IJLTEMAS International Journal of Latest Technology in Engineering, Management & Applied Science (IJLTEMAS) IJLTEMAS 2278-2540 IJLTEMAS 171 10.51583/IJLTEMAS.2026.150700170 Education Development of an Enhanced Ripple Down System for Detecting Cybercrimes in a University Environment Rukayat Ayokunmi Adeyemo Ladoke Akintola University of Technology, Ogbomoso) Nigeria Wasiu Oladimeji Ismaila Ladoke Akintola University of Technology, Ogbomoso) Nigeria Adeolu Olabode Afolabi Ladoke Akintola University of Technology, Ogbomoso) Nigeria Isiaka Akinkunmi Adeyemo Ladoke Akintola University of Technology, Ogbomoso) Nigeria Folasade Muibat Ismaila Osun State University Nigeria 15 7 2263 2333 10 08 2026 15 08 2026 26 08 2026 Enhanced Ripple Nigeria Abstract

University environments have become increasingly vulnerable to cybercrimes, including identity theft, hacking, financial fraud, and data breaches. Existing detection systems often exhibit high false positive rates, causing alert fatigue and missed detections, alongside prolonged recognition times that delay response to critical incidents. These limitations undermine the effectiveness of cybersecurity measures in academic institutions. This research aims to develop and evaluate an Enhanced Ripple Down Rule (ERDR) system for detecting cybercrimes in a university environment, addressing the limitations of standard rule-based approaches. A dataset comprising 3,800 cybercrime records, covering seven crime types (labelled Crime A through G) with four attributes each, was used. The dataset was partitioned into training (2,800 records) and testing (1,000 records) sets. The ERDR system, extending the standard Ripple Down Rule framework, employs a binary tree structure with enhanced rule processing capabilities. The system was implemented using MATLAB (R2023a) on a Windows 10 64-bit platform. Performance was evaluated using Sensitivity, Specificity, False Alarm Rate (FAR), Accuracy, and Computational Time (CT) across multiple threshold values. At the optimum threshold of 0.80, the ERDR system achieved: Sensitivity of 97.86%, Specificity of 98.92%, FAR of 1.08%, Accuracy of 99.86%, and CT of 140.9 seconds. In comparison, the standard RDR achieved: Sensitivity of 96.24%, Specificity of 95.74%, FAR of 4.26%, Accuracy of 97.85%, and CT of 131.11 seconds. The ERDR system demonstrated superior performance across all metrics, reducing false alarms by 74.6% while improving accuracy by 2.01%. The system provides a robust, accurate, and practical solution for cybercrime detection in university environments. Its superior performance, particularly in reducing false positives, makes it suitable for deployment across higher education institutions. This research contributes to the field of cybersecurity by extending the Ripple Down Rule methodology with enhancements that significantly improve detection accuracy and operational efficiency.

 References Alazab, A., Abawajy, J. H., & Hobbs, M. (2013). Web Malware that Targets Web Applications. Social Network Engineering for Secure Web Data and Services, 12, 248-264. doi:10.4018/978-1-4666-3926-3. Alex S., David D., Aladdin A. (2018). Intelligent intrusion detection systems using artificial neural networks. The Korean Institute of Communication and Information Science, ICT Express 4, 95–99. Ameer A. and Chafika B., (2015). Cybercrime Profiling: Decision-Tree Induction, Examining Perceptions of Internet Risk and Cybercrime Victimization. IEEE Trustcom. Amrit, P. S., and Manik, D.S. (2014). Analysis of Host-Based and Network-Based Intrusion Detection System, International Journal of Computer Network and Information Security, 8, PP.41-47. Aru, O. E and Chiaghana, C. E. (2018). Malware Analysis and Mitigation in Information Preservation, Computer Engineering (IOSR-JCE), e-ISSN: 2278-0661, 20(4): 53-62. Arulogun, O.T., Amusan D. G. (2015). Vehicle license plate recognition using edge detection and neural network. International Journal of Science and Research (IJSR), ISSN (Online): 2319-7064. Anderson, R., and Moore, T. (2007). Information security economics and beyond. Advances in Cryptology, 68-91. Anderson, B., Storlie, C., and Lane, T. (2012). Improving malware classification: bridging the static/dynamic gap. In Proceedings of the 5th ACM Workshop on Security and Artificial Intelligence, 3–14. Asmaa, S. and Sharad, G. (2011). Intrusion detection system and intrusion prevention system. International Journal of Scientific and Engineering Research, 2(7): 64-71. Blum, A, Song, D, and Venkataraman, S. (2004). Detection of interactive stepping stones. Algorithms and confidence bounds, in Proceedings of the 7th International Symposium on Recent Advances in Intrusion Detection (RAID), 27, 1134-1142. Burch, H. and Cheswick, B. (2000). Tracing anonymous packets to their approximate source, in Proceedings of USENIX LISA, New Orleans, USA, 319-327. Chao, R., Tan, Y. (2009). A Virus Detection System Based on Artificial Immune System, International Conference on Computational Intelligence and Security, 1, 6–10. Cker Chiueh, T., and Hsu, F., H. (2001). A Compile-Time Solution to Buffer Overflow Attacks. Proc. 21st Int’l Conf. Distributed Computing Systems (ICDCS). Cohen, LE., Felson M. (1979). Social Change and Crime Rate Trends. A Routine Activity Approach. Am. Sociol. 44(2): 588-605. Connolly, T. M., Begg, C. E., and Strachan. A. D. (2006). Database systems: Addison-Wesley. Journal of Image and Vision Computing, 19, 142-160. Corporation. M., (2012). Common Vulnerabilities and Exposures Available: http://cve.mitre.org/. Cova, M., Balzarott D., Felmetsger V., and Vigna, G. (2008). Swaddler: An approach for an anomaly-based detection of state violations in web applications, 63-86. Dagorn, N. (2008). A Cooperative Bayesian Anomaly-Based Intrusion Detection System for Web Applications (Extended Abstract), Web IDS: 392-393. Danforth, M. (2009). Towards a Classifying Artificial Immune System for Web Server Attacks. International Conference on Machine Learning and Applications, 523–527. Dharmapurikar, S., Krishnamurthy. P, Sproull. T, and Lockwood J. W. (2004). Deep Packet inspection using parallel bloom filters. IEEE. 52-61. Duman, E. and Ozcelik. H. M. (2011). Detecting credit card fraud by genetic algorithm and scatter search. Science Direct, Expert System with Applications 38, 13057-13063. EshghiShargh, A. (2009). Using Artificial Immune System on Implementation of Intrusion Detection Systems. Third UK Sim European Symposium on Computer Modeling and Simulation, 164-168. Figueiredo, M. A. (2000). On gaussian radial basis function approximations: interpretation, extensions, and learning strategies. In Proceedings of the IEEE 15th International Conference on Pattern Recognition 2, 618–621. Folashade, B. O and Abimbola K.A. (2013). The Nature, Causes and Consequences of Cyber Crime in Tertiary Institutions in Zaria-Kaduna State, Nigeria. American International Journal of Contemporary Research. 3(9): 21-32. Garcia K. A. (2012). Analyzing Log Files for Postmortem Intrusion Detection. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42(6): 1690-704. Garcia-Teodoro, P., Diaz-Verdejo, J., Macia-Fernandez. G., and Vazquez. E. (2009). Anomaly-based network intrusion detection. Techniques, systems and challenges, Computers & Security, 28, 18-28. Gianini, G., Anisetti, M., Azzini, V., Bellandi, V., Damiani, E., Marrara, S. (2009). An Artificial Immune System approach to Anomaly Detection in Multimedia Ambient Intelligence. 3rd IEEE International Conference on Digital Ecosystems and Technologies. 502–506. Hesham, A. and Saeed A. (2011). Bayesian based intrusion detection system. Journal of King Saud University Computer and Information Sciences. 2-7. Huang, G., Saratchandran, P., and Sundararajan, N. (2005). A generalized growing and pruning RBF (GGAP-RBF) neural network for function approximation. IEEE Transactions on Neural Networks. 16(1), 57–67. Ismaila, W. O, Ismaila, F. M, Falohun, A. S. Oladoye S. F. (2018). Soft Computing Approach to Anomaly Detection in Subscribers Call Profiles. The International Journal of Engineering and Science (IJES). 7(12), 23-30. Ismaila, W. O., Falohun, A.S., Ismaila F.M., Ogunjinmi T.O., Babalola O.R. (2019). Soft Computing: Investigation of two-step approach to identify theft detection in electronic payments. The proceedings for Academic Conference of the sub-sahara African Academic Research publications on unleashing sub-sahara African Resources held at Multipurpose Hall, Gonbe State University, Gombe State, Nigeria. 18(2), 13–23. Iwarimie, J., D. (2010). Criminology, Crime and Delinquency in Nigeria. Port Harcourt, Pearl Publishers. Iwasokun, G. B., Alese, B. K., T A., and Aranuwa, F. O. (2012). Statistical evaluation of the impact of ICT on Nigerian universities. International Journal of Education and Development using Information and Communication Technology (IJEDICT), 8(1), 104-120. James l. (2018). Economic Impact of Cybercrime—No Slowing Down. McAfee, one of the world’s leading independent cybersecurity companies. 1-28. Jamil F. (2003). Statistical based Intrusion Detection. Symantec. Security Focus. Juvonen and Sipola T. (2013). Combining conjunctive rule extraction with diffusion maps for network intrusion detection. 20-13. Kim, K, Choi, Y and Park, J. (2013). Pricing fraud detection in online shopping malls using a finite mixture model. Science Direct Electronic Commerce Research and Applications, 195-207. Kolbitsch. (2013). Discriminant Malware Distance Learning on Structural Information for Automated Malware Classification. Proceedings of the ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems. 347-348. Kruegel, C. Comparetti, P. M., Hlauschek, C. and Bayer, U. (2009). Scalable, Behavior-Based Malware Clustering. IEEE. Kruegel, C., Mutz, D., Robertson, W., and Valeur F. (2003). Bayesian event classification for intrusion detection. In Computer Security Applications Conference, 2003. Proceedings. 19th Annual. 14-23. Lebbe, M. A., Agbinya, J. I., Chaczko, Z., Chiang F. (2007). Self-Organized Classification of Dangers for Secure Wireless Mesh Networks. Australasian Telecommunication Networks and Applications Conference. 322–327. Longe, B., Chiemeke, C. (2008). Cybercrime and criminality in Nigeria. Journal of Social Science. 6(4), 133-139. Maher, A., Hossain, M.A., Keshav, D., Fadi, T. (2010). Predicting Phishing Websites using Classification Mining Techniques with Experimental Case Studies. Seventh International Conference on Information Technology. Maitanmi, O, Ogunlere S, Ayinde S, Adekunle Y. (2013). Impact of Cyber Crimes on Nigerian Economy. The International Journal Of Engineering And Science (IJES), 2(4), 45-51. Manjeri N. K., and Shelke C. J. (2014). Remote Administrative Trojan/Tool (RAT). JCSMC. 3(3), 482–487. Moore. T., and Anderson. R. (2007). Information security economics–And beyond. Advances in Cryptology-CRYPTO, 68-91. Neelabh (2012). Tracking Digital Footprints of Scareware to Thwart Cyber Hypnotism Through Cyber Vigilantism in Cyberspace. BVICAM’s International Journal of Information Technology (BIJIT). 4(2), 0973–5658. Ng, J. D. Joshi, and Banik. S. M. (2015). Applying Data Mining Techniques to Intrusion Detection. Information Technology: New Generations (ITNG) 2015 Proceedings of the 12th International Conference on Information Technology, Las Vegas, NV, USA, 800-810. Odumesi, J. O. (2014). A socio-technological analysis of cybercrime and cybersecurity in Nigeria. International Journal of Sociology and Anthropology, 6(3), 116-125. Okoye R and Gbegi N. (2013). An evaluation of the effect of fraud and related financial crimes in Nigerian economy. Kuwait chapter of Arabian Journal of Business and Management Review 2(7). Open Web Application Security Project. (2010). The Top 10 Most Critical Web Application Security Risks. Patcha. A and Park J. M. (2007). An overview of anomaly detection techniques: Existing solutions and latest technological trends. Computer Networks, 51, 3448-3470. Parvinder, S., and Mandeep, S. (2015). Fraud Detection by Monitoring Customer Behavior and Activities. International Journal of Computer Applications. 111(11), 0975–8887. Powers, S. T., and He, J. (2008). A hybrid artificial immune system and Self Organizing Map for network intrusion detection. Information Sciences, 178, 3024-3042. Priyanka, S. (2009). Ripple-Down Rules for Knowledge Acquisition in Intelligent System (JTES) Delving. Journal of Technology and Engineering Sciences. 1(1). Quinlan, J. R. (2006). Induction of Decision Trees. Machine Learning, 1, 81-106. Razzaq and Abdul. (2014). Semantic Security Against Web Application Attacks: 254 Vol. Elsevier Inc, 2014. Reazul K. M., Abdur Rahman O., Tanvir S. (2017). A Network Intrusion Detection Framework based on Bayesian Network using Wrapper Approach. International Journal of Computer Applications. 166(4): 0975–8887. Robertson, W. K. (2010). Detecting and preventing attacks against web applications. Information of Technology, University of California, Santa California. Roesch, M. (1999). Snort-lightweight intrusion detection for networks. In Proceedings of the 13th USENIX Conference on System Administration. 1.9, 229-238. Roger, E.S. (2008). Rogers Communications Inc, 2008 Annual Report. Samanvay, G. (2012). Buffer Overflow Attacka. IOSR Journal of Computer Engineering (IOSRJCE), ISSN: 2278-0661, 1(1): 10-23. Seong, S.K., and Narasimha R. (2005). A study of analyzing network traffic as images in real-time. In IEEE Infocom. 2056–2067. Sesan, G., Soremi, B., and Oluwafemi B. (2013). Economic Cost of Cybercrime in Nigeria. Cyber Steward Network Project of the Citizen Lab. University of Toronto. Shafi, K., and Abbass, H. A. (2009). An adaptive genetic-based signature learning system for intrusion detection. Expert Systems with Applications. 36, 12036-12043. Sheikhan, M., Jadidi, Z., and Beheshti, M. (2010). Effects of feature reduction on the performance of attack recognition by static and dynamic neural networks. World Applied Sciences Journal, 8, 302-308. Shimrit Tzur-David (2011). Network Intrusion Prevention System. Signature-based and Anomaly Detection. Ph.D. Thesis, School Computer Science. The Hebrew University of Jerusalem. Simone, A. L. (2019). Applying a Neural Network Ensemble to Intrusion Detection. JAISCR, 9(6). Sinclair, C., Pierce, L., and Matzner, S. (2009). An application of machine learning to network intrusion detection. In Computer Security Applications Conference, 1999 (ACSAC'99) Proceedings. 15th Annual. 371-377. Singh R., Kumar H., Singla R.K., Ketti, R.R. (2017). Internet attacks and intrusion detection System. A review of the literature, Online Inform. 41(2): 171–184. Stakhanova, N., Basu, S., and Wong, J. (2007). A taxonomy of intrusion response systems. International Journal of Information and Computer Security, 1, 169-184. Steve U. B, Diepreye O, Uduak D. A. (2009). Information Communication Technologies in the Management of Education for Sustainable Development in Africa. An International Multi-Disciplinary Journal, Ethiopia, 3(3): 414-428. Stibor, T., Timmis, J., and Eckert, C. (2005). A comparative study of real-valued negative selection to statistical anomaly detection techniques. In Artificial Immune Systems, Springer, 262-275. Sumanjit, D. and Tapaswini N. (2013). Impact of Cyber Crime: Issues and Challenges. International Journal of Engineering Sciences & Emerging Technologies, 6(2): 142-153. Tong, X, Wang, Z, and Yu, H. (2009). Hybrid RBF/Elman neural networks for intrusion detection system secure model. Computer Physics Communications, 180, 1795-1801. Vigna, G., and Kemmerer, R. A. (2009). A network-based intrusion detection system. Journal of Computer Security. 7, 37-72. Vijesh K., Santhadevi P. (2014). Internet spam threats and email exploitation – A scuffle with inbox attack. Int. Journal of Applied Sciences and Engineering Research, 3(4): 46-55. Wang, G, Hao, J, Ma, J, and Huang, L. (2010). A new approach to intrusion detection using Artificial Neural Networks and fuzzy clustering. Expert Systems with Applications, 37, 6225-6232. William R. Cheswick., Steven M. Bellovin., Aviel D. R. (2003). Firewalls and Internet Security. Repelling the Wily Hacker, 2nd Edition, Addison Wesley Publication. Wu J., Peng D., Li Z., Zhao L., Ling H. (2015). Network intrusion detection based on a general regression neural network optimized by an improved artificial immune algorithm. PLOS ONE, 10(3): 1–13. Yeh, Y.R., Lee, Y.J., and Wang, Y.C.F. (2013). Anomaly Detection via Online Oversampling Principal Component Analysis. IEEE Transactions on Knowledge and Data Engineering, 25(7): 1460-1470. Yoohwan, K., Wing, C., Lau, M., Choo, C., and Jonathan, C. (2004). Statistics-based overload control against distributed denial-of-service attacks. In IEEE Infocom, 2594–2604. Zhang, A., Chen, C., and Karimi, H. (2013). A new adaptive LSSVR with on-line multikernel RBF tuning to evaluate analog circuit performance. Abstract and Applied Analysis. 20, 1-7. Article ID 231735. Zou, C. Towsley, Gong, W. & Cai. Routing S. (2015). Worm: A fast, selective attack worm based on IP address information. In Proc. IEEE Work. Princ. Adv. and Dist. Simul. (PADS), 199–206.