Abstract
Wireless sensor networks (WSNs) and the advancement into software-defined
wireless sensor networks (SDWSNs) are crucial components in the rapid evolution
of the Internet of Things (IoT), with applications demanding secure and efficient
task execution. Despite their potential, these networks face significant security
challenges due to their vulnerability to various attacks, making the implementation
of effective intrusion detection systems (IDSs) and intrusion prevention systems
(IPSs) essential. To address the challenges in IoT-enabled SDWSN environments,
this research introduces a new model with a honeycomb structure-based intrusion
detection and prevention system (IDPS). In this system, there is a hybrid
IDS, secure authentication using the three-dimensional cube (3D cube) algorithm,
modified honeycomb-based network partitioning, clustering, reinforcement learning
(RL)-based intelligent routing with a transfer learning-based deep Q network
(TLDQN), and honeycomb-based network partitioning. A driver training-based
optimization (DTO) algorithm and a bidirectional generative adversarial network
(Bi-GAN) algorithm are used as the last methods to detect malicious nodes and intrusions.
Additionally, this research also focuses on preprocessing, feature extraction,
and intrusion recognition using the network security laboratory-knowledge
discovery in databases (NSL-KDD) dataset in order to evaluate intrusion detection
in IoT-enabled SDWSNs. Feature extraction is facilitated by the novel
exponential grey wolf-optimized grid search algorithm (EGWOGSA), while feature
selection is achieved using the novel symmetric gradient Boruta for enhanced
feature selection algorithm (SGBFSA). Intrusion detection is executed through
a gated bidirectional recurrent convolutional neural network (GBR-CNN) algorithm.
This research contributes to the advancement of security in IoT-enabled
SDWSNs through advanced feature optimization and artificial intelligence (AI)
techniques. The simulation results demonstrate the effectiveness of the proposed
work in detecting network intrusions with high accuracy, outperforming existing
methods across most measures, such as energy consumption, throughput, and
computational overhead, while maintaining competitive performance in packet delivery
ratio and network time, thereby demonstrating its effectiveness in securing
IoT-enabled SDWSN environments.