Cyber threat hunting early hunts for cyberattacks hidden by conventional defence tools. It inspects extreme to recognize mischievous programs (i.e., malware) that evade recognition. It is significant because complicated cyberattacks can evade the mechanisms of cyber security. The performance of cyberattack hunting is enhanced over artificial intelligence (AI), particularly explainable AI (XAI), which includes a trust module to the cyberattack hunting procedure. Owing to the addition of XAI, security specialists obtain complete descriptions of perceived attacks as the recognition method in XAI is recognized. Information, like attack, how it was noticed, and why it was identified, is attained very effortlessly owing to XAI in the hunting process of cyberattack. AI, mainly over machine learning (ML) and deep learning (DL) approaches, has exposed promising latent in progressing cybersecurity measures. Recently, the growth of the blockchain (BC) method has indicated a route value in solving the distributed trusted problem in the Internet of Things (IoT) platform. So, this manuscript presents a novel Two-Tier Optimization Algorithms for Cyberthreat Detection and Mitigation Using Explainable Artificial Intelligence with Recurrent Neural Networks (TTOCDM-XAIRNN) methodology. The main intention of the TTOCDM-XAIRNN algorithm framework is to improve the detection and mitigation of cyber threats in dynamic environments. The BC technology is utilized for safe inter-cluster data transmission methods. The presented TTOCDM-XAIRNN model initially employs data preprocessing with a linear scaling normalization (LSN) model to standardize the input features for improved model performance. The pelican optimization algorithm (POA) model is employed for dimensionality reduction to identify the most relevant data attributes. Furthermore, the hybrid attention-based long short-term memory and bidirectional gated recurrent unit (A-LSTM-BiGRU) technique is utilized for cyber threat detection. Finally, the earthworm optimization algorithm (EOA) is implemented to tune the hyperparameters and ensure the model's parameters are optimized for superior detection and mitigation capabilities. Finally, XAI with SHAP presents transparent insights into model decisions, ensuring high performance and a clear understanding of the threat mitigation process. A wide range of simulation studies of the TTOCDM-XAIRNN approach is examined under the NSLKDD and CICIDS 2017 datasets. The comparison study of the TTOCDM-XAIRNN approach portrayed a superior accuracy value of 98.34% and 98.87% under dual datasets.
Alohali et al. (Tue,) studied this question.