A Comprehensive Evaluation and Practice of System Penetration Testing

With the rapid advancement of information technology, the complexity of applications continues to increase, and the cybersecurity challenges we face are also escalating. This paper aims to investigate the methods and practices of system security penetration testing, exploring how to enhance system security through systematic penetration testing processes and technical approaches. It also examines existing penetration tools, analyzing their strengths, weaknesses, and applicable domains to guide penetration testers in tool selection. Furthermore, based on the penetration testing process outlined in this paper, appropriate tools are selected to replicate attack processes using target ranges and target machines. Finally, through practical case analysis, lessons learned from successful attacks are summarized to inform future research.

💡 Research Summary

The paper presents a comprehensive study of system penetration testing, aiming to standardize and optimize the testing workflow while providing a quantitative framework for tool selection. After outlining the rapid growth of internet usage and the escalating sophistication of cyber‑attacks, the authors emphasize penetration testing as a critical defensive measure. They review existing literature, noting the dichotomy between traditional manual testing—valued for flexibility and depth but costly and time‑consuming—and emerging automated approaches that leverage artificial intelligence and large language models (LLMs).

A central contribution is the design of a six‑phase penetration testing process: (1) information gathering, (2) threat modeling, (3) vulnerability scanning, (4) attack implementation, (5) privilege escalation and persistence, and (6) reporting with re‑testing. For each phase, detailed deliverables, checklists, and best‑practice guidelines are provided, creating a repeatable methodology applicable to both host‑based and web‑application assessments.

To address the challenge of selecting appropriate tools, the authors propose a weighted quantitative evaluation model. The model assesses tools along three dimensions—functional coverage, performance (speed and accuracy), and usability (learning curve and automation level). Weighting schemes are tailored for three typical scenarios: large‑scale network environments, web‑application testing, and embedded‑system assessments. This multi‑criteria decision‑making approach enables testing teams to objectively rank and combine tools such as Nmap, Metasploit, Burp Suite, as well as newer AI‑driven platforms like PENTESTGPT and AISOC.

Experimental validation is conducted on Windows 10 and Ubuntu 22.04 hosts, reproducing memory‑corruption exploits, privilege‑escalation techniques, and file‑upload vulnerabilities. Web‑application experiments use the DVWA platform to demonstrate SQL injection, file upload, and XSS attacks. The experiments follow the six‑phase workflow, confirming its practicality and reproducibility. When AI‑assisted tools are employed, the authors observe a 20‑30 % increase in vulnerability detection efficiency and a reduction of attack‑path generation time by roughly 50 % compared with traditional manual workflows. However, they also acknowledge limitations such as the cost of model training, the need for up‑to‑date vulnerability databases, and a potential rise in false‑positive rates.

The paper concludes with case‑study analyses of recent high‑profile incidents: the MOVEit Transfer data‑theft breach, large‑scale DDoS campaigns, and AI‑generated identity‑spoofing attacks. These cases reveal common success factors—poor patch management, inadequate human‑factor defenses, and insufficient automation in security testing. Based on these insights, the authors recommend an integrated defense strategy that combines regular penetration testing, AI‑enhanced detection, continuous employee security awareness training, and rigorous patch‑management processes.

Overall, the study bridges theoretical frameworks with hands‑on validation, offering a concrete roadmap for organizations to improve proactive security posture through a balanced mix of manual expertise and automated AI‑driven tools.