PERFORMANCE ANALYSIS OF CAPTCHA BASED BLOCKING AND RESPONSE OF AN INTRUSION DETECTION MODEL USING SIGNATURE
Intrusion Detection System is the process of intelligently monitoring the events occurring in a computer system or network, analyzing them for signs of violations of a security policy. Its primary aim is to protect the availability, confidentiality and integrity of critical networked information systems. This paper considered and reviewed a CAPTCHA based intrusion detection model. A method of incorporating signature was used along with the CAPTCHA in the intrusion detection model to clear the controversy identified in the existing model. The signature provides a means of identifying intruders that are able to by-pass the system as legitimate users. The model was implemented using a website hosted online. Dataset obtained from the site was analyzed based on NaÃ¯ve Bayes classification model using confusion matrices. Implementation of the data analysis was carried out using RStudio software package. Analyzed results shows a better Detection Rate (DR), Accuracy (CR) and False Positive Rate (FPR). This shows that the developed system has significant capability of identifying intelligent spywares targeted at breaking CAPTCHA
Abubakar, H., Souley, B., & Ya'u, A. G. (2020). An Improved CAPTCHA-Based Intrusion Detection System Based on Redirector Model. Journal of Theoritical and Applied InformationTechnology, Volume 98, No. 03, 429-440.
El Mourabit, Y., bouirden, A., Toumanari, A., & El Moussaid, N. (2015). Intrudion Detection Techniques in Wireless Sensor Network using Data Mining Algorithms: Comparative Evaluation Based on Attacks Detection. International Journalof Advanced Computer Science and Application, Vol 6, No. 9, 164-172.
Khudadad, M., & Huang, Z. (2018). Novel Intrusion Detection Methods for Security of Wieless Sensor Netwok. Journal of Fundermental and Applied Sciences, 173-189.
Malav, S., Avinash, M. S., Satish, N. S., & Sandeep, S. C. (2016). Network Security Using IDS, IPS, and Honeypot. Interenational Journal of Recent Research in Mathematics Computer Science and Information Technology, Vol 2, Issue 2, 27-30.
Milan, Sardana, H., & Singh, K. (2018). Reducing False Alarms in Intrusion Detection Systems â€“ A Survey. International Research Journal of Engineering and Technology, Volume 05, Issue 02, 9-12.
Nachar, R. A., Inaty, E., Bonnin, P. J., & Alayli, Y. (2015). Breaking Down CAPTCHA Using Edge Corners and Fuzzy Logic Segmentation/Recognition Technique. Security and Communication Networks, Vol 8, No. 18, 3995-4012.
Sano, S., Otusko, T., Itoyama, K., & Okuno, H. G. (2015). HMM- based Attacks on Google's ReCAPTCHA with Continous Visual and audio symbol. International Journal of Information Processing, vol. 23, No. 6, 814-826.
Souley, B., & Abubakar, H. (2018). A CAPTCHA â€“ BASED INTRUSION DETECTION MODEL. International Journal of Software Engineering & Applications, Vol.9, No.1, 29-40.
Stevens, I., D. (2016). Using machine learning to detect bots in World of Warcraft. Transactions on networking19 (5).
Yesugade, K. D., Avinash, M. S., Satish, N. S., Sandeep, S. C., & Malav, S. (2016). Infracstructure Security Using IDS, IPS and Honeypot. International Engineering Research Journal (IERJ), vol 2, issue3, 851-855.
Zhuoheng, X., Zhenghao, Y., Simon, J., Micheal, R., Chris, R., Theerakorn, P., & Matthew, A. (2018). Caret Versus Scikit-Leran AComparison of Data Science Tools. Lanham Purdue University Krannert.
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