A multidimensional-multilayered anomaly detection in RFID-sensor integrated internet of things network
Adarsh Kumar, Deepak Kumar Sharma · International Journal of Cloud Computing · 2021
Outlier detection in a single dimension is not enough to protect the network from known and unknown attacks. There is a strong need to apply multiple steps at multiple stages for combating these attacks. There are various approaches to protect the network from malicious activities and it is an ongoing process to fight against them from a multilayer perspective parallel to networking layering models. This work has considered a multilayered and multi-dimensional approach to protect the hierarchical mobile ad hoc network (MANET) from known and unknown attacks. The proposed multilayered approach consists of ultra-light, light and heavy computational overhead-based outlier detection approaches to combat the attacks. It has been observed that these approaches apply threshold as well as earning processes-based mechanisms for fighting against the attacks. Further, hardware constraint is considered to identify these schemes as ultra-light, light, or heavy. Simulation results show that variations of nodes from 10 to 5,000 vary the cluster formation from 5 to 53 with an error rate of 0.4%.