Edge-of-Things (EoT) enables the seamless transfer of services, storage and data processing from the Cloud layer to Edge devices in a large-scale distributed Internet of Things (IoT) ecosystems (e.g., Industrial systems). This transition raises the privacy and security concerns in the EoT paradigm distributed at different layers. Intrusion detection systems are implemented in EoT ecosystems to protect the underlying resources from attackers. However, the current intrusion detection systems are not intelligent enough to control the false alarms, which significantly lower the reliability and add to the analysis burden on the intrusion detection systems. In this article, we present a DaaS, Dew Computing as a Service for intelligent intrusion detection in EoT ecosystems. In DaaS, a deep learning-based classifier is used to design an intelligent alarm filtration mechanism. In this mechanism, the filtration accuracy is improved (or sustained) by using deep belief networks. In the past, the cloud-based techniques have been applied for offloading the EoT tasks, which increases the middle layer burden and raises the communication delay. Here, we introduce the dew computing features which are used to design the smart false alarm reduction system. DaaS, when experimented in a simulated environment, reflects lower response time to process the data in the EoT ecosystem. The revamped DBN model achieved the classification accuracy up to 95%. Moreover, it depicts a 60% improvement in the latency and 35% workload reduction of the cloud servers as compared to Edge intrusion detection system.