• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

Year: 2022, Volume: 15, Issue: 36, Pages: 1769-1778

Original Article

Internet of Things with Deep Learning Enabled Fraud Detection in Surveillance Video Processing

Received Date:18 June 2022, Accepted Date:19 August 2022, Published Date:21 September 2022


Background: In the present digital era, fraud detection using surveillance video has become a mandatory tool to determine the occurrence of abnormal events in an automated way. Since the traditional visual inspection of surveillance videos for fraud detection is time-consuming and labour-intensive, intelligent fraud detection approaches based on Deep Learning (DL) concepts have been presented in the literature. Methods: This paper designs a DL with optimal classification based on fraud detection in a video surveillance system, named the DLOC-FDVS technique. The proposed DLOC-FDVS technique aims to examine the surveillance videos for the existence of frauds (i.e., robbery) in the IoT environment. At the initial stage, IoT enables the data acquisition and frame conversion process to be carried out. For fraud detection, densely connected networks (DenseNet-169) feature extractor and optimal Long Short Term Memory (LSTM) classifiers are applied. Finally, the Grasshopper Optimization Algorithm (GOA) is utilised to alter the LSTM model’s hyperparameters. It is frequently employed in a number of industrial settings and achieves suitable answers due to its ease of deployment and excellent precision. Findings: The DLOC-FDVS model is experimentally validated using a benchmark anomaly detection dataset from the Kaggle repository, which comprises 211 frames of normal video and 100 frames of fraud video. The experimental results show that the suggested model is an excellent fraud detection tool in the IoT context, achieving maximum precision, recall, F1score, and AUC of 96.56%, 96.56%, 96.56%, and 96.56% respectively. Novelty: The use of GOA for hyperparameter adjustment of the LSTM model for fraud detection demonstrates the work’s uniqueness. As a result, the DLOC-FDVS model may be used to identify fraud in real-time surveillance recordings.

Keywords: Video surveillance; Deep learning; Video processing; Internet of Things; Fraud detection; Hyperparameter tuning


  1. Shadroo S, Rahmani AM, Rezaee A. Survey On The application of Deep Learning in Internet of Things (IoT) Telecommunication Systems. 2022;79(4):601–627.
  2. Datta D, Sarkar NI. Deep Learning Frameworks for Internet of Things. In: Internet of Things. (pp. 137-161) Springer International Publishing. 2022.
  3. Ileberi E, Sun Y, Wang Z. A machine learning based credit card fraud detection using the GA algorithm for feature selection. Journal of Big Data. 2022;9(1):1–17.
  4. Öğrek M, Öğrek E, Bahtiyar Ş. A deep learning method for fraud detection in financial systems: Poster. Proceedings of the 12th Conference on Security and Privacy in Wireless and Mobile Networks. 2019;p. 298–299.
  5. Rai AK, Dwivedi RK. Fraud Detection in Credit Card Data using Unsupervised Machine Learning Based Scheme. 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC). 2020;p. 421–426.
  6. Soleymanzadeh R, Aljasim M, Qadeer MW, Kashef R. Cyberattack and Fraud Detection Using Ensemble Stacking. AI. 2022;3(1):22–36.
  7. Sarker IH. Machine Learning: Algorithms, Real-World Applications and Research Directions. SN Computer Science. 2021;2(3):1–21.
  8. Le Q, Miralles-Pechuán L, Sayakkara A, Le-Khac NAA, Scanlon M. Identifying Internet of Things software activities using deep learning-based electromagnetic side-channel analysis. Forensic Science International: Digital Investigation. 2021;39:301308.
  9. Chen JIZIZ, Lai KLL. Deep Convolution Neural Network Model for Credit-Card Fraud Detection and Alert. June 2021. 2021;3(2):101–112.
  10. Supriya M, Deepa AJ. Machine learning approach on healthcare big data: a review. Big Data and Information Analytics. 2020;5(1):58–75.
  11. Pustokhina IV, Pustokhin DA, Vaiyapuri T, Gupta D, Kumar S, Shankar K. An automated deep learning based anomaly detection in pedestrian walkways for vulnerable road users safety. Safety Science. 2021;142:105356.
  12. Ullah W, Ullah A, Hussain T, Muhammad K, Heidari AA, Del Ser J, et al. Artificial Intelligence of Things-assisted two-stream neural network for anomaly detection in surveillance Big Video Data. Future Generation Computer Systems. 2022;129:286–297.
  13. Murugesan M, Thilagamani S. Efficient anomaly detection in surveillance videos based on multi layer perception recurrent neural network. Microprocessors and Microsystems. 2020;79:103303.
  14. Murugesan M, Thilagamani S. Efficient anomaly detection in surveillance videos based on multi layer perception recurrent neural network. Microprocessors and Microsystems. 2020;79:103303.
  15. Huang C, Wu Z, Wen J, Xu Y, Jiang Q, Wang Y. Abnormal Event Detection Using Deep Contrastive Learning for Intelligent Video Surveillance System. IEEE Transactions on Industrial Informatics. 2022;18(8):5171–5179.
  16. Vosta S, Yow KCC. A CNN-RNN Combined Structure for Real-World Violence Detection in Surveillance Cameras. Applied Sciences. 2022;12(3):1021.
  17. Ullah W, Ullah A, Hussain T, Khan ZA, Baik SW. An Efficient Anomaly Recognition Framework Using an Attention Residual LSTM in Surveillance Videos. Sensors. 2021;21(8):2811.
  18. Ghatwary N, Ye X, Zolgharni M. Esophageal Abnormality Detection Using DenseNet Based Faster R-CNN With Gabor Features. IEEE Access. 2019;7:84374–84385.
  19. Islam MZ, Islam MM, Asraf A. A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images. Informatics in Medicine Unlocked. 2020;20:100412.
  20. Mirjalili SZ, Mirjalili SZ, Saremi S, Faris H, Aljarah I. Grasshopper optimization algorithm for multi-objective optimization problems. Applied Intelligence. 2018;48(4):805–820.
  21. Ewees AA, Abd Elaziz M, Houssein EH. Improved grasshopper optimization algorithm using opposition-based learning. Expert Systems with Applications. 2018;112:156–172.


© 2022 Pushpa et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Published By Indian Society for Education and Environment (iSee


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