Towards data fusion-based big data analytics for intrusion detection

References

• Abid, A., Jemili, F., & Korbaa, O. (2022). Distributed architecture of an intrusion detection system in industrial control systems. In C. Bădică, J. Treur, D. Benslimane, B. Hnatkowska, & M. Krótkiewicz (Eds.), Advances in computational collective intelligence. ICCCI 2022. Communications in computer and information science (Vol. 1653). Springer. https://doi.org/10.1007/978-3-031-16210-7_39.
   Google Scholar
• Abraham, M. A., & Koppen, M. M. (2018). “Intelligent Systems Design and Applications” pp: 240-245.
   Google Scholar
• Ahmadian, A., & Yazdi, H. S. (2021). A new feature selection approach for intrusion detection using machine learning algorithms. Journal of Network and Computer Applications, 184, 103006.
   Google Scholar
• Aleroud, A., & Karabatis, G. (2017). Contextual information fusion for intrusion detection: a survey and taxonomy. Knowledge and Information Systems, 52(3), 563–619. https://doi.org/10.1007/s10115-017-1027-3
   Web of Science ®Google Scholar
• Anjum, N., Latif, Z., Lee, C., Shoukat, I. A., & Iqbal, U. (2021). MIND: A multi-source data fusion scheme for intrusion detection in networks. Sensors, 21(14), 4941. https://doi.org/10.3390/s21144941
   PubMed Web of Science ®Google Scholar
• Ankur, G. (2021a). “Book : Neo4j Cookbook”, pp: 2, 2021.
   Google Scholar
• Ankur, G. (2021b). “Book : Neo4j Cookbook”, pp: 51-52, 2021.
   Google Scholar
• Arachchilage, D. D. P. C., & Wijekoon, K. M. K. (2021). Improving the performance of intrusion detection systems by ensemble learning. Proceedings of the 2021 3rd international conference on computer science and artificial intelligence (pp. 27–33).
   Google Scholar
• Balani, N., Chavan, P., & Ghonghe, M. (2022). Design of high-speed blockchain-based sidechaining peer to peer communication protocol over 5G networks. Multimedia Tools and Applications, 81, 36699–36713. doi:10.1007/s11042-021-11604-6
   Web of Science ®Google Scholar
• Ben Fekih, R., & Jemili, F. (2018). Distributed architecture of an Intrusion detection system based on cloud computing and big data techniques. 8th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT 2018).
   Google Scholar
• Ben Sujitha, B., Roja Ramani, R., & Wari, P. (2020). Intrusion detection system using fuzzy genetic approach. International Journal of Advanced Research in Computer and Communication Engineering, 1(10).
   Google Scholar
• DARPA. (1999). DATA: (https://web.cs.dal.ca/~riyad/Site/Download.html).
   Google Scholar
• Essid, M., & Jemili, F. (2016). Combining intrusion detection datasets using MapReduce. proceedings of the International Conference on Systems, Man, and Cybernetics.
   Google Scholar
• Farhan, H., Sattar, S., Raza, H., & Ali, S. (2021). Intrusion detection system using hybrid machine learning techniques. 2021 5th international conference on computing methodologies and communication (ICCMC), pp. 164–170. IEEE.
   Google Scholar
• Geepalla, E., & Asharif, S. (2020). “Analysis of Physical Access Control System for Understanding Users Behavior and Anomaly Detection Using Neo4j”. ICEMIS'20: Proceedings of the 6th International Conference on Engineering & MIS 2020, September 2020, Article No. 81, pp. 1–6, https://doi.org/10.1145/3410352.3410817.
   Google Scholar
• Group BDW. (2020). “Big Data Analytics for Security Intelligence”. https://downloads.cloudsecurityalliance.org/initiatives/bdwg/Big_Data_Analytics_for_Security_Intelligence.pdf.
   Google Scholar
• Hadoop. (2020). https://hadoop.apache.org/.
   Google Scholar
• Hadoop Architecture. (2020). https://hadoop.apache.org/.
   Google Scholar
• He, H., Sun, X., He, H., Zhao, G., He, L., & Ren, J. (2019). A novel multimodal-sequential approach based on multi-view features for network intrusion detection. IEEE Access, 7, 183207–183221. doi:10.1109/ACCESS.2019.2959131
   Google Scholar
• Islam, M. S. S., & Islam, A. R. (2021). Deep convolutional neural networks for network intrusion detection: A review. arXiv preprint arXiv:2101.10301.
   Google Scholar
• Jeyepalan, D. P., & Kirubakaran, E. (2019). High performance network intrusion detection model using graph databases. International journal of computational intelligence and information security December 2019.
   Google Scholar
• KDD Cup. (1999). Data :(http://kdd.ics.uci.edu/databases/kddcup99/).
   Google Scholar
• Kharche, D., & Patil, R. (2020). Use of genetic algorithm with fuzzy class association rule mining for intrusion detection. International Journal of Computer Science and Information Technologies.
   Google Scholar
• Khraisat, A., Gondal, I., Vamplew, P., Kamruzzaman, J., & Alazab, A. (2020). Hybrid intrusion detection system based on the stacking ensemble of c5 decision tree classifier and one class support vector machine. Electronics, 9(1), 173. https://doi.org/10.3390/electronics9010173
   Web of Science ®Google Scholar
• Li, D., & Li, Y. (2021). Intrusion detection system based on random forest with feature selection. Journal of Computer and Communications, 9(5), 118–128.
   Google Scholar
• Meng, T., Jing, X., Yan, Z., & Pedrycz, W. (2020). A survey on machine learning for data fusion. Information Fusion, 57, 115–129. ISSN 1566-2535. https://doi.org/10.1016/j.inffus.2019.12.001
   Web of Science ®Google Scholar
• Mhatre, M., Kashid, H., Jain, T., & Chavan, P. (2022). BCPIS: Blockchain-based counterfeit product identification system. Journal of Applied Security Research, 1–26. doi: 10.1080/19361610.2022.2086784
   Web of Science ®Google Scholar
• Pradhan, A. B., & Das, A. K. (2021). An intrusion detection system using hybrid approach of rule-based and machine learning. Journal of Cybersecurity and Information Management, 4(1), 1–14.
   Google Scholar
• Qiu, X., Liang, S., Meng, L., et al. (2021). Exploiting feature fusion and long-term context dependencies for simultaneous ECG heartbeat segmentation and classification. International Journal of Data Science and Analytics, 11, 181–193. https://doi.org/10.1007/s41060-020-00239-9
   Google Scholar
• Radhakrishna, V., Aljawarneh, S., Kumar, P. V., Janaki, V., & Cheruvu, A. (2019). Tree based data fusion approach for mining temporal patterns. Proceedings of the 5th international conference on engineering and MIS (ICEMIS ‘19). association for computing machinery, New York, NY, USA.
   Google Scholar
• Singh, O. P., Singh, A. K., & Zhou, H. (2022). Multimodal fusion-based image hiding algorithm for secure healthcare system. IEEE Intelligent Systems, doi: 10.1109/MIS.2022.3210331
   PubMed Web of Science ®Google Scholar
• Sun, F., Gao, R., Xing, W., et al. (2022). Deep fusion for travel time estimation based on road network topology. IEEE Intelligent Systems, doi: 10.1109/MIS.2022.3173427
   Web of Science ®Google Scholar
• Tavallaee, M., Bagheri, E., Lu, W., & Ghorbani, A. (2019). A detailed analysis of the KDD CUP 99 data set. Proceedings of the 2019 IEEE symposium on computational intelligence in security and defense applications (CISDA 2019).
   Google Scholar
• Velampalli, S., Mookiah, L., & Eberle, W. (2019). Discovering suspicious patterns using a graph based approach. Proceedings of the thirty-second international florida artificial intelligence research society conference (FLAIRS-32).
   Google Scholar
• Yang, X., Yu, Y., & Hu, L. (2021). Intrusion detection system based on support vector machine and recursive feature elimination. Journal of Ambient Intelligence and Humanized Computing, 1–12.
   Google Scholar