FlowerSec: An AI-Enhanced Security Framework for Clustered Wireless Sensor Networks

Source abstract

Energy constraints along with scalability and vulnerability to several malicious attacks including Blackhole, Grayhole, Flooding and Scheduling attacks, are serious impediments for wireless sensors networking deployment. Therefore, we present FlowerSec, a novel topology design for wireless sensor networks along with an AI enhanced security scheme which has the ability to overcome afore stated downsides by design. The sensor network is configured on a hierarchical topology of “Flowers,” each comprising a central, resource-rich “Seed” node (Cluster Head) and up to four “Leaf” nodes. The security boundaries that emerge naturally within such topology along with the ease of operating the energy efficient sleep mode, has given us use to propose a scheme which has high-security guarantees with minimal energy loss. Hence a lightweight supervised AI based Intrusion detection System is deployed at the Seed node. Our LightGBM based model with SHAP feature selection that selects only the top 8 network features can obtain an optimal Macro F1-score of 0.9494 on WSN-DS real world dataset (number of instances are 374,661). The classification at CH occurs at 0.0066 ms per sample proving this is a computationally cheap method and can be performed at resource-constrained nodes such as CHs. Beside this, the supervised engine is complemented with an unsupervised AutoEncoder which is trained on pure normal traffic and able to isolate the benign traffic with reconstruction loss difference of >300 times (AUC-ROC score is 0.9819). We adopt confidence gated escalation protocol which sends the packets having low confidence scores of the supervised engine for detection of novel unseen attacks at a small bounded energy cost of the unsupervised engine.