A Chatbot Assistance Detecting Cross-Site Scripting (XSS) Attacks

By: Pinaki Sahu, International Centre for AI and Cyber Security Research and Innovations (CCRI), Asia University, Taiwan, 0000pinaki1234.kv@gmail.com


The struggle to safeguard web applications is becoming more intense as cyber risks continue to change, and Cross-Site scripting (XSS) attacks are one major area of concern. This article examines a cutting-edge method for enhancing cybersecurity defenses by incorporating chatbot support for instantaneous XSS attack detection. By utilizing artificial intelligence, this creative approach seeks to offer an effective incident response, ongoing monitoring, and adaptive learning all of which will strengthen web applications’ defenses against the ever-changing threat landscape.

Introduction :A Rise of XSS Attacks

XSS attacks continue to be a major cybersecurity problem. By taking advantage of vulnerabilities in web applications, they enable attackers to insert scripts that can be run in naive individual browsers. This could end up in the defacement of websites, the loss of confidential data, and session hijacking. Even while they are crucial, traditional security measures might not always be enough to counteract these threats’ dynamic nature[1].

The Role of Chatbot Support

Artificial intelligence-driven chatbots have emerged as useful tools for a variety of applications, including personal assistants and customer service. It makes sense to use them for cybersecurity in order to improve threat detection and response. Organizations can obtain real-time monitoring and analysis of user interactions by integrating chatbot help into their web application security infrastructure[2].

Detecting XSS Attacks: A Chatbot-Powered Process

Fig1.The process of detecting XSS attacks with chatbot assistance

The flow chart represents the process of detecting XSS attacks with chatbot assistance involves several key steps .

  • User Interaction: By entering text or completing forms, users can interact with the web application by providing input data.
  • Input validation: After processing the data input, the web application verifies it by comparing it to predetermined guidelines. Suspicious input is marked for additional examination.
  • Chatbot Analysis: The integrated chatbot receives the flagged data and uses machine learning and natural language processing to scan the input for possible XSS attack patterns[3].
  • XSS Detection: The chatbot looks for trends that point to XSS assaults. This involves spotting script tags, odd code structures, and malicious content injection attempts[3].
  • Alert and Reporting: The chatbot notifies the security team to take immediate action in the event that it finds evidence of a potential cross-site scripting (XSS) attack. In parallel, a thorough report is logged in the system for review and analysis at a later time.


Enhancing XSS attack detection through chatbot help integration with the web application security framework is a good approach. Organizations can strengthen their defenses against new cybersecurity threats and ultimately give users a safer online experience by combining artificial intelligence and human skills. Cutting-edge solutions like chatbot-powered XSS detection will become essential foundations in cybersecurity as long as the threat landscape keeps changing.


  1. Gupta, S., & Gupta, B. B. (2017). Cross-Site Scripting (XSS) attacks and defense mechanisms: classification and state-of-the-art. International Journal of System Assurance Engineering and Management, 8, 512-530.
  2. Adamopoulou, E., & Moussiades, L. (2020). An overview of chatbot technology. In IFIP international conference on artificial intelligence applications and innovations (pp. 373-383). Springer, Cham.
  3. Bozic, J., & Wotawa, F. (2018, September). Security testing for chatbots. In IFIP International Conference on Testing Software and Systems (pp. 33-38). Cham: Springer International Publishing.
  4. Poonia, V., Goyal, M. K., Gupta, B. B., Gupta, A. K., Jha, S., & Das, J. (2021). Drought occurrence in different river basins of India and blockchain technology based framework for disaster management. Journal of Cleaner Production312, 127737.
  5. Gupta, B. B., & Sheng, Q. Z. (Eds.). (2019). Machine learning for computer and cyber security: principle, algorithms, and practices. CRC Press.
  6. Singh, A., & Gupta, B. B. (2022). Distributed denial-of-service (DDoS) attacks and defense mechanisms in various web-enabled computing platforms: issues, challenges, and future research directions. International Journal on Semantic Web and Information Systems (IJSWIS)18(1), 1-43.
  7. Almomani, A., Alauthman, M., Shatnawi, M. T., Alweshah, M., Alrosan, A., Alomoush, W., & Gupta, B. B. (2022). Phishing website detection with semantic features based on machine learning classifiers: a comparative study. International Journal on Semantic Web and Information Systems (IJSWIS)18(1), 1-24.

Cite As

Sahu P. (2024) A Chatbot Assistance Detecting Cross-Site Scripting (XSS) Attacks, Insights2Techinfo, pp.1

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