Detecting Phishing Patterns with Rust: A Zero-Budget Approach for Security Researchers

Phishing remains one of the most pervasive cybersecurity threats, targeting users’ credentials and sensitive information through maliciously crafted URLs and email content. Traditional detection techniques often rely on expensive tools or extensive datasets, but as security researchers, especially with limited resources, we need efficient, reliable methods that are inexpensive or free.

This article explores how to leverage Rust, a performant and memory-safe systems programming language, to develop a lightweight tool for identifying phishing patterns in URLs and email content — all without any budget.

Why Rust?

Rust’s zero-cost abstractions, strong type safety, and excellent performance make it ideal for building reliable security tools. Its ecosystem includes powerful libraries for string processing, pattern matching, and network analysis, enabling us to implement effective detection algorithms without external dependencies.

Core Approach

The core idea is to develop heuristics that identify common phishing traits such as:

• Lookalike domains (typosquatting, homoglyphs)
• Suspicious URL structures (long URLs, unusual URL parameters)
• Obfuscated tokens or encoded components

Instead of relying on external blacklists, we focus on pattern recognition to flag potential threats.

Implementation Details

1. Domain Similarity Detection

Detecting typosquatting or homoglyphs involves analyzing domain similarity. For example, substituting Cyrillic characters that look like Latin letters.

fn is_homoglyph(domain: &str) -> bool {
    // Simplified mapping of common homoglyphs
    let homoglyphs = [('а', 'a'), ('е', 'e'), ('о', 'o'), ('і', 'i')];
    let mut score = 0;
    for (hom, lat) in homoglyphs.iter() {
        if domain.contains(*hom) {
            score += 1;
        }
        if domain.contains(*lat) {
            score -= 1;
        }
    }
    score > 0
}

This function flags domains containing homoglyphs as suspicious.

2. URL Structure Analysis

Phishing URLs often contain long, complex strings or suspicious parameters. Using Rust’s pattern matching:

fn is_suspicious_url(url: &str) -> bool {
    let params = url.split('?').nth(1).unwrap_or("");
    if url.len() > 100 || params.contains("redirect") || params.contains("auth") {
        return true;
    }
    false
}

3. Obfuscation Detection

URL encoding can be used to obfuscate malicious links. We can decode URLs and check for base64 or hexadecimal encoding.

use base64::{decode_config, URL_SAFE};

fn is_base64_encoded(s: &str) -> bool {
    match decode_config(s, URL_SAFE) {
        Ok(_) => true,
        Err(_) => false,
    }
}

4. Combining Checks

A simple detection pipeline combines these heuristics:

fn is_phishing_candidate(url: &str) -> bool {
    is_suspicious_url(url) || is_homoglyph(url) || is_base64_encoded(url)
}

Final Remarks

This minimalistic, zero-budget approach offers security researchers an effective method to spot potential phishing URLs leveraging Rust's safety and performance. It is extendable — with more heuristics and context-aware rules, the system can improve detection accuracy over time.

By building tools based on heuristics instead of large datasets, security teams can achieve real-time detection without incurring costs, making this strategy particularly valuable for resource-constrained environments.

Developing these tools can be further enhanced through integration into larger security workflows, such as SIEM systems or email filtering solutions. Rust’s ecosystem continues to grow, making it increasingly feasible to develop sophisticated, maintainable cybersecurity solutions with minimal expenses.

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