The paper addresses the challenge of detecting distributed denial of service (DDoS) attacks in smart grid networks, which are vulnerable due to the presence of multi-directional communication devices. The authors propose a reconstructive deep learning approach that can integrate new attack classes without retraining the entire system, minimizing disruptions. They trained several deep and shallow reconstructive models to learn representations for each attack type and used a class-specific reconstruction error-based classification method for DDoS detection. The technique was evaluated using two standard databases for DDoS attacks and compared with six other methods. The results show that the proposed method achieved higher accuracy and eliminated the need for complete model retraining when new attack classes are introduced. This approach enhances the security, stability, and reliability of smart grid networks by effectively detecting and mitigating DDoS attacks.The paper addresses the challenge of detecting distributed denial of service (DDoS) attacks in smart grid networks, which are vulnerable due to the presence of multi-directional communication devices. The authors propose a reconstructive deep learning approach that can integrate new attack classes without retraining the entire system, minimizing disruptions. They trained several deep and shallow reconstructive models to learn representations for each attack type and used a class-specific reconstruction error-based classification method for DDoS detection. The technique was evaluated using two standard databases for DDoS attacks and compared with six other methods. The results show that the proposed method achieved higher accuracy and eliminated the need for complete model retraining when new attack classes are introduced. This approach enhances the security, stability, and reliability of smart grid networks by effectively detecting and mitigating DDoS attacks.