Internet of Things (IoT) is a way to communicate with the real world without much human involvement. It is booming in today's computing world, with billions of devices having sensors and actuators connected to the internet using various low power technologies. Despite several profits, it experiences multiple security threats that impel catastrophic crashes in the IEEE 802.15.4e (6TiSCH) network. Various threats like jamming attacks, DDoS, abnormal behavior, etc., are detected using multiple Machine Learning (ML) and Deep Learning (DL) approaches. In this paper, an edge-based ML enables Intrusion Detection Systems (IDS) is proposed to detect distributed denial-of-service (DDoS) attack patterns from a particular source. Experimental outcomes confirm that the proposed approach is scalable and efficient in terms of computation and storage. Hence, the intended approach gives a faster response as (24.2- 68.9) Sec. The average memory utilization (ROM/RAM), energy usage, and accuracy achieved by our intended solution are 35834B/5378B, 85916mJ, 98.7%, respectively, which outperform closely related work.