Neighborhood area network is a robust communication model essential for disturbance-free power distribution. The reliability of the network depends on the flow-based attack detection model but the lengthy flow completion introduces high latency. This potential delay buys the time for attackers to study and pose subsequent attacks. Thus, the packet-based analysis is utilized in this work to detect the attacks at early stages. The proposed Intrusion Detection System (IDS) is designed with deep learning based Bidirectional Long Short-Term Memory algorithm and attention mechanism. IDS, with multiheaded attention, is executed in the substation to analyze the consolidated collected data of the traffic and detect coordinated attacks in the network. The developed model works effectively in earlier attack detection and the secondary level is used only on requirement. The proposed IDS is evaluated with standard datasets like 5G_NIDS, CICIDS2017 and UNSW-LD. The results proved the efficiency of the proposed method in of Neighborhood Area Network (NAN) coordinated attacks detection of smart grid communication.

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