Leveraging Go and Open Source Tools for Phishing Pattern Detection in DevOps

Introduction

In the ever-evolving landscape of cybersecurity, detecting phishing attacks remains a critical challenge for organizations. Phishing patterns often exploit human vulnerabilities through malicious URLs, deceptive domains, or suspicious email headers. Automating detection using robust, scalable tools is essential for maintaining security posture. In this post, we explore how a DevOps specialist can leverage Go, combined with open-source tools, to create an effective phishing pattern detection system.

Why Go for Phishing Detection?

Go presents an excellent choice for security tooling due to its performance, concurrency support, and strong standard library. Its ability to compile into static binaries makes deployment simple across different environments, and its rich ecosystem of libraries simplifies parsing and data analysis tasks.

Open Source Tools and Libraries

• GoSpider: For crawling and extracting URLs.
• Regexp: Standard library for pattern matching.
• CUE: For schema validation.
• ElasticSearch & Kibana: For logging and dashboards.
• Goroutines: For concurrent processing.

Building a Phishing Pattern Detector

Step 1: URL Extraction and Normalization

Using Go's net/http and goquery libraries, we fetch and parse email or webpage content to extract URLs.

package main

import (
    "fmt"
    "net/http"
    "github.com/PuerkitoBio/goquery"
)

func extractUrls(url string) ([]string, error) {
    resp, err := http.Get(url)
    if err != nil {
        return nil, err
    }
defer resp.Body.Close()

    document, err := goquery.NewDocumentFromReader(resp.Body)
    if err != nil {
        return nil, err
    }

    var urls []string
    document.Find("a").Each(func(i int, s *goquery.Selection) {

txt, exists := s.Attr("href")
    if exists {
        urls = append(urls, txt)
    }
})

    return urls, nil
}

func main() {
    urls, err := extractUrls("https://example.com")
    if err != nil {
        fmt.Println("Error extracting URLs:", err)
    }
    fmt.Println("Extracted URLs:", urls)
}

Step 2: Pattern Matching for Suspicious URLs

Phishing URLs often contain patterns such as homoglyphs, suspicious domains, or obfuscated characters. Using regex patterns, we can identify candidates.

package main

import (
    "regexp"
    "fmt"
)

var suspiciousPattern = regexp.MustCompile(`(?i)([a-z0-9.-]*[¡|!|@|#|$|%|^|&|*|~])`) // example pattern

func isSuspicious(url string) bool {
    return suspiciousPattern.MatchString(url)
}

func main() {
    urls := []string{"http://secure-login.com", "http://paypai.com", "http://g00gle.com"}

    for _, url := range urls {
        if isSuspicious(url) {
            fmt.Printf("Suspicious URL detected: %s\n", url)
        }
    }
}

Step 3: Concurrency for Scalability

Using goroutines and channels, we process large volumes of URLs efficiently.

package main

import (
    "fmt"
    "sync"
)

func processURL(wg *sync.WaitGroup, url string, results chan<- string) {
    defer wg.Done()
    if isSuspicious(url) {
        results <- url
    }
}

func main() {
    urls := []string{"http://example.com", "http://malicious.com"}
    var wg sync.WaitGroup
    results := make(chan string, len(urls))

    for _, url := range urls {
        wg.Add(1)
        go processURL(&wg, url, results)
    }

    wg.Wait()
    close(results)

    for suspiciousURL := range results {
        fmt.Printf("Flagged suspicious URL: %s\n", suspiciousURL)
    }
}

Step 4: Alerting and Dashboarding

Integrating with Elasticsearch and Kibana, logs of suspicious URLs can be stored for further analysis. Automated alerts can be configured to notify security teams upon pattern detections.

Conclusion

Employing Go with open-source tools offers a performant, scalable, and flexible approach to detecting phishing patterns in operational environments. By automating URL extraction, pattern matching, and analyzing suspicious activity, DevOps teams can enhance their proactive security measures.

This combination of techniques exemplifies how DevOps practices can be extended into the realm of cybersecurity, leveraging open source and modern programming languages to safeguard digital assets effectively.

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