The Internet of Things (IoT) system gathers data through diverse smart devices and sensors to make thorough decisions tailored to specific needs. Yet, in intricate IoT setups, privacy infringement occurs through various means like data collection, initial data handling, and data sharing. Therefore, the concealment of data during transmission should receive sufficient attention. The communication approach that merges blockchain technology with covert communication has shown progress in addressing the aforementioned issues. However, this integration has also led to challenges, such as low-data embedding rates and distinctive features in blockchain transactions containing covert data. To seek a solution with high-embedding rates that do not make generated transactions stand out distinctly, this article analyzes the Ethereum transaction field formats, identifies the input data field with high concealment and large capacity as the embedding target, then proposes a data covert transmission scheme based on hybrid embedding in contract fields. This scheme utilizes LSB steganography to embed high-capacity covert data in images, and embeds the URL of the image into the input data field of the Ethereum smart contract transaction, thereby increasing the embedding rates. Subsequently, to further enhance the concealment of this scheme, a data embedding method based on contract relationships is proposed. Through this technique, for the first time, covert data transmission is achieved solely through the invocation relationships of smart contracts within the blockchain covert communication environment, instead of directly embedding covert data into transactions. This method results in transactions that are theoretically indistinguishable from regular transactions, greatly enhancing the security of the scheme. Finally, an evaluation of undetectability, embedding rate, and scalability was conducted for the proposed schemes, concluding that the schemes presented in this article have significant advantages in all three areas.

Vulnerability behavior scanning plays a crucial role in securing Intelligent Autonomous Transportation Systems by ensuring protected communications and maintaining data integrity. Current scanning solutions, however, demonstrate several critical shortcomings: (1) their dependence on static analysis methods with predetermined scanning locations prevents dynamic adjustment of scanning strategies; (2) their limited capacity to capture data across multiple system layers fails to address sophisticated multi-layered attack patterns; and (3) their inability to dynamically activate monitoring probes hinders timely responses to newly emerging threats. To resolve these limitations, we present$\textsf {VBSF}$, an efficient and non-intrusive vulnerability scanning framework built upon extended Berkeley Packet Filter technology. The proposed system incorporates two key innovations: a dynamic probe activation mechanism that intelligently adjusts scanning locations in real-time to optimize resource usage, and a standardized data format that enables integrated analysis of vulnerability behaviors across different system layers. Experimental evaluations confirm that$\textsf {VBSF}$effectively identifies critical vulnerability behaviors in diverse attack scenarios while introducing only 1.47% additional system overhead.

Understanding the network characteristics of IP nodes and identifying their potential usage scenarios are crucial for cyberspace applications, such as asset evaluation, fraud prevention, and network attack prevention. However, many studies related to IP nodes primarily focused on IP geolocation and anomaly detection, with very little attention given to IP usage. Identifying the usage scenarios of IP node can facilitate network optimization, enhance security and improve resource allocation, which is crucial for network management and security. In this work, we propose a novel framework named GraphCyber based on graph neural network to identify street-level IP usage for cyberspace mapping. We first design a topology rule-based approach dividing a large number of IP nodes into regional blocks. Then we devise regional blocks to fuse the self-information of IP nodes and various neighborhood relationships into the graph. Last, based on an uncertainty-aware graph neural network, we identify the usage scenarios of IP nodes within regional blocks. Extensive experiments conducted on three large-scale real-world data sets demonstrate the superiority of GraphCyber over several state-of-the-art baselines in accurately identifying the IP usage scenarios.

Recent studies have demonstrated that malicious adversaries are capable of fingerprinting Internet of Things (IoT) devices in a smart home and further causing privacy breaches. However, many existing anti-fingerprinting schemes, either by traffic padding or traffic mutation, are less effective in defending against state-of-the-art fingerprinting methods. To meet this gap, we in this paper propose the HomeSentinel, an intelligent anti-fingerprinting scheme to counter IoT traffic fingerprinting in smart homes. Specifically, we first design a LightGBM-based IoT traffic extraction model to accurately distinguish IoT traffic from raw network traffic in a smart home without user operations. Second, we develop a dummy IoT traffic generation model to produce dummy IoT traffic in desired spatial-temporal patterns. Third, an IoT traffic mixing strategy is crafted to heuristically merge dummy IoT traffic with real IoT traffic in desired spatial-temporal patterns. Extensive experiments on three real-world datasets (i.e., two public and one custom) demonstrate that our proposed HomeSentinel scheme can effectively defend against state-of-the-art IoT traffic fingerprinting methods, and outperforms existing IoT traffic anti-fingerprinting schemes. Further, real-world experiments are conducted on a self-built testbed show that, reasonably low communication delays can be caused when implementing the HomeSentinel in smart homes.