Phishing attacks present a critical cybersecurity threat, with global financial losses exceeding USD 70 million in 2024. Modern machine-learning-based detection methods achieve high accuracy but have fundamental limitations, including lack of interpretability, significant computational requirements, and high energy consumption, which restrict their use in resource-constrained environments. This research presents a novel deterministic approach based on Boolean algebra for detecting phishing URLs. The method employs a 15-dimensional Boolean feature space covering structural, protocol, content-based, infrastructure, and reputation-based features, formalized as mathematically rigorous logical rules. Experimental evaluation which based on a balanced dataset of 50,000 URLs demonstrated a classification accuracy of 89.1% along with substantial operational advantages: processing latency of approximately 1 ms (24–69× faster), power consumption of 4.8 mW (108–250× lower), and full decision interpretability unlike machine learning methods. The proposed Boolean approach enables transparent, energy-efficient, and high-performance threat detection suitable for real-time cybersecurity applications, establishing a foundation for next-generation security systems with verifiable detection mechanisms. The proposed system is not intended to replace advanced ML-based detection systems; rather, it serves as an additional first line of defense, providing rapid initial filtering with minimal resource overhead and forwarding complex or borderline cases to ML systems for secondary verification.
Babala et al. (2026) studied this question.