International Journal of Computer Network and Information Security (IJCNIS)
IJCNIS Vol. 17, No. 6, 8 Dec. 2025
Cover page and Table of Contents: PDF (size: 1481KB)
ITD-GMJN: Insider Thread Detection in Cloud Computing using Golf Optimized MICE based Jordan Neural Network
PDF (1481KB), PP.116-132
Views: 0 Downloads: 0
Author(s)
B. GAYATHRI
1,*
Index Terms
Cloud Computing, MICE, Behavioral Log Files, Golf Optimization Algorithm, LS-AE and Jordan Neural Network
Abstract
Cloud computing refers to a high-level network architecture that allows consumers, authorized users, owners, and users to swiftly access and store their data. These days, the user's internal risks have a significant impact on this cloud. An intrusive party is established as a network member and presented as a user. Once they have access to the network, they will attempt to attack or steal confidential information while others are exchanging information or conversing. For the cloud network's external security, there are numerous options. But it's important to deal with internal or insider threats. Thus in the proposed work, an advanced deep learning with optimized missing value imputation is developed to mitigate insider thread in the cloud system. Behavioral log files were taken in an organization which is split into sequential data and standalone data based on the login process. This data was not ready for the detection process due to improper data samples so it was pre-processed using Multivariate Imputation by Chained Equations (MICE) imputation. In this imputation process, the estimation parameter was optimally chosen using the Golf Optimization Algorithm (GOA). After the missing values were filled, the data proceeded to the extraction process. In this, the sequential data are proceeded for the domain extractor and the standalone data are proceeded for Long Short-Term Memory-Autoencoder (LS-AE). Both features are fused to create a single data which is further given to the detection process using Jordan Neural Network (JNN). The proposed method offers 96% accuracy, 92% recall, 91.6% specificity, 8.39% fall out and 8% Miss Rate. The results showed that the recommended JNN detection model has successfully detected insider threads in a cloud system.
Cite This Paper
B. Gayathri, "ITD-GMJN: Insider Thread Detection in Cloud Computing using Golf Optimized MICE based Jordan Neural Network", International Journal of Computer Network and Information Security(IJCNIS), Vol.17, No.6, pp.116-132, 2025. DOI:10.5815/ijcnis.2025.06.08
Reference
[1]S. El Kafhali, I. El Mir, and M. Hanini, “Security threats, defense mechanisms, challenges, and future directions in cloud computing,” Archives of Computational Methods in Engineering, 2022, vol. 29, no. 1, pp. 223-246.
[2]J. R. Twagiramungu, “Transformation of organizations through cloud technologies–challenges & benefits,” A case study in Rwanda, 2022.
[3]F. Thabit, S. A. H. Alhomdy, A. Alahdal, and S. B. Jagtap, “Exploration of security challenges in cloud computing: Issues, threats, and attacks with their alleviating techniques,” Journal of Information and Computational Science, 2020, vol. 12, no. 10.
[4]R. Rani, M. A. Azraai, R. M. Qibtiah, H. N. Daud, H. Imran, and S. M. Zulfadhli, “A Development of Cyber-Physical Intelligent Model Based Multi-factor authentication of un authorised device for Insider Attacks,” Review of International Geographical Education Online, 2021, vol. 11, no. 7.
[5]A. Nicolaou, S. Shiaeles, and N. Savage, “Mitigating insider threats using bio-inspired models,” Applied Sciences, 2020, vol. 10, no. 15, pp. 5046.
[6]S. Karthick, “Semi Supervised Hierarchy Forest Clustering and KNN Based Metric Learning Technique for Machine Learning System,” Journal of Advanced Research in Dynamical and Control Systems, vol. 9, pp. 2679-2690, 2023.
[7]A. Loukaka, and S. S. Rahman, “Security Professionals Must Reinforce Detect Attacks to Avoid Unauthorized Data Exposure,” Information Technology in Industry, 2020, vol. 8, no. 1.
[8]Y. Wei, K. P. Chow, and S. M. Yiu, “Insider threat prediction based on unsupervised anomaly detection scheme for proactive forensic investigation,” Forensic Science International: Digital Investigation, 2021, vol. 38, pp. 301126.
[9]M. Sravanthi, G. Suchithra, and P. Vennela, “Cyber Threat Detection Based On Artificial Neural Networks Using Event Profiles,” 2023.
[10]M. P. Novaes, L. F. Carvalho, J. Lloret, and M. L. Proenca, “Long short-term memory and fuzzy logic for anomaly detection and mitigation in software-defined network environment,” IEEE Access, 2020, vol. 8, pp. 83765-83781.
[11]D. C. Le, and N. Zincir‐Heywood, “Exploring anomalous behaviour detection and classification for insider threat identification,” International Journal of Network Management, 2021, vol. 31, no. 4, pp. e2109.
[12]Mehmood, M., Amin, R., Muslam, M. M. A., Xie, J., & Aldabbas, H. (2023). Privilege escalation attack detection and mitigation in cloud using machine learning. IEEE Access.
[13]Suganya, M., & Sasipraba, T. (2023). Stochastic Gradient Descent long short-term memory based secure encryption algorithm for cloud data storage and retrieval in cloud computing environment. Journal of Cloud Computing, 12(1), 74.
[14]N. Elmrabit, S. H. Yang, L. Yang, and H. Zhou, “Insider threat risk prediction based on Bayesian network,” Computers & Security, 2020, vol. 96, pp. 101908.
[15]W. Meng, W. Li, Y. Wang, and M. H. Au, “Detecting insider attacks in medical cyber–physical networks based on behavioral profiling,” Future Generation Computer Systems, 2020, vol. 108, pp. 1258-1266.
[16]S. J. Bu, and S. B. Cho, “A convolutional neural-based learning classifier system for detecting database intrusion via insider attack,” Information Sciences, 2020, vol. 512, pp. 123-136.
[17]W. Hong, J. Yin, M. You, H. Wang, J. Cao, J. Li, and C. Man, “A graph empowered insider threat detection framework based on daily activities,” ISA transactions, 2023, vol. 141, pp. 84-92.
[18]D. C. Le, N. Zincir-Heywood, and M. I. Heywood, “Analyzing data granularity levels for insider threat detection using machine learning,” IEEE Transactions on Network and Service Management, 2020, vol. 17, no. 1, pp. 30-44.
[19]D. W. Chadwick, W. Fan, G. Costantino, R. De Lemos, F. Di Cerbo, I. Herwono, and X. S. Wang, “A cloud-edge based data security architecture for sharing and analysing cyber threat information,” Future generation computer systems, 2020, vol. 102, pp. 710-722.
[20]N. M. Sheykhkanloo, and A. Hall, “Insider threat detection using supervised machine learning algorithms on an extremely imbalanced dataset,” International Journal of Cyber Warfare and Terrorism (IJCWT), 2020, vol. 10, no. 2, pp. 1-26.
[21]M. Rabbani, Y. L. Wang, R. Khoshkangini, H. Jelodar, R. Zhao, and P. Hu, “A hybrid machine learning approach for malicious behaviour detection and recognition in cloud computing,” Journal of Network and Computer Applications, 2020, vol. 151, pp. 102507.
[22]F. Bouchama, and M. Kamal, “Enhancing Cyber Threat Detection through Machine Learning-Based Behavioral Modeling of Network Traffic Patterns,” International Journal of Business Intelligence and Big Data Analytics, 2021, vol. 4, no. 9, pp. 1-9.
[23]F. D. Herliani, and A. Kudus, “Penanganan Data Missing dengan Algoritma Multivariate Imputation By Chained Equations (MICE),” DataMath: Journal of Statistics and Mathematics, 2023, vol. 1, no. 1, pp. 35-42.
[24]Z. Montazeri, T. Niknam, J. Aghaei, O. P. Malik, M. Dehghani, and G. Dhiman, “Golf Optimization Algorithm: A New Game-Based Metaheuristic Algorithm and Its Application to Energy Commitment Problem Considering Resilience,” Biomimetics, 2023, vol. 8, no. 5, pp. 386.
[25]L. Su, and X. Ling, “Estimating weak pulse signal in chaotic background with Jordan neural network,” Complexity, 2020, pp. 1-14.
[26]Dataset1:https://kilthub.cmu.edu/articles/dataset/Insider_Threat_Test_Dataset/12841247/1
[27]Dataset 1: https://www.kaggle.com/datasets/kiranmahesh/nslkdd?select=kdd.
[28]Dataset 2: https://research.unsw.edu.au/projects/unsw-nb15-dataset
[29]Aydın, H., Orman, Z., & Aydın, M. A. (2022). A long short-term memory (LSTM)-based distributed denial of service (DDoS) detection and defense system design in public cloud network environment. Computers & Security, 118, 102725.
[30]Aslan, Ö., Ozkan-Okay, M., & Gupta, D. (2021). Intelligent behavior-based malware detection system on cloud computing environment. IEEE Access, 9, 83252-83271.
[31]Gao, J. (2022). Network intrusion detection method combining CNN and BiLSTM in cloud computing environment. Computational intelligence and neuroscience, 2022.
[32]T. Thilagam, and R. Aruna, “Intrusion detection for network based cloud computing by custom RC-NN and optimization,” ICT Express. 2021, vol. 7, no. 4, pp. 512-520.