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To ground our research in real-world conditions, we utilized a high-quality dataset derived from the ISCX-URL2016 benchmark. This dataset contains over 11,000 unique samples, providing a robust foundation for our training and testing phases.
Each URL in our study is represented by a broad range of lexical and structural features, including length-based metrics, special character counts, and entropy. Crucially, the dataset is almost perfectly balanced, with over 5,500 phishing and 5,500 legitimate URLs. This balance ensures that our model learns to distinguish between classes without bias, providing a challenging yet fair environment to prove the effectiveness of our BiLSTM and hEVOSWO approach. |
IEEE/ICACT20260103 Slide.05
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