Strategic mining attacks, such as selfish mining, exploit blockchain consensus protocols by deviating from honest behavior to maximize rewards. Markov Decision Process (MDP) analysis faces scalability challenges in modern digital economics, including blockchain. To address these limitations, reinforcement learning (RL) provides a scalable alternative, enabling adaptive strategy optimization in complex dynamic environments. In this survey, we examine RL’s role in strategic mining analysis, comparing it to MDP-based approaches. We begin by reviewing foundational MDP models and their limitations, before exploring RL frameworks that can learn near-optimal strategies across various protocols. Building on this analysis, we compare RL techniques and their effectiveness in deriving security thresholds, such as the minimum attacker power required for profitable attacks. Expanding the discussion further, we classify consensus protocols and propose open challenges, such as multi-agent dynamics and real-world validation. This survey highlights the potential of reinforcement learning to address the challenges of selfish mining, including protocol design, threat detection, and security analysis, while offering a strategic roadmap for researchers in decentralized systems and AI-driven analytics.

Proof-of-Stake (PoS) is an energy-efficient consensus where proposer election based on validator stakes causes centralization, especially in stake pools. However, existing reward allocation mechanisms aim to address centralization but increase the risk of Sybil attacks. Therefore, designing a reward allocation mechanism for stake pools that reconciles decentralization and Sybil resilience remains a key challenge. In this paper, we propose Dream-SR, a reward allocation mechanism established with formal function properties. Dream-SR enhances decentralization by imposing reward constraints to limit the dominance of large stake pools. Sybil resilience is ensured by aligning reward allocation with both the economic incentives and the influence, effectively addressing utility-maximizing and influence-maximizing Sybil attacks. Furthermore, we leverage a multi-leader-multi-follower (MLMF) Stackelberg game model to capture the interactions between validators and users regarding commission pricing and stake delegation within stake pools. The game model is used to analyze the impact of the reward allocation mechanism on the strategies of participants and the system equilibrium. Compared with other relevant mechanisms, numerical results reveal that Dream-SR effectively improves decentralization and resilience to Sybil attacks at equilibrium.

Link flooding attacks (LFAs) have always been a security concern as the impact of volumetric attacks on transit links are increasingly severe. Capacity expansion, while being effective in combating LFAs, involves considerable deployment costs. Therefore, how to efficiently manage the link resource among spatio-temporal dynamic customers remains a challenge for Internet service providers (ISPs). In this paper, we study the differential pricing strategy for bandwidth allocation with LFA resilience by leveraging a multi-leader-multi-follower (MLMF) Stackelberg game approach. Based on network capabilities, we employ pricing approaches instead of empirical assignments to regulate the privileged channel allocation, economically facilitating the domain-level resource coordination. And we formulate the bandwidth pricing and allocation decision problem by using a Stackelberg game-theoretic approach to capture the interactions between providers and customers. Then, we analyze the Stackelberg game equilibrium of uniform pricing strategy and differential pricing strategy, where differential pricing is applied to adjust the prices that individual customers receive according to heterogeneous factors. Furthermore, we give the optimal solution derivation in the case of differential pricing. By comparing with other relevant pricing strategies, our numerical results show that the differential pricing strategy achieves congestion-free property and provides enough incentives for ISPs to deploy.

As the flourishing of cloud services, data centers are widely invested and deployed. However, facing the hazard of Distributed Denial of Service (DDoS) attacks, legitimate users' bandwidth access to a data center is not yet a guarantee. In response, capability-based DDoS defenses provide a promising countermeasure, especially when leveraging Autonomous System (AS) as a geographic constraint to throttle attacking flows. Unfortunately, previous schemes essentially involve a source-AS fair sharing strategy, which is too coarse- grained to provide fairness among heterogeneous AS entities. This paper proposes D4, a capability-based data center protection with state-defined allocating granularity. We differentiate the states of stub ASes through diverse aspects of domain attributes. D4 enables fair bandwidth allocation among source domains and scalable data center access for users. Our scheme is integrated with Border Gateway Protocol and can be practically deployed by Internet data centers. We illustrate the effectiveness of D4 via experiments under different scenarios and comparative simulations with closely related schemes.