In end-to-end data sharing, data are directly distributed to data receivers and stored on their terminals, making it hard to ensure forward security because receivers whose permissions have been revoked may still access previously shared data. To address these challenges, we propose an attribute-key-free CP-ABE scheme, aimed at securely binding data with access policies while ensuring forward security. Specifically, the decryption process in our scheme is delegated to the attribute authorities, which adopt the user’s real-time attribute values to decrypt the ciphertext. To prevent the honest-but-curious attribute authorities from accessing the plaintext, the ciphertext is re-encrypted with a one-time key before being sent to the attribute authorities. Furthermore, to prevent sensitive information from being inferred through the policy, we design a policy-hiding mechanism to conceal attribute values. Through these mechanisms, it can be ensured that the data subject always has control over his or her personal data during the end-to-end data-sharing process. We evaluate the performance of our scheme through both theoretical analysis and comparative experiments, and the results show our scheme’s effectiveness.

As cross-domain access constitutes a significant portion of network communication, multi-domain networks present both enhanced capabilities and increased cybersecurity risks. Traditional defense strategies often overlook the complexities of cross-domain collaboration, particularly the strategic interactions among domains that prioritize their own interests. In this paper, we introduce Macd, a multi-domain collaborative defense framework, which models the defense interactions as a multi-agent stochastic game. This enables Macd to consider long-term security performance across domains, mitigating multi-step attack threats. To promote effective collaboration, we propose a Shapley-value based reputation mechanism to ensure fair incentives for non-attacked domains that contributing Security Service Functions (SSFs). Additionally, we implement a MAPPO-based Macd-solver to dynamically compute optimal defense strategies. Simulations in a DDoS attack-defense scenario demonstrate that Macd significantly enhances cross-domain collaboration and improves the overall security of multi-domain networks.

Edge computing is witnessing a convergence of cloud data centers and edge clouds, thereby the large thereby intensifying the vulnerability of cloud services from multi-vector DDoS attacks. However, existing DDoS filtering approaches, characterized by independent offline decisions made by clouds, exhibit shortcomings in efficacy and real-time performance. This paper proposed an online collaborative mitigation framework for multi-vector DDoS attacks, which formulates the mitigation challenge as an Online Multi-dimensional Multiple-Choice Knap-sack Problem (O-MdMCKP). Further, the framework generates candidate filtering policies for each incoming attack flow and designs a policy selection algorithm by employing online analysis based on reservation functions, ensuring prompt and efficient filtering. Experimental results show the proposed algorithm outperforms other online benchmark methods.

In an increasingly interconnected world, multi-domain networks serve as vital infrastructure, enabling seamless communication and resource sharing across diverse sectors, but also leading to increasingly frequent cyberattacks. Defense-in-depth (DiD) is widely regarded as a necessary strategy for mitigating these threats through layered security measures. However, current DiD strategies often fall short due to their single-domain focus, reliance on centralized control, and inability to adapt to dynamic threats. This paper proposes a novel framework to extend DiD strategies for multi-domain networks. It progressively defines key elements of multi-domain networks, culminating in a detailed hierarchical framework that clarifies the roles and interactions of management domains. Furthermore, cross-domain intrusion response is modeled as a multi-agent stochastic game, accounting for self-interested behavior and interactions between domains. The Independent Q-Learning (IQL) algorithm is employed to solve this game, with experimental results demonstrating substantial improvements in security across multi-domain environments.