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@article{180797,
        author = {Farooq Hassian and Ch. Likith and G. Siddharth and S. Gowtham},
        title = {Hack-Box: A Consolidated and Modular Hacking Toolkit for Ethical Hackers},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {2652-2655},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180797},
        abstract = {This paper presents Hack-Box, a consolidated and modular hacking toolkit designed for ethical hackers, penetration testers, and cybersecurity researchers. The toolkit integrates a wide array of tools into a single framework, automating repetitive tasks such as scanning, exploitation, and reporting. By offering a unified, user-friendly interface, Hack-Box simplifies navigation and reduces the time and complexity associated with security assessments. The modular design ensures flexibility, allowing the toolkit to evolve alongside emerging security needs. Experimental results demonstrate the toolkit's efficiency in streamlining penetration testing workflows, enabling professionals to focus on deeper analysis rather than repetitive tasks. Future enhancements include the incorporation of machine learning-based vulnerability detection and cloud integration for distributed testing on large-scale networks.},
        keywords = {This paper presents Hack-Box, a consolidated and modular hacking toolkit designed for ethical hackers, penetration testers, and cybersecurity researchers. The toolkit integrates a wide array of tools into a single framework, automating repetitive tasks such as scanning, exploitation, and reporting. By offering a unified, user-friendly interface, Hack-Box simplifies navigation and reduces the time and complexity associated with security assessments. The modular design ensures flexibility, allowing the toolkit to evolve alongside emerging security needs. Experimental results demonstrate the toolkit's efficiency in streamlining penetration testing workflows, enabling professionals to focus on deeper analysis rather than repetitive tasks. Future enhancements include the incorporation of machine learning-based vulnerability detection and cloud integration for distributed testing on large-scale networks.},
        month = {June},
        }