Salma Akther¹, Wencheng Yang^1,*, Song Wang², Shicheng Wei¹, Ji Zhang¹, Xu Yang³, Yanrong Lu⁴, Yan Li¹

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.076358 - 12 March 2026

Abstract As deep learning (DL) models are increasingly deployed in sensitive domains (e.g., healthcare), concerns over privacy and security have intensified. Conventional penetration testing frameworks, such as OWASP and NIST, are effective for traditional networks and applications but lack the capabilities to address DL-specific threats, such as model inversion, membership inference, and adversarial attacks. This review provides a comprehensive analysis of penetration testing for the privacy of DL models, examining the shortfalls of existing frameworks, tools, and testing methodologies. Through systematic evaluation of existing literature and empirical analysis, we identify three major contributions: (i) a critical… More >

Stefan Ćirković¹, Vladimir Mladenović¹, Siniša Tomić², Dalibor Drljača², Olga Ristić^1,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4409-4430, 2025, DOI:10.32604/cmc.2025.059696 - 06 March 2025

Abstract With the increasing use of web applications, challenges in the field of cybersecurity are becoming more complex. This paper explores the application of fine-tuned large language models (LLMs) for the automatic generation of synthetic attacks, including XSS (Cross-Site Scripting), SQL Injections, and Command Injections. A web application has been developed that allows penetration testers to quickly generate high-quality payloads without the need for in-depth knowledge of artificial intelligence. The fine-tuned language model demonstrates the capability to produce synthetic payloads that closely resemble real-world attacks. This approach not only improves the model’s precision and dependability but… More >

Ziqiao Zhou¹, Tianyang Zhou^1,*, Jinghao Xu², Junhu Zhu¹

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2613-2634, 2024, DOI:10.32604/cmes.2023.028553 - 08 July 2024

Abstract Intelligent penetration testing is of great significance for the improvement of the security of information systems, and the critical issue is the planning of penetration test paths. In view of the difficulty for attackers to obtain complete network information in realistic network scenarios, Reinforcement Learning (RL) is a promising solution to discover the optimal penetration path under incomplete information about the target network. Existing RL-based methods are challenged by the sizeable discrete action space, which leads to difficulties in the convergence. Moreover, most methods still rely on experts’ knowledge. To address these issues, this paper… More >

Bandar Abdulrhman Bin Arfaj¹, Shailendra Mishra^2,*, Mohammed Alshehri¹

Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 223-239, 2022, DOI:10.32604/iasc.2022.019485 - 03 September 2021

Abstract The financial and confidential cost of cyberattack has presented a significant loss to the organization and government where the privacy of worthless information has become vulnerable to cyber threat. In terms of efforts implemented to avoid this risk, the cyberattack continues to evolve, making the cybersecurity systems weekend. This has necessitated the importance of comprehensive penetration testing, assessment techniques, and tools to analyze and present the currently available unconventional penetration techniques and tactics to test and examine their key features and role in supporting cybersecurity and measure their effectiveness. The importance of cyberspace and its… More >

Yichao Zang^1,*, Tairan Hu², Tianyang Zhou², Wanjiang Deng³

CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2573-2585, 2021, DOI:10.32604/cmc.2021.012220 - 28 December 2020

Abstract Mining penetration testing semantic knowledge hidden in vast amounts of raw penetration testing data is of vital importance for automated penetration testing. Associative rule mining, a data mining technique, has been studied and explored for a long time. However, few studies have focused on knowledge discovery in the penetration testing area. The experimental result reveals that the long-tail distribution of penetration testing data nullifies the effectiveness of associative rule mining algorithms that are based on frequent pattern. To address this problem, a Bayesian inference based penetration semantic knowledge mining algorithm is proposed. First, a directed More >