Covert communication enables covert information transmission in an undetectable way to prevent the exposure of communication behaviors. Blockchain-based covert communication breaks through the limitations on concealment, reliability and anti-traceability, and has shown promising application prospects in both sensitive data transmission and botnets. Although there are studies on blockchain-based covert communication, it still lacks a systematic investigation. In this paper, we conduct a comprehensive study on channel building and survey its core technologies by information embedding, transaction filtering, and transaction obfuscation. We also summarize evaluation metrics to better analyze blockchain-based covert channels. Privacy aspects are also discussed. Finally, we suggest seven future directions to stir research efforts into this area.

Blockchain-based censorship-resistant systems enable the user to access the blocked content through a covert channel while avoiding a suspicious network connection between the user and the proxy. However, state-of-the-art blockchain-based censorship-resistant schemes cannot satisfy both low communication fees and unobservability, and their method of identifying transactions with covert data may inadvertently expose the covert channel. In this paper, we present Hades, a blockchain-based covert channel framework that aims to circumvent censorship. Hades allows users to encode covert data as a transaction field, and identify transactions with covert data by using another transaction field as a label. We also present the security model for Hades, which defines the unobservability of Hades as the indistinguishability of transactions with covert data from normal transactions. We further propose two cost-friendly and unobservable instantiations of Hades: the basic RDSAC and the improved DDSAC. RDSAC uses private keys to encode covert data and utilizes random factors in the signing process as labels, while incurring a communication delay. DDSAC avoids the delay by encoding covert data into random factors and sampling a transaction amount from normal transactions as the label. We implement a prototype system of Hades and evaluate its performance. Experiment results show that our Hades prototype is unobservable, robust, and efficient. RDSAC and DDSAC can identify 1,654 transactions in 6.054 seconds and 0.071 seconds, respectively. Hades supports 1KB data transfer at $0.44 on the Bitcoin mainnet and cost-free data transfer on the Bitcoin testnet.

Deep Neural Networks (DNNs) have made significant progress in addressing the Non-Intrusive Load Monitoring (NILM) task, which aims to disaggregate appliance-level power signals from aggregated meter readings. Despite these advancements, existing DNN-based NILM approaches rely on training with power signals from individual appliances, which are obtained intrusively through sensor installations. This method is not only expensive but also poses a risk of damaging the original circuits. To overcome these limitations, we introduce the State-based Supervised NILM (SS-NILM) problem. Instead of using appliance power signal labels, we leverage on-off state information, which can be collected in a non-intrusive manner. However, solving SS-NILM presents a challenge, as it requires developing a model that maps on-off state labels to the corresponding appliance power signals in an indirectly supervised setting. In this work, we propose a state-based DNN that predicts the power signals of multiple target appliances simultaneously. The model is trained by minimizing the discrepancy between the aggregated prediction and the true aggregated power signal. Additionally, the model predicts the on-off states of appliances, which are used as auxiliary information to improve the accuracy of power signal predictions. Extensive experiments conducted on real-world datasets demonstrate that our model, trained using non-intrusive on-off state information, achieves performance comparable to that of traditional NILM models.

Blockchain-based covert communication enables undetectable data transmission by constructing covert channels in blockchain networks. However, extant approaches require transactions carrying secret data to be permanently preserved in the public ledger, which cannot resist data mining. Besides, these solutions cost up to $48,169 to transmit 1-MegaByte (MB) data. In this paper, we introduce a Tripartite Covert Communication Model (TCCM), which amalgamates blockchain and the Inter Planetary File System (IPFS) to facilitate the transfer of MB-level files while simultaneously circumventing data mining. TCCM comprises an IPFS covert channel, a ledger-layer covert channel, and a network-layer covert channel. The IPFS covert channel transmits the initial secret data. The ledger-layer covert channel embeds a timestamp into the blockchain transaction, which governs the construction time of the network-layer covert channel. The network-layer covert channel conveys the content identifier of the secret data utilizing Bitcoin's inventory message. We further present a TCCM instantiation and formally prove its unobservability. We instant TCCM to evaluate its performance on the Bitcoin mainnet. We also discuss its scalability, real-world use cases, and ethical considerations. Experimental outcomes demonstrate that the proposed instantiation is unobservable and able to transmit 100-MB files at a cost of $1.47.

Covert channels serve the construction of cyberspace security. By realizing the secure transmission of data, it is widely used in political and financial fields. Blockchain covert channels have higher reliability and concealment compared to traditional network-based covert channels. However, existing blockchain covert storage channels need to create a large number of transactions to transmit covert information. Creating transactions requires a transation fee, which means that the implementation of blockchain covert storage channels requires a high cost. Besides, created transactions remain on-chain permanently, leading to the threat of covert information being detected. To overcome these limitations, we propose a blockchain covert timing channel framework. Specifically, we utilize inv and getdata messages in the Bitcoin transaction broadcast as carriers and propose three modulation modes to achieve covert channels without cost and leaving no trace. We evaluate the concealment of our modes by K-S, KLD tests, and machine learning approaches. Experimental results show the indistinguishability between traffic carrying covert information and normal traffic. Our channels promise a capacity of 2.4 bit/s.

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.