TRUXL: TRUST-BASED SECURE ROUTING AGAINST RPL ATTACKS IN IOT USING XGBOOST WITH CNN-LSTM

One of the most popular routing protocols in Internet of Things (IoT) contexts is the Routing Protocol for Low-Power and Lossy Networks (RPL). However, RPL is extremely vulnerable to a number of security risks that jeopardize data integrity and network dependability, including as wormhole, Sybil, blackhole, and rank assaults. This research suggests TRUXL (Trust-Based Routing using XGBoost and CNN-LSTM), a hybrid machine learning (ML) and deep learning (DL) architecture for trust-aware secure routing in Internet of Things networks, as a solution to these problems. The TRUXL model combines a CNN-LSTM hybrid technique for dynamic trust score prediction with XGBoost for trust-based node classification. While CNN extracts geographical trust patterns and LSTM captures temporal trust changes for improved anomaly detection and attack mitigation, XGBoost efficiently classifies IoT nodes based on energy levels, packet forwarding behavior, and historical anomalies. Real-time trust evaluation is used to develop a secure routing technique that mitigates security vulnerabilities by dynamically choosing the most dependable paths. The suggested model's performance is compared to that of DBN-TSP (Deep Belief Network with Trust Score Propagation) and RF-Trust (Random Forest-Based Trust Model) utilizing OMNeT++ with the INET Framework. Packet Delivery Ratio (PDR), End-to-End Delay, Routing Overhead, Anomaly Detection Accuracy, Trust Score Stability, and Attack Mitigation Rate are among the evaluation measures. According to experimental data, TRUXL performs better than conventional trust-based routing models in RPL-based IoT networks, achieving enhanced routing efficiency, adaptive security, and higher attack detection accuracy. The suggested TRUXL offers a clever, scalable, and reliable solution for secure IoT routing by combining CNN-LSTM for trust prediction with XGBoost for classification. This greatly improves trust-aware communication in resource-constrained contexts.