throughput and 1.11 s of latency. Moreover, by the proposed two-tier consortium model, time consumption is also decreased when uploading data. For a data set of 50,000, the suggested model has a time consumption of 96 s. The proposed research results in higher throughput and less latency in transactions. Also, the research enhances the scalability and reliability by overcoming the poor node issues.

The Internet of Things (IoT) is an evolving paradigm that has dramatically transformed the traditional style of living into a smart lifestyle. IoT devices have recently attained great attention due to their wide range of applications in various sectors, such as healthcare, smart home devices, smart industries, smart cities, and so forth. However, security is still a challenging issue in the IoT environment. Because of the disparate nature of IoT devices, it is hard to detect the different kinds of attacks available in IoT. Various existing works aim to provide a reliable intrusion detection system (IDS) technique. But they failed to work because of several security issues. Thus, the proposed study presents a blockchain-based deep learning model for IDS. Initially, the input data are preprocessed using min-max normalization, converting the raw input data into improved quality. In order to detect the presented attacks in the provided dataset, the proposed work introduced Gaussian mixture–fully convolutional variational autoencoder (GM-FCVAE) model. The implementation is performed in Python, and the performance of the proposed GM-FCVAE model is analyzed by evaluating several metrics. The proposed GM-FCVAE model is tested on three datasets and attained superior accuracy of 99.18%, 98.81%, and 98.4% with UNSW-NB15, CICIDS 2019, and N_BaIoT datasets, respectively. The comparison reveals that the proposed GM-FCVAE model obtained higher results than the other deep learning techniques. The outperformance shows the efficacy of the proposed study in identifying security attacks.