Rohit Kumar,
- Assistant Professor, Department of Computer Applications, Echelon Institute of Technology, Faridabad, Haryana, India
Abstract
Launch-Attack is a lightweight and practical threat-detection framework designed specifically for smaller web-automation environments, including setups that rely on tools such as Selenium. Rather than aiming to replace large enterprise-grade security platforms, the framework focuses on offering an accessible option for developers, testers, and researchers who need real-time monitoring without the heavy resource demands of traditional systems. The model relies on machine-learning techniques implemented through Scikit-learn, enabling it to detect common web-based threats such as cross-site scripting (XSS) attempts and malicious script-injection behavior. During evaluation using publicly available datasets, including portions of CIC-IDS2017, the framework achieved encouraging results: an overall detection accuracy of approximately 92%, a false- positive rate near 3%, and an average response time well under one second. These performance characteristics highlight its suitability for continuous testing workflows and automated browser interactions. Additionally, its modest memory footprint—around 200MB—makes it a practical choice for constrained environments. Future development aims to expand Launch-Attack’s compatibility to broader browser-based ecosystems and mobile automation platforms, further increasing its versatility and usability.
Keywords: Machine Learning, Cybersecurity, Browser Automation, Threat Detection, Lightweight Framework, Freelance Automation, Web Security, Selenium, Puppeteer
[This article belongs to Journal of Web Engineering & Technology ]
Rohit Kumar. Cybersecurity in Web Automation: A Machine Learning Approach to Lightweight Intrusion Detection. Journal of Web Engineering & Technology. 2026; 13(01):35-41.
Rohit Kumar. Cybersecurity in Web Automation: A Machine Learning Approach to Lightweight Intrusion Detection. Journal of Web Engineering & Technology. 2026; 13(01):35-41. Available from: https://journals.stmjournals.com/jowet/article=2026/view=235616
References
- Sommer R, Paxson V. Outside the closed world: On using machine learning for network intrusion detection. In2010 IEEE symposium on security and privacy 2010 May 16 (pp. 305-316). IEEE.
- Buczak AL, Guven E. A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Communications surveys & tutorials. 2015 Oct 26;18(2):1153-76.
- Vinayakumar R, Alazab M, Soman KP, Poornachandran P, Al-Nemrat A, Venkatraman S. Deep learning approach for intelligent intrusion detection system. IEEE access. 2019 Apr 3;7:41525-50.
- Sharafaldin I, Lashkari AH, Ghorbani AA. Toward generating a new intrusion detection dataset and intrusion traffic characterization. ICISSp. 2018 Jan 22;1(2018):108-16.
- Moustafa N, Slay J. UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set). In2015 military communications and information systems conference (MilCIS) 2015 Nov 10 (pp. 1-6). IEEE.
- Jueckstock J, Sarker S, Snyder P, Beggs A, Papadopoulos P, Varvello M, Livshits B, Kapravelos A. Towards realistic and reproducibleweb crawl measurements. InProceedings of the Web Conference 2021 2021 Apr 19 (pp. 80-91).
- CBE Style Manual Committee. Scientific Style and Format: The CBE Manual for Authors, Editors, and Publishers. Cambridge University Press; 1994 Nov 25.
- García B. Hands-On Selenium WebDriver with Java. ” O’Reilly Media, Inc.”; 2022 Mar 31.
- Cohen MI. Puppets, puppeteers, and puppet spectators: A response to the Volkenburg Puppetry Symposium. Contemporary Theatre Review. 2017 Apr 3;27(2):275-80.
- Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J. Scikit-learn: Machine learning in Python. the Journal of machine Learning research. 2011 Nov 1;12:2825-30.
Journal of Web Engineering & Technology
| Volume | 13 |
| Issue | 01 |
| Received | 20/06/2025 |
| Accepted | 28/09/2025 |
| Published | 05/01/2026 |
| Publication Time | 199 Days |
Login
PlumX Metrics