Overview of Anomaly Detection Techniques across Different Domains: A Systematic Review
DOI:
https://doi.org/10.22399/ijcesen.522Keywords:
Anomaly Detection, Machine Learning, Deep Learning, Artificial Intelligence, Healthcare and Cyber securityAbstract
An anomaly, defined as something that deviates from what is normal, expected, or usual. It signifies abnormality or an irregularity that stands out from typical behaviours or patterns. Detecting anomalies is significant among numerous sectors due to the reasons of signal potential difficulties or opportunities. For an instance, in retail, detecting anomalies in sales data might prompt for further analysis into operational issues or customer behaviour to reduce losses and capitalize on its trends. Hence, different techniques are used for Anomaly Detection. However, anomaly detection using manual method are measured for time consuming, prone to error and can be tedious process. Therefore, different approaches have been considered for anomaly detection as AI (Artificial Intelligence) methods are efficient, faster, provides high level accuracy by effectively detecting the abnormalities. Owing to these aspects, this paper focuses on compiling different techniques and emphasizes on reviewing all anomaly detection using numerous techniques like ML (Machine Learning) and DL (Deep Learning) classifiers, statistical methods, one-class classification, clustering and density-based models which helps with identifying and comprehending the diversity of detection techniques that are applied in various domains like finance, retail, healthcare and cyber security. Various existing researches on anomaly detection are reviewed in the study. In addition to an overview, certain studies also deals with applications of detection models and future trends are reviewed in precise. Finally, the challenges are identified through the analysis of existing researchers and future recommendations are provided for overcoming the gaps that are intended to create promising work in this area.
References
Riggs, H., Tufail, S., Parvez, I., Tariq, M., Khan, M. A., Amir, A., . . . Sarwat, A. I. J. S. (2023). Impact, vulnerabilities, and mitigation strategies for cyber-secure critical infrastructure. Sensors (Basel). 17;23(8):4060. doi: 10.3390/s23084060.
Hosseinzadeh, M., Rahmani, A. M., Vo, B., Bidaki, M., Masdari, M., & Zangakani, M. J. S. C. (2021). Improving security using SVM-based anomaly detection: issues and challenges. Soft Computing 25(4), 3195-3223. DOI:10.1007/s00500-020-05373-x
Duo, W., Zhou, M., & Abusorrah, A. J. I. C. J. o. A. S. (2022). A survey of cyber attacks on cyber physical systems: Recent advances and challenges. 9(5), 784-800.
Hussein, A., Chadad, L., Adalian, N., Chehab, A., Elhajj, I. H., & Kayssi, A. J. J. o. C. S. T. (2020). Software-Defined Networking (SDN): the security review. Journal of Cyber Security 4(1), 1-66.
Khan, M. A., & Malaika, M. (2021). Central Bank Risk Management, Fintech, and Cybersecurity. International Monetary Fund.
Mothukuri, V., Khare, P., Parizi, R. M., Pouriyeh, S., Dehghantanha, A., & Srivastava, G. J. I. I. o. T. J. (2021). Federated-learning-based anomaly detection for IoT security attacks. IEEE Internet of Things Journal 9(4), 2545-2554. DOI: 10.1109/JIOT.2021.3077803
Inuwa, M. M., & Das, R. J. I. o. T. (2024). A comparative analysis of various machine learning methods for anomaly detection in cyber attacks on IoT networks. Internet of Things 26, 101162. DOI: 10.1016/j.iot.2024.101162
Zhao, H., Li, Y., He, N., Ma, K., Fang, L., Li, H., & Zheng, Y. J. I. T. o. M. I. (2021). Anomaly detection for medical images using self-supervised and translation-consistent features. IEEE Transactions on Medical Imaging 40 (12), 3641-3651
Rahman, M.S., Halder, S., Uddin, M.A. et al. (2021). An efficient hybrid system for anomaly detection in social networks. Cybersecur 4, 10. https://doi.org/10.1186/s42400-021-00074-w
Dumitrescu, B., Băltoiu, A., & Budulan, Ş. J. I. A. (2022). Anomaly detection in graphs of bank transactions for anti money laundering applications. IEEE Access 10, 47699-47714. DOI: 10.1109/ACCESS.2022.3170467
Kale, R., Lu, Z., Fok, K. W., & Thing, V. L. J. a. e.-p. (2022). A Hybrid Deep Learning Anomaly Detection Framework for Intrusion Detection. arXiv: 2212.00966.
Bammidi, T. R., Gutta, L. M., Kotagiri, A., Samayamantri, L. S., & krishna Vaddy, R. J. I. J. o. M. E. f. S. D. (2024). The Crucial Role of Data Quality in Automated Decision-Making Systems. International Journal of Management Education for Sustainable Development 7(7), 1-22.
Bakumenko, A., & Elragal, A. J. S. (2022). Detecting anomalies in financial data using machine learning algorithms. Systems 10(5), 130. DOI: 10.3390/systems10050130
Wang, Y., Perry, M., Whitlock, D., & Sutherland, J. W. J. J. o. M. S. (2022). Detecting anomalies in time series data from a manufacturing system using recurrent neural networks. Journal of Manufacturing Systems 62, 823-834. DOI: 10.1016/j.jmsy.2020.12.007
Cabrera, D., & Cabrera, L. L. J. J. o. S. T. P. (2023). The Steps to Doing a Systems Literature Review (SLR).
Antons, D., Breidbach, C. F., Joshi, A. M., & Salge, T. O. J. O. R. M. (2023). Computational literature reviews: Method, algorithms, and roadmap. Organizational Research Methods 26(1), 107-138 DOI:10.1177/1094428121991230.
Galeano-Brajones, J., Carmona-Murillo, J., Valenzuela-Valdés, J. F., & Luna-Valero, F. J. S. (2020). Detection and mitigation of DoS and DDoS attacks in IoT-based stateful SDN: An experimental approach. Sensors (Basel) 20(3), 816. doi: 10.3390/s20030816.
Polat, H., Polat, O., & Cetin, A. J. S. (2020). Detecting DDoS attacks in software-defined networks through feature selection methods and machine learning models. Sustainability 12(3), 1035. DOI:10.3390/su12031035
Novaes, M. P., Carvalho, L. F., Lloret, J., & Proença Jr, M. L. J. F. G. C. S. (2021). Adversarial Deep Learning approach detection and defense against DDoS attacks in SDN environments. Future Generation Computer Systems 125, 156-167. DOI: 10.1016/j.future.2021.06.047
Gadze, J. D., Bamfo-Asante, A. A., Agyemang, J. O., Nunoo-Mensah, H., & Opare, K. A.-B. J. T. (2021). An investigation into the application of deep learning in the detection and mitigation of DDOS attack on SDN controllers. Technologies 9(1), 14. DOI:10.3390/TECHNOLOGIES9010014
Al-Dunainawi, Y., Al-Kaseem, B.R., & Al-Raweshidy, H.S. (2023). Optimized Artificial Intelligence Model for DDoS Detection in SDN Environment. IEEE Access, 11, 106733-106748. DOI:10.1109/ACCESS.2023.3319214
Kyaw, A. T., Oo, M. Z., & Khin, C. S. (2020). Machine-learning based DDOS attack classifier in software defined network. 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON),
Wong, M. L., Arjunan, T. J. E. T. i. M. I., & Data, B. (2024). Real-Time Detection of Network Traffic Anomalies in Big Data Environments Using Deep Learning Models. International Journal for Research in Applied Science and Engineering Technology 16(1), 1-11. DOI: 10.22214/ijraset.2024.58946.
Nadeem, M. W., Goh, H. G., Ponnusamy, V., Aun, Y. J. C., Materials, & Continua. (2022). DDoS Detection in SDN using Machine Learning Techniques. CMC 71(1). DOI: 10.32604/cmc.2022.021669
Mukherjee, I., Sahu, N.K. & Sahana, S.K. (2023). Simulation and Modeling for Anomaly Detection in IoT Network Using Machine Learning. Int J Wireless Inf Networks 30(2);173–189 (2023). https://doi.org/10.1007/s10776-021-00542-7
Fotiadou, K., Velivasaki, T.N., Voulkidis, A.C., Skias, D., Tsekeridou, S., & Zahariadis, T.B. (2021). Network Traffic Anomaly Detection via Deep Learning. Inf., 12(5), 215. DOI:10.3390/info12050215
Hwang, R.-H., Peng, M.-C., Huang, C.-W., Lin, P.-C., & Nguyen, V.-L. J. I. A. (2020). An unsupervised deep learning model for early network traffic anomaly detection. IEEE Access, 8, 30387-30399. DOI: 10.1109/access.2020.2973023
Xu, W., Jang-Jaccard, J., Singh, A., Wei, Y., & Sabrina, F. (2021). Improving Performance of Autoencoder-Based Network Anomaly Detection on NSL-KDD Dataset. IEEE Access, 9, 140136-140146. DOI:10.1109/ACCESS.2021.3116612
Koren, O., Koren, M., & Peretz, O. J. E. A. o. A. I. (2023). A procedure for anomaly detection and analysis. Engineering Applications of Artificial Intelligence 117, 105503. DOI: 10.1016/j.engappai.2022.105503
Rafique, S. H., Abdallah, A., Musa, N. S., & Murugan, T. J. S. (2024). Machine learning and deep learning techniques for internet of things network anomaly detection—current research trends. Sensors (Basel) 24(6):1968. doi: 10.3390/s24061968.
Chikodili, N.B., Abdulmalik, M.D., Abisoye, O.A., Bashir, S.A. (2021). Outlier Detection in Multivariate Time Series Data Using a Fusion
of K-Medoid, Standardized Euclidean Distance and Z-Score. In: Misra, S., Muhammad-Bello, B. (eds) Information and Communication Technology and Applications. ICTA 2020. Communications in Computer and Information Science, vol 1350. Springer, Cham. https://doi.org/10.1007/978-3-030-69143-1_21
Ullah, I., & Mahmoud, Q. H. J. I. A. (2021). Design and development of a deep learning-based model for anomaly detection in IoT networks. IEEE Access 9, 103906-103926. doi: 10.1109/ACCESS.2021.3094024
Alqahtani, H. (2020). Cyber Intrusion Detection Using Machine Learning Classification Techniques. In book: Computing Science, Communication and Security (pp.121-131) DOI:10.1007/978-981-15-6648-6_10
Doreswamy, Hooshmand, M. K., & Gad, I. J. C. T. o. I. T. (2020). Feature selection approach using ensemble learning for network anomaly detection. CAAI Transactions on Intelligence Technology 5(4), 283-293. DOI: 10.1049/trit.2020.0073
Pu, G., Wang, L., Shen, J., Dong, F. J. T. S., & Technology. (2020). A hybrid unsupervised clustering-based anomaly detection method. Tsinghua Science and Technology 26(2), 146-153. doi: 10.26599/TST.2019.9010051.
Nanehkaran, Y., Licai, Z., Chen, J., Jamel, A. A., Shengnan, Z., Navaei, Y. D., . . . Computing, M. (2022). Anomaly Detection in Heart Disease Using a Density‐Based Unsupervised Approach. Wireless Communications and Mobile Computing, Article ID 6913043, 14 pages DOI: 10.1155/2022/6913043
Xu, H., Wang, Y., Jian, S., Liao, Q., Wang, Y., Pang, G. J. I. T. o. K., & Engineering, D. (2024). Calibrated one-class classification for unsupervised time series anomaly detection. arXiv:2207.12201v2
Suseendran, G., Chandrasekaran, E., Akila, D., & Sasi Kumar, A. (2020). Banking and FinTech (financial technology) embraced with IoT device. Data Management, Analytics and Innovation: Proceedings of ICDMAI 2019, Volume 1,
Da, Z., Dun, Y., Liu, C., Liang, Y., Xue, Y., & Qian, X. J. K.-B. S. (2023). Anomaly detection framework for unmanned vending machines. Knowledge-Based Systems 262, 110251. DOI: https://doi.org/10.1016/j.knosys.2023.110251
Abououf, M., Singh, S., Mizouni, R., & Otrok, H. (2024). Explainable AI for Event and Anomaly Detection and Classification in Healthcare Monitoring Systems. IEEE Internet of Things Journal, 11, 3446-3457. DOI: 10.1109/JIOT.2023.3296809
Ahmad, Z., Shahid Khan, A., Nisar, K., Haider, I., Hassan, R., Haque, M. R., . . . Rodrigues, J. J. J. A. S. (2021). Anomaly detection using deep neural network for IoT architecture. Applied Sciences 11(15), 7050. DOI: 10.3390/app11157050
Kapoor, A., Sengar, V., George, N., Vatsal, V., Gubbi, J., P., B., & Pal, A. (2023). Concept-Based Anomaly Detection in Retail Stores for Automatic Correction Using Mobile Robots. 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 163-170. DOI: 10.1109/SMC53992.2023.10394209
Yaseen, A. J. S. S. R. o. A. M. L. (2023). The role of machine learning in network anomaly detection for cybersecurity. SSRAML SageScience, 1(1), 1–15.
Al-Alawi, A. I., & Al-Bassam, M. S. A. J. J. o. X. U. (2020). The significance of cybersecurity system in helping managing risk in banking and financial sector. Journal of Xidian University 14(7), 1523-1536. DOI: 10.37896/jxu14.7/174
Varma, P., Nijjer, S., Sood, K., Grima, S., & Rupeika-Apoga, R. J. R. (2022). Thematic Analysis of Financial Technology (Fintech) Influence on the Banking Industry. Risks 10(10), 186. DOI: DOI:10.3390/risks10100186
Shvetsova, N., Bakker, B., Fedulova, I., Schulz, H., & Dylov, D. V. J. I. A. (2021). Anomaly detection in medical imaging with deep perceptual autoencoders. IEEE Access, 9, 118571-118583. DOI: 10.1109/ACCESS.2021.3107163
Guha, A., & Samanta, D. (2021). Hybrid approach to document anomaly detection: an application to facilitate RPA in title insurance. International Journal of Automation and Computing, 18(1), 55-72.
Palakurti, N. R. (2024). Challenges and future directions in anomaly detection. In Practical Applications of Data Processing, Algorithms, and Modeling (pp. 269-284). IGI Global.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 International Journal of Computational and Experimental Science and Engineering
This work is licensed under a Creative Commons Attribution 4.0 International License.
