With the rapid development of Optical Networks, ensuring their security against sophisticated cyber attacks has become increasingly challenging. In this paper, we propose a novel path graph-based framework for attack-induced fault diagnosis by modeling system logs as a path graph, where nodes represent individual log entries and edges capture sequential and contextual relationships between adjacent logs. This modeling approach effectively encodes temporal dependencies while avoiding the high computational complexity associated with Transformer-based models. To further enhance fault detection accuracy and interpretability, we design a custom graph neural network (GNN) architecture that leverages both local and global structural information within the path graph to identify anomalies indicative of malicious activities. Experimental results demonstrate that the proposed framework provides a scalable and effective solution for real-time security assessment in Optical Networks, enabling early diagnosis of attack-induced faults with reduced computational overhead.