The growing digitalization of Industrial Control Systems (ICSs) presents both significant benefits and security challenges, especially for small and medium-sized factories with limited resources. Effective anomaly detection is essential to safeguard these facilities and prevent costly disruptions. Although current research has advanced anomaly detection, it is still challenging for algorithms to be capable of effectively balancing the interplay between training speed, computational cost, and accuracy while simultaneously exhibiting robust stability and adaptability. This gap often leaves small and medium-sized factories without efficient solutions. To address these issues, this work introduces a deep belief network-based boosting adversarial autoencoder termed DBN-BAAE, a novel lightweight anomaly detection mechanism based on boosting adversarial learning. The proposed lightweight mechanism saves computational overhead, enhances autoencoder training stability with an improved deep belief network (DBN) for pre-training, boosts encoder expression through ensemble learning, achieves high detection accuracy via an adversarial decoder, and employs a dynamic threshold to enhance adaptability and reduce the need for retraining. Experiments reveal that the mechanism not only achieves an F1 score of 0.82, surpassing the best baseline by 1%, but also accelerates training speed by 2.2 times, demonstrating its effectiveness and efficiency in ICS environments, particularly for small and medium-sized factories.