Complex event processing faces significant challenges in real-world scenarios where noisy event streams containing missing events, misordered sequences, and erroneous attribute values are prevalent. Existing approaches address only specific aspects of this problem, with approximate matching techniques handling missing events and out-of-order processing methods addressing sequence misalignment, but none provide a comprehensive solution. This paper introduces K-NFA, a novel fault-tolerant automaton that systematically handles all major error types in complex event matching. Our approach features a compact automaton structure with specialized transitions for event deletion and swap operations, coupled with a dynamic matching algorithm that efficiently processes noisy streams while respecting temporal and attribute constraints. Experimental results show our method improves fault-tolerant matching capacity by 32.91% on average over existing approaches, while maintaining competitive efficiency as the first unified framework for robust complex event matching.