OPIN-ITP: Optimized Physics Informed Network with Trimmed Score Regression Based Insider Threats Prediction in Cloud Computing

B. Gayathri

doi:10.4108/eetsis.6134

Authors

B. Gayathri Bishop Heber College

DOI:

https://doi.org/10.4108/eetsis.6134

Keywords:

Insider threats, trimmed score regression, deep feature synthesis extraction, physics informed neural networks, Hunter-prey Optimization

Abstract

INTRODUCTION: Insider threats are a major issue for cyber security. In contrast to external attackers, insiders have more privileges and authorized access to data and resources, which can cause an organization great harm. To completely understand an insider's activities throughout the organization, a more sophisticated method is needed.

OBJECTIVES: Based on an organization's login activity, this study proposes a novel conceptual method for insider threat detection. Behavioural activities such as HTTP, Email and Login details are collected to create a dataset which is further processed for pre-processing using data transformation and Trimmed Score Regression (TSR).

METHODS: These pre-data are given to the feature extraction process using Deep Feature Synthesis (DFS) extraction. The extracted data are fed to Physics Informed Neural Networks (PINN) for insider threat detection.

RESULTS: The prediction process of PINN was improved through optimally choosing parameters such as learning rate and weight using Hunter-prey Optimization (HPO). The proposed model offers 68% detection rate, 98.4% accuracy, 5% FDR, 95% F1_score and 0.7005 sec execution time.

CONCLUSION: Observed outcomes are compared to other traditional approaches of validation. The contrast with traditional approaches shows that the proposed model provides better outcomes than in traditional models and is therefore a good fit for real-time threat prediction.

References

[1] Yuan Yuan S, Wu X. Deep learning for insider threat detection: Review, challenges and opportunities. Computers & Security. 2021;104:102221.

[2] Jeong M, Zo H. Preventing insider threats to enhance organizational security: The role of opportunity-reducing techniques. Telematics and Informatics. 2021;63:101670.

[3] Yu K, Tan L, Mumtaz S, Al-Rubaye S, Al-Dulaimi A, Bashir AK, Khan FA. Securing critical infrastructures: deep-learning-based threat detection in IIoT. IEEE Communications Magazine. 2021;59(10):76-82.

[4] Robayo TA. The Enemy Within: A Framework for Understanding the Lifecycle of the Malicious Insider Threat to Information Systems (Doctoral dissertation, Saint Leo University).

[5] Saxena N, Hayes E, Bertino E, Ojo P, Choo KK, Burnap P. Impact and key challenges of insider threats on organizations and critical businesses. Electronics. 2020;9(9):1460.

[6] Al-Shehari T, Alsowail RA. An insider data leakage detection using one-hot encoding, synthetic minority oversampling and machine learning techniques. Entropy. 2021;23(10):1258.

[7] Bao Y, Hilary G, Ke B. Artificial intelligence and fraud detection. Innovative Technology at the Interface of Finance and Operations: Volume I. 2022:223-47.

[8] Wei Y, Chow KP, Yiu SM. Insider threat prediction based on unsupervised anomaly detection scheme for proactive forensic investigation. Forensic Science International: Digital Investigation. 2021;38:301126.

[9] Chowdhury M, Ray B, Chowdhury S, Rajasegarar S. A novel insider attack and machine learning based detection for the internet of things. ACM Transactions on Internet of Things. 2021;2(4):1-23.

[10] Williams AD, Abbott SN, Shoman N, Charlton WS. Results from invoking artificial neural networks to measure insider threat detection & mitigation. Digital Threats: Research and Practice (DTRAP). 2021;3(1):1-20.

[11] Feng W, Wu Y, Fan Y. A new method for the prediction of network security situations based on recurrent neural network with gated recurrent unit. International Journal of Intelligent Computing and Cybernetics. 2020;13(1):25-39.

[12] Bu SJ, Cho SB. A convolutional neural-based learning classifier system for detecting database intrusion via insider attack. Information Sciences. 2020;512:123-36.

[13] Al-Mhiqani MN, Ahmed R, Abidin ZZ, Isnin SN. An integrated imbalanced learning and deep neural network model for insider threat detection. International Journal of Advanced Computer Science and Applications. 2021;12(1).

[14] Zeng, Y., Kang, Z., & Shi, Z. (2023). Secure data processing technology of distribution network opgw line with edge computing. EAI Endorsed Transactions on Scalable Information Systems, 10(3), e7-e7.

[15] Ahmed, S. H., & Aljuboori, A. F. (2023). Big Data Detection Utilizing Cloud Networks with Video Vision Techniques. EAI Endorsed Transactions on Scalable Information Systems, 10(5).

[16] Hong, W., Yin, J., You, M., Wang, H., Cao, J., Li, J., ... & Man, C. (2023). A graph empowered insider threat detection framework based on daily activities. ISA transactions, 141, 84-92.

[17] Yin, J., Tang, M., Cao, J., You, M., Wang, H., & Alazab, M. (2022). Knowledge-driven cybersecurity intelligence: software vulnerability coexploitation behavior discovery. IEEE transactions on industrial informatics, 19(4), 5593-5601.

[18] Al-Mhiqani MN, Ahmad R, Abidin ZZ, Abdulkareem KH, Mohammed MA, Gupta D, Shankar K. A new intelligent multilayer framework for insider threat detection. Computers & Electrical Engineering. 2022;97:107597.

[19] Anakath AS, Kannadasan R, Joseph NP, Boominathan P, Sreekanth GR. Insider Attack Detection Using Deep Belief Neural Network in Cloud Computing. Computer Systems Science & Engineering. 2022;41(2).

[20] Wang X, Fidge C, Nourbakhsh G, Foo E, Jadidi Z, Li C. Anomaly detection for insider attacks from untrusted intelligent electronic devices in substation automation systems. IEEE Access. 2022;10:6629-49.

[21] Nasir R, Afzal M, Latif R, Iqbal W. Behavioral based insider threat detection using deep learning. IEEE Access. 2021;9:143266-74.

[22] Alsowail RA, Al-Shehari T. A multi-tiered framework for insider threat prevention. Electronics. 2021;10(9):1005.

[23] Al Razib M, Javeed D, Khan MT, Alkanhel R, Muthanna MS. Cyber threats detection in smart environments using SDN-enabled DNN-LSTM hybrid framework. IEEE Access. 2022;10:53015-26.

[24] Sheykhkanloo NM, Hall A. Insider threat detection using supervised machine learning algorithms on an extremely imbalanced dataset. International Journal of Cyber Warfare and Terrorism (IJCWT). 2020;10(2):1-26.

[25] Haq MA, Khan MA, Alshehri M. Insider threat detection based on NLP word embedding and machine learning. Intell. Autom. Soft Comput. 2022;33:619-35.

[26] Meng W, Li W, Wang Y, Au MH. Detecting insider attacks in medical cyber–physical networks based on behavioral profiling. Future Generation Computer Systems. 2020;108:1258-66.

[27] Rabbani M, Wang YL, Khoshkangini R, Jelodar H, Zhao R, Hu P. A hybrid machine learning approach for malicious behaviour detection and recognition in cloud computing. Journal of Network and Computer Applications. 2020;151:102507.

[28] Asha, S., Shanmugapriya, D., & Padmavathi, G. (2023). Malicious insider threat detection using variation of sampling methods for anomaly detection in cloud environment. Computers and Electrical Engineering, 105, 108519.

[29] 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.

[30] Mishra P, Biancolillo A, Roger JM, Marini F, Rutledge DN. New data preprocessing trends based on ensemble of multiple preprocessing techniques. TrAC Trends in Analytical Chemistry. 2020;132:116045.

[31] Maliwat JY, Ylade PA, Regala RC, Cortez DM, Alipio AJ, Mata KE, Blanco MC. An Enhancement of Deep Feature Synthesis Algorithm Using Mean, Median, and Mode Imputation.

[32] Cai S, Wang Z, Wang S, Perdikaris P, Karniadakis GE. Physics-informed neural networks for heat transfer problems. Journal of Heat Transfer. 2021;143(6):060801.

[33] Naruei I, Keynia F, Sabbagh Molahosseini A. Hunter–prey optimization: Algorithm and applications. Soft Computing. 2022;26(3):1279-314.

[34] Dataset 1: https://kilthub.cmu.edu/articles/dataset/Insider_Threat_Test_Dataset/12841247/1

[35] Bouchama F, Kamal M. Enhancing Cyber Threat Detection through Machine Learning-Based Behavioral Modeling of Network Traffic Patterns. International Journal of Business Intelligence and Big Data Analytics. 2021;4(9):1-9.