Internet of things has marked its dominance in multiple domains. One cannot deny their heavy influence and reliance on our day-to-day lives. From smart home gadgets to big sensor devices capable of automating industrial processes, this technology is widely growing and revolutionizing all around the globe. However, their wide popularity has exposed them to potential cyber threats. The major attributes of these devices which make them highly vulnerable to attacks are their easy availability, elementary configuring steps, less secure frameworks etc. In this paper, an in-depth analysis of novel and comprehensive dataset i.e. Edge IIoT dataset has been done. Data generation methods, tools for executing attacks, devices used for creating IoT testbed are consciously observed. Stacked LSTM which is a prominent deep Learning algorithm have been employed for both binary and multiclass classification. It has been observed that DL algorithms have significantly performed better in field of IoTs attack detection and presented good results with an overall accuracy of 100% and 91.6% for binary and multiclassification respectively.