The huge economic loss resulting from network attacks and intrusions has led to an intensive study on network security. The network security is usually reflected by some relevant data that can be collected in a network system. By learning and analyzing such data, which are called security-related data, we can detect the intrusions to the network system and further measure its security level. Clearly, the first step of detecting network intrusions is to collect security-related data. However, in the context of 5G and big data, there are a number of challenges in collecting these data due to the heterogeneity of network and ever-growing amount of data. Therefore, traditional data collection methods cannot be applied in the next generation network systems directly, especially for security-related data. This paper presents the design and implementation of an adaptive security-related data collector based on network context in heterogeneous networks. The proposed collector solves the issue of heterogeneity of network system by designing a Security-related Data Description Language (SDDL) to instruct security related data collection in various networking contexts. It also applies adaptive sampling algorithms to reduce the amount of collected data. Furthermore, performance evaluation based on a prototype implementation shows the effectiveness of the adaptive security-related data collector in terms of a number of pre-defined design requirements.
Cloud computing plays an important role in supporting data storage, processing, and management in the Internet of Things (IoT). To preserve cloud data confidentiality and user privacy, cloud data are often stored in an encrypted form. However, duplicated data that are encrypted under different encryption schemes could be stored in the cloud, which greatly decreases the utilization rate of storage resources, especially for big data. Several data deduplication schemes have recently been proposed. However, most of them suffer from security weakness and lack of flexibility to support secure data access control. Therefore, few can be deployed in practice. This article proposes a scheme based on attribute-based encryption (ABE) to deduplicate encrypted data stored in the cloud while also supporting secure data access control. The authors evaluate the scheme's performance based on analysis and implementation. Results show the efficiency, effectiveness, and scalability of the scheme for potential practical deployment.
This paper investigates the performance of wireless powered communication (WPC) systems from a physical layer security (PLS) viewpoint. With that aim, By considering the Fisher-Snedecor $\mathcal{F}$ distribution as the underlying fading model for all channels, we derive closed-form expressions for the average secrecy capacity (ASC) and the secrecy outage probability (SOP). The derived expressions are formulated using the bivariate Fox's If-function, providing a mathematical framework to analyze the secrecy performance of WPC systems. The accuracy of the obtained analytical results is further validated by Monte-Carlo simulation, which provides valuable insights for the design and optimization of WPC systems under Fisher-Snedecor $\mathcal{F}$ fading model.
The rapid development of the Internet of Things (IoT) has led to the generation and perception of large amounts of data from IoT devices. These data are outsourced to the cloud for flexible sharing and deep analytics, which can significantly enhance IoT applications. But this raises privacy concerns for IoT devices. Attribute-based encryption is applied to realize fine-grained access control, which offers data owners control capability over their outsourced data. However, the centralized infrastructure is susceptible to the single-point-of-failure problem and may not be suitable for highly distributed IoT applications due to high latency. To overcome this issue, blockchain technology is introduced to realize a distributed infrastructure and provide robust data services. Owing to the innate transparency characteristic of blockchain, challenges associated with privacy are amplified. Therefore, this paper presents an efficient and decentralized data access control scheme (EDDAC) that preserves attribute privacy. The scheme utilizes chameleon hash to implement attribute hiding, providing resistance against dictionary attacks. Additionally, it includes blockchain-based decryption tests that reduce the decryption overhead on clients through the application of inner-product predicate encryption. Furthermore, our scheme employs Shamir secret sharing to achieve decentralized authorization based on blockchain, thereby reducing the trust-building overhead on authorization nodes. Finally, we provide proof of the adaptive security of the proposed scheme and demonstrate its effectiveness and advantages through simulations and comparisons with existing literature.
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