Popular chained Byzantine Fault Tolerant (BFT) consensus protocols adopt a pipelined paradigm to achieve efficiency. However, these protocols remain vulnerable to critical attacks, such as forking and partitioning. While the two-phase commit rule can prevent forking, it often requires complex or expensive view-change mechanisms. Moreover, our study confirms that partitioning attacks are feasible in many existing chained BFT protocols. This paper devised NunChuck, a fully two-phase chained BFT protocol that ensures linear authenticator complexity, optimistic responsiveness, and resilience to partitioning attacks. NunChuck employs a lightweight certificate-based view-change mechanism to maintain efficiency, and introduces a unified message structure that enables leaders to make progress even under partitioned network conditions. We formally prove its safety and liveness properties. Furthermore, we develop a prototype implementation in Golang and conduct extensive evaluations. The results demonstrate that NunChuck consistently outperforms HotStuff and FastHotStuff, particularly in adversarial environments, with simulated delay analysis further corroborating our experimental findings.

The completion of missing network traffic is of great significance for network operation and maintenance. In recent years, the low-rank tensor completion (LRTC) techniques based on tensor ring (TR) decomposition have attracted much attention. In general, the LRTC model requires the stability of the whole tensor space. However, continuous mutation of network traffic is very common in real networks. At this time, existing completion work has difficulty in capturing the global low-rank feature of normal data and ignores the local continuous feature of mutation data, leading to a decrease in completion performance. To solve the above problems, we propose a low-rank tensor completion model that can adapt to various continuous mutation patterns of network traffic. The original tensor is represented as the sum of a normal tensor and a mutation tensor to extract their features respectively. Then, an algorithm based on the alternating direction method of multipliers (ADMM) is developed to solve the proposed model. Finally, our experimental results on both synthetic and real datasets indicate that our model can adapt to various missing data completion under different continuous mutation patterns, and has more accurate completion performance compared to advanced models.

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.

Hybrid distributed denial-of-service (DDoS) attack, which utilizes multiple types of DDoS attack to launch one attack event, has become more rampant. However, existing researches for DDoS attack detection mainly focus on the single attack scene and ignore the hybrid attack incident. To deal with the hybrid DDoS attack detect problem, we propose a distributed and collaborative DDoS detection framework(DICOF) to detect and classify multiple DDoS attack simultaneously. Firstly, we propose an entropy-based method to quickly identify the DDoS attack events by measuring the distribution of the total length of inbound and outbound packets for network traffics. Then, we adopt a GRU(Gated Recurrent Unit) based classification method to distinguish the type of different DDoS attacks contained in one attack event. Experiment results show that the DICOF is able to detect hybrid DDoS attack events at millisecond level and classify different DDoS attacks precisely.