Lyrebird Green Anaconda Optimization based Bayesian Hierarchical Neural Attention Harmonic Network for Illicit Dark Web Classification - Babu, Bollikonda Vinod; D., Kiran K V (Journal of Trends in Computer Science and Smart Technology, 2025)
The quick development of internet technology has opened the door for a number of illegal activities targeted at users. The fact that these malevolent actions are usually carried out by anonymous people or organizations makes identification and tracking more difficult. In order to address these problems, a novel method for categorizing illegal dark content has been created called the Lyrebird Green Anaconda Optimization-based Bayesian Hierarchical Neural Attention Harmonic Network (LGAO_BHNAHN). To find and extract pertinent information, textural content extraction is done first. Following that, GPT-NEOX receives the extracted contents and uses them to process and produce text or passages. The Bayesian Hierarchical Neural Attention Harmonic Network (BHNAHN) is then used to classify illicit dark web content. However, the Bayesian Neural Network (BNN) and the Hierarchical Neural Attention classifier with Forward Harmonic analysis are combined to create BHNAHN. Additionally, Lyrebird Green Anaconda Optimization (LGAO), which combines the Lyrebird Optimization Algorithm (LOA) and Green Anaconda Optimization (GAO), is used to train BHNAHN. Lastly, GLOA-trained HNAHN is used to classify drug and arms types. The proposed framework makes a significant advancement in secure, real-time threat detection by combining GPT-NEOX with a novel Bayesian Hierarchical Neural Attention Harmonic Network optimized by the Lyrebird Green Anaconda Algorithm (LGAO). It achieves 93.30% accuracy, with a False Positive Rate (FPR) of 5.62% and a True Positive Rate (TPR) of 92.85% in classifying illicit dark web content.
Keywords: Dark Web; Illicit Dark Web Classification; Hierarchical Neural Attention Classifier; Lyrebird Optimization Algorithm; Green Anaconda Optimization