A Lightweight Hybrid MLP-Based Framework for Real-Time Phishing URL Detection Using Structural URL Features
arXiv:2606.00889v1 Announce Type: cross Abstract: Phishing attacks remain a major cybersecurity threat, exploiting deceptive URLs to steal sensitive user information. Traditional blacklist and rule-based detection approaches are reactive and often fail to identify newly emerging phishing URLs. This paper proposes a lightweight hybrid framework for real-time phishing URL detection that combines blacklist-based screening with a Multi-Layer Perceptron (MLP) classifier operating solely on structural URL features. The framework extracts 16 URL-derived features capturing structural, domain-based, an
The continuous evolution of sophisticated phishing attacks, coupled with the limitations of traditional detection methods, necessitates more adaptive, real-time solutions.
This development represents a measurable improvement in cybersecurity defenses, directly addressing a pervasive threat to sensitive data and critical infrastructure.
Phishing detection can become more proactive and efficient, reducing reliance on manual updates and improving the security posture for individuals and organizations.
- · Cybersecurity industry
- · Organizations with sensitive data
- · General internet users
- · AI/ML researchers in security
- · Phishing attackers
- · Organizations with outdated security systems
Reduced incidence and success rate of phishing attacks across various sectors.
Increased trust in online interactions and a potential decrease in data breaches attributable to phishing.
Deterred investment in simple phishing tactics, forcing attackers to develop more advanced, potentially harder-to-detect methods, driving an arms race in cyber defense.
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Read at arXiv cs.LG