Detecting Phishing Patterns in Go: A Zero-Budget Approach for Lead QA Engineers

As a Lead QA Engineer tasked with identifying and mitigating phishing attempts, the challenge is often compounded by limited resources. In this post, we will explore how to leverage Go, a lightweight and efficient programming language, to develop a practical, zero-budget phishing pattern detection system.

Understanding the Challenge

Phishing attacks rely on deceptive URLs, impersonation of trusted brands, or subtle changes to legitimate sites. The core goal is to identify these malicious patterns without incurring additional costs—utilizing open-source tools, existing data, and smart heuristics.

Core Strategies

The primary strategies for detecting phishing patterns include:

• URL pattern analysis
• Domain reputation checks
• Suspicious token detection in URLs
• Rapid heuristic filtering

Since we're constrained on budget, the focus is on implementing heuristic-based detection rather than relying on complex machine learning models or costly APIs.

Building the Detection Tool in Go

Go offers excellent performance, easy concurrency, and a simple syntax that reduces development time. We'll craft a modular tool that scans URLs against known patterns and heuristic rules.

Step 1: Define Common Phishing Patterns

Identify key patterns that indicate suspicious URLs:

• Excessive URL length
• Use of IP addresses instead of domain names
• Unusual subdomain patterns
• Abnormal domain name similarity to legitimate sites

Step 2: Writing the Detection Logic

Here's a basic example highlighting some heuristic checks:

package main

import (
    "fmt"
    "net"
    "regexp"
    "strings"
)

func isIPAddress(host string) bool {
    // Check if the URL contains an IP address
    return net.ParseIP(host) != nil
}

func hasSuspiciousSubdomain(host string, legitimateDomain string) bool {
    // Detect excessive subdomains or mismatched domains
    parts := strings.Split(host, ".")
    domainParts := strings.Split(legitimateDomain, ".")

    // Example heuristic: subdomain count greater than threshold
    if len(parts) > len(domainParts)+2 {
        return true
    }
    // Mismatched domain name
    if !strings.Contains(host, legitimateDomain) {
        return true
    }
    return false
}

func isLikelyPhishing(url string, legitimateDomain string) bool {
    // Basic URL parsing
    parts := strings.Split(url, "/")
    host := parts[2]

    // Heuristic 1: IP address in URL
    if isIPAddress(host) {
        return true
    }

    // Heuristic 2: Suspicious subdomain pattern
    if hasSuspiciousSubdomain(host, legitimateDomain) {
        return true
    }

    // Heuristic 3: URL length
    if len(url) > 75 {
        return true
    }

    return false
}

func main() {
    // Sample URLs for testing
    sampleURLs := []string{
        "http://192.168.1.1/secure-login",
        "http://login.security-example.com.fake/",
        "https://secure.example.com/account",
        "http://sub.sub.sub.malicious.com/",
    }
    legitimateDomain := "example.com"

    for _, url := range sampleURLs {
        if isLikelyPhishing(url, legitimateDomain) {
            fmt.Printf("Suspicious URL detected: %s\n", url)
        } else {
            fmt.Printf("URL looks safe: %s\n", url)
        }
    }
}

This script performs basic heuristic checks for IP addresses, subdomain patterns, and URL length, which are common indicators of phishing.

Step 3: Improving and Integrating

• Expand heuristics as needed, e.g., checking for uncommon URL characters, suspicious query parameters.
• Incorporate a local database of known malicious domains (maintainability is key due to zero-cost constraints).
• Run the scanner periodically or integrate into CI pipelines for proactive detection.

Conclusion

Detecting phishing with Go on a zero-budget requires creativity, relying heavily on heuristic analysis and open data. While it isn't foolproof, such a system provides an accessible first line of defense, empowering QA teams to identify potential threats early.

Final thoughts

Utilize open-source threat intelligence sources, keep heuristics updated, and continually test with new phishing examples. As threat tactics evolve, so should your detection scripts, all without breaking the bank.

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