Side-channel attack, which exploits time information leakage during the digital signature generation process, poses a severe threat to the confidentiality and integrity of transactions in blockchain payment channels. Currently, Transformer-based detection methods are effective at capturing long-term dependencies, while Long Short-Term Memory (LSTM) networks excel at modeling short-term dynamic time-series features. However, existing approaches struggle to uniformly model both long-term and short-term time-series features, limiting their performance in anomaly detection for complex transaction sequences. In this article, we propose TLD-SCA, a novel side-channel attack detection model that innovatively integrates Transformer’s capability for global dependency modeling with LSTM’s advantage in capturing local time-series dynamics. This enables long-term and short-term time-series analysis of transaction timing data. Experimental results demonstrate that TLD-SCA significantly outperforms existing methods in terms of accuracy (99.5%), precision (99.2%), and recall (98.3%), thereby providing a higher level of security assurance for blockchain payment channels.

Source location privacy (SLP) protection is an emerging research topic in wireless sensor networks. Because the source location represents the valuable information of the target being monitored and tracked, it is of great practical significance to achieve a high degree of privacy of the source location. Although many studies based on phantom nodes have alleviates the protection of SLP to some extent. It is urgent to solve the problems, such as complicate the ac path between nodes, improve the centralized distribution of phantom nodes near the source nodes and reduce the network communication overhead. In this paper, protection scheme based on sector phantom routing (PSSPR) routing is proposed as a visible approach to address SLP issues. We use the coordinates of the center node V to divide sector domain, which act an important role in generating a new phantom node. The phantom nodes perform specified routing policies to ensure that they can choose various locations. In addition, the directed random route can ensure that data packets avoid the visible range when they move to the sink node hop by hop. Thus, the source location is protected. Theoretical analysis and simulation experiments show that this protocol achieves higher security of source node location with less communication overhead.

With the development of network technology, privacy protection and users anonymity become a new research hotspot. The existing blockchain privacy-aware public key infrastructure (PKI) model can ensure the privacy of users in the authentication process to a certain extent, but there are still problems of the storage and leakage of users' keys. This paper first proposes a strong forward-secure ring signature scheme based on RSA, which ensures the anonymity of the signing users and the forward-backward security of the keys. Then, by introducing the ring signature technology into the privacy-aware PKI model, this paper proposes a privacy-aware PKI model with strong forward security based on block chains, which not only ensures the users' identity privacy, but also solves the problem of the storage and leakage of the users' keys, greatly improving the success rate and security of the users' identity authentication. Finally, this paper applies the proposed PKI model to anonymous transactions, designs a privacy-aware anonymous transaction model with strong forward security, realizing anonymous transactions without relying on trusted third parties, and implementing users' privacy protection.

Selfish mining attacks get a high prize due to the additional rewards unproportionate to their mining power (mining pools have particular advantages). Generally, this category of attacks stresses decreasing the threshold to maximize the rewards toward the view of attackers. Semi-selfish mining falls into the family of selfish mining attacks, where the threshold value is approximately 15%. However, it gets little attention to implement these attacks in practical. In this paper, we focus on the validity of semi-selfish mining attacks considering the probability of being detected. More specifically, we discuss mining strategies through backward deduction. That is to say that the attacking states derived from the observable states, which with normal forking rate, just as without semi-selfish mining attacks, toward the view of the honest miners. Rewards distribution is further investigated concerning these strategies. The simulation results indicate that it does not necessarily bring rewards advantage over large pools. Instead, the small pools have an advantage over the additional rewards. However, the probability for small pools to successfully implement these strategies is pretty low. That is, it is impossible for the pools, although profitable for them, to sponsor semi-selfish mining attacks without being detected.

Transaction data on the public chain is open and transparent to all participants, which poses a potential threat to the privacy of participants. Some privacy-conscious token holders want to employ obfuscation methods to protect the origin and destination of their tokens, and the need for token shuffling services (TSS) arose at a historical moment. The prevailing token shuffling policies rely too heavily on blacklists in the token shuffling process, which is contrary to the idea of decentralization. Therefore, weakening or even eliminating the usage of blacklisting mechanisms in TSSs, which is an urgent issue to be addressed. In this paper, we adopt the idea of machine learning and propose a general framework for TSSs, which replaces the natural role under incomplete information with machine learning, so as to achieve the goal of eliminating the blacklist mechanism in TSSs. Then, the token shuffling process is constructed as an extended game under incomplete information on the basis of different token shuffling policies, and this incomplete information game is analyzed on the basis of poison policy, haircut policy, and suicide policy respectively. Finally, the sequential equilibrium under different games is investigated through simulations. The simulation results show that in the incomplete information game based on the poison policy, the participation of two players in the TSS is a sequential equilibrium, while in the incomplete information game based on the haircut policy, the players do not participate in the TSS is an sequential equilibrium.

Selfish mining attacks sabotage the blockchain systems by utilizing the vulnerabilities of consensus mechanism. The attackers' main target is to obtain higher revenues compared with honest parties. More specifically, the essence of selfish mining is to waste the power of honest parties by generating a private chain. However, these attacks are not practical due to high forking rate. The honest parties may quit the blockchain system once they detect the abnormal forking rate, which impairs their revenues. While selfish mining attacks make no sense anymore with the honest parties' departure. Therefore, selfish miners need to restrain when launch selfish mining attacks such that the forking rate is not preposterously higher than normal level. The crux is how to illustrate the attacks toward the view of honest parties, who are blind to the private chain. Generally, previous works, especially those using Markov decision processes, stress on the increment of attackers' revenues, while overlooking the detection on forking rate. In this paper, we propose, to maintain the benefit from selfish mining, an improved selfish mining based on hidden Markov decision processes (SMHMDP). To reduce the forking rate, we also relax the behaviors of selfish miners (also known as semi-selfish miners), who mine on the private chain, to mine on public chain with a small probability ρ. Simulation results show that SMHMDP can trade off between revenues and forking rate. Put differently, selfish miners benefit from attacking within an acceptable forking rate toward the view of honest parties, without leading selfish mining attacks to be an armchair strategist.

vMixing coins strategy can realize the anonymity of user information, thereby protecting the user's privacy. Ideally, the blacklist is public information and all bad coins are recorded in it. However, due to the failure of some bad coins to be registered in the blacklist in time, users can only obtain part of the blacklist information, which allows illegal criminals to take advantage of it. How to prevent illegal activities under the partial information blacklist and how to design coins' quality updating rule rationally have become open issues in mixing coins. The updating rule of coins' quality in mixing is addressed since illegal criminals may carry out illegal activities, for example, money laundering. ImpSuic, an improved suicide strategy, is proposed as a new quality updating rule. The intuition is: all coins of the one who has the highest bad coins according to the blacklist, are recorded as bad coins. On the other hand, the coins' quality of others remain unchanged. Besides, linear programming is introduced into ImpSuic strategy to predict the maximum utility after mixing coins, which facilitates users to make reasonable decisions before mixing coins. Simulation results show that the quality updating rule in ImpSuic strategy can preserve users' privacy and antimoney launder.