Establishing software-defined network for fraud detection on energy fraud and traffic classification test case(s)
By:
References
- Altay, O. (2022). Performance of different KNN models in prediction English language readability. 2022 2nd International Conference on Computing and Machine Intelligence (ICMI), Istanbul, Turkey, pp. 1–5.
- Anupriya, E., Kumaresan, N., Suresh, S., Dhanasekaran, S., Ramprathap, K, and Chinnasamy, P. (2022). Fraud Account Detection on Social Network Using Machine Learning Techniques. 2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC), Bhubaneswar, India, pp. 1–4.
- Bao, J. (2020). Multi-features Based Arrhythmia Diagnosis Algorithm Using Xgboost. 2020 International Conference on Computing and Data Science (CDS), Stanford, CA, USA, pp. 454–457.
- Bheemesh. R.K, and Deepa. N. (2023). Accurate SMS Spam Detection Using Support Vector Machine in Comparison with Logistic Regression. 2023 Eighth International Conference on Science Technology Engineering and Mathematics (ICONSTEM), Chennai, India, pp. 1–5.
- Bindu, P., Mishra, R., and Thilagam, P.S. (2018). Discovering spammer communities in twitter. Journal of Intelligent Information Systems, vol. 51, no. 3, pp. 503–527.
- Cao, N., Shi, C., Lin, S., Lu, J., Lin, Y.,-R., and Lin C,-Y. (2015). Targetvue: Visual analysis of anomalous user behaviors in online communication systems. IEEE Transactions on Visualization and Computer Graphics, vol. 22, no. 1, pp. 280–289.
- Chang, K.-C, and Fan, C.-P. (2019). Cost-Efficient Adaboost-based Face Detection with FPGA Hardware Accelerator. 2019 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW), Yilan, Taiwan, pp. 1–2.
- Doss, L.M, and Gunasekaran, M. (2023). Evasion and Poison attacks on Logistic Regression-based Machine Learning Classification Model. 2023 Eighth International Conference on Science Technology Engineering and Mathematics (ICONSTEM), Chennai, India, pp. 1–8.
- Gonzalez, P.A. (2005). Fraud detection method for mobile telecommunication networks, EP.
- Gori, M., G. Monfardini, and F. Scarselli. (2005). A new model for learning in graph domains. in Proceedings. 2005 IEEE International Joint Conference on Neural Networks. IEEE.
- Gurajala, S., White, J., Hudson, H, and Matthews, J. (2016). Profile characteristics of fake Twitter accounts. Big Data Society, pp. 1–13.
- Hairani, H., Anggrawan, A., Wathan, A.I., Latif, K.A., Marzuki, K, and Zulfikri, M. (2021). The Abstract of Thesis Classifier by Using Naive Bayes Method. 2021 International Conference on Software Engineering & Computer Systems and 4th International Conference on Computational Science and Information Management (ICSECS-ICOCSIM), Pekan, Malaysia, pp. 312–315.
- Indumathi, N., Ramalakshmi, R, and Ajith, V. (2021). Analysis of risk factors in the Firework Industries: Using Decision Tree Classifier. 2021 International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), Greater Noida, India, pp. 811–814.
- Juszczyszyn, K & Kolaczek, G. (2019). Complex Networks Monitoring and Security and Fraud Detection for Enterprises. 2019 IEEE 28th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE), pp. 124–125.
- Manjula Devi, C., Gobinath, A., Padma Priya, S., Adithiyaa, M., Chandru, M.K and Jothi, M. (2024). Next-Generation Anomaly Detection Framework Leveraging Artificial Intelligence for Proactive Credit Card Fraud Prevention and Risk Management. 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT), Kamand, India, pp. 1–6.
- Mishra, S., Mallick, R.K, and Gadanayak, D.A. (2020). Islanding Detection of Microgrid using EMD and Random Forest Classifier. 2020 International Conference on Computational Intelligence for Smart Power System and Sustainable Energy (CISPSSE), Keonjhar, India, pp. 1–5.
- Molloy, I., Chari, S., Finkler, U., Wiggerman, M., Jonker, C., Habeck, T., Park, Y., Jordens, F and Schaik, R. (2016). Graph analytics for realtime scoring of cross-channel transactional fraud,” in Proceedings of International Conference on Financial Cryptography and Data Security, pp. 22–40.
- Niu, K., Jiao, H., Deng, N., and Gao, Z. (2016). A Real-Time Fraud Detection Algorithm Based on Intelligent Scoring for the Telecom Industry. 2016 International Conference on Networking and Network Application(NaNA), Hakodate, pp. 303–306.
- Park, N., et al. (2019). Estimating node importance in knowledge graphs using graph neural networks.” in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.
- Peng, L, and Lin, R. (2018). Fraud Phone Calls Analysis Based on Label Propagation Community Detection Algorithm. 2018 IEEE World Congress on Services, San Francisco, CA, 2018, pp. 23–24.
- Raman, R., Kumar, V., Pillai, B.G., Rabadiya, D., Divekar, R and Vachharajani, H. (2024). Detecting Credit Card Fraud: A Comparative Analysis of KNN, Random Forest, and Logistic Regression Methods. 2024 Second International Conference on Data Science and Information System (ICDSIS), Hassan, India, pp. 1–5.
- Sahoo, P. K., Mishra, S., Panigrahi, R., Bhoi, A. K., & Barsocchi, P. (2022). An Improvised Deep-Learning-Based Mask R-CNN Model for Laryngeal Cancer Detection Using CT Images. Sensors, 22(22), 8834.
- Shearer, C. (2000). The CRISP-DM model: The new blueprint for data mining,” J. Data Warehousing, vol. 5, no. 4, pp. 13–22.
- Sheng, C and Yu, H. (2022). An optimized prediction algorithm based on XGBoost. 2022 International Conference on Networking and Network Applications (NaNA), Urumqi, China, pp. 1–6.
- Smruthi, N. Harini. (2019). A Hybrid Scheme for Detecting Fake Accounts in Facebook. International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, 7:5S3.
- Tarmazakov, E, I. and Silnov, D, S. (2018). Modern approaches to prevent fraud in mobile communications networks. 2018 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), Moscow and St. Petersburg, Russia. pp. 379–381.
- Teng, H., Wang, C., Yang, Q., Chen X, and Li, R. (2023). Leveraging Adversarial Augmentation on Imbalance Data for Online Trading Fraud Detection. in IEEE Transactions on Computational Social Systems.
- Thar, K.W and Wai, T.T. (2024). Machine Learning Based Predictive Modelling for Fraud Detection in Digital Banking. 2024 IEEE Conference on Computer Applications (ICCA), Yangon, Myanmar, pp. 1–5.
- Vijay, V, and Verma, P. (2023). Variants of Naïve Bayes Algorithm for Hate Speech Detection in Text Documents. 2023 International Conference on Artificial Intelligence and Smart Communication (AISC), Greater Noida, India, pp. 18–21.
- Xuan, S., et al. (2018). Random forest for credit card fraud detection. in 2018 IEEE 15th International Conference on Networking, Sensing and Control (ICNSC). IEEE.
- Yulita, I.N., Paulus, E., Sholahuddin, A, and Novita, D. (2021). AdaBoost Support Vector Machine Method for Human Activity Recognition. 2021 International Conference on Artificial Intelligence and Big Data Analytics, Bandung, Indonesia, pp. 1–4.
- Zhao, M, and Song, H. (2022). Semantic Analysis based on Artificial Intelligence—Prediction of Telecom Fraud. 2022 4th International Conference on Frontiers Technology of Information and Computer (ICFTIC), Qingdao, China, pp. 924–927.
- Zhong, R., Dong, X., Lin, R. and Zou, H. (2019). An Incremental Identification Method for Fraud Phone Calls Based on Broad Learning System. 2019 IEEE 19th International Conference on Communication Technology (ICCT), Xi’an, China, pp. 1306–1310.
- Zhou, J et al. (2023). FraudAuditor: A Visual Analytics Approach for Collusive Fraud in Health Insurance. in IEEE Transactions on Visualization and Computer Graphics.
- Zulfikar, W.B., Gerhana, Y.A, and Rahmania, A.F. (2018). An Approach to Classify Eligibility Blood Donors Using Decision Tree and Naive Bayes Classifier. 2018 6th International Conference on Cyber and IT Service Management (CITSM), Parapat, Indonesia, pp. 1–5.
DOI: https://doi.org/10.2478/ijssis-2025-0043 | Journal eISSN: 1178-5608
Language: English
Submitted on: Jan 22, 2025
Published on: Aug 22, 2025
Published by: Professor Subhas Chandra Mukhopadhyay
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
Related subjects:
© 2025 Elisha Indarjit, Vipin Balyan, Marco Adonis, published by Professor Subhas Chandra Mukhopadhyay
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.