A reinforcement-guided multi-phase hybrid architecture for threat profiling and defense towards IoT handheld device

Abstrak

The contribution of artificial intelligence (AI) towards offering proactive security in handheld devices of internet of things (IoT) is in evolving stage. Review of literature showcases noteworthy attempts of machine learning (ML) and deep learning (DL) models; however, they are a large scope of improvement towards bridging the trade-off between security and computational-communication efficiency. This problem is addressed in this manuscript by presenting a unique and innovative solution where reinforcement learning (RL) has been hybridized with standalone ML and DL models. The model reads the permission-based data in cloud, followed by vulnerability prediction carried out by hybridization of RL and logistic regression (LR). Further, RL is integrated with deep neural network (DNN) for exploring a secure path to facilitate data transmission. The proposed model witnessed 97.9% accuracy, 67.35% of higher accuracy, 55.14% of reduced latency, and 52.54% of faster response time in contrast to baselines.