Human Intruder Detection System (IDS) for Restricted Security Area: A Systematic Literature Review
Abstract
Ensuring security in sensitive areas such as airports, military bases, and nuclear facilities is critical to prevent unauthorized access. Traditional reliance on security personnel is often inefficient and insufficient for continuous monitoring. Intruder Detection Systems (IDS), which utilize devices or sensors to detect unauthorized entry, have emerged as essential tools for safeguarding high-security environments. However, there is a lack of comprehensive understanding that systematically synthesizes existing research on human intruder detection. This study aims to conduct a systematic literature review (SLR) on human IDS to provide a structured overview of current methodologies, technologies, and challenges in the field. Using established SLR protocols, relevant studies were collected, analyzed, and categorized to identify prevailing trends and gaps. The results highlight various object detection techniques and their effectiveness in real-world security applications. Despite the advances, challenges such as limited environmental adaptability and real-time accuracy remain. The findings of this review offer valuable insights for professionals and future researchers, guiding the development of more robust and efficient human intruder detection solutions.
References
M. Quwaider, “Real-time intruder surveillance using low-cost remote wireless sensors,” 2017 8th International Conference on Information and Communication Systems (ICICS), Irbid, Jordan, 2017, pp. 194-199, https://doi.org/10.1109/IACS.2017.7921970.
Rinaldi. 2022. ITS Material: Indoctrination for the Introduction of Aviation Security Facilities. Jakarta: Indonesian Ministry of Transportation.
Hiroyuki Tsuji, “Development of an intruder detection system using radio waves,” NICT, http://www.nict.go.jp/publication/researcher/pdf
S. Kusumadewi and H. Purnomo, Aplikasi Logika Fuzzy untuk pendukung keputusan, Yogyakarta: Graha Ilmu, 2004
Xie Z, Qin Y. High-speed railway perimeter intrusion detection approach based on Internet of Things. Advances in Mechanical Engineering. 2019;11(2). https://doi.org/10.1177/1687814018821511.
Hoang ML. Smart Drone Surveillance System Based on AI and on IoT Communication in Case of Intrusion and Fire Accident. Drones. 2023; 7(12):694. https://doi.org/10.3390/drones7120694.
Rambabu, Kalathiripi. (2019). IoT Based Human Intrusion Detection System using Lab View. International Journal of Innovative Technology and Exploring Engineering. 8. https://doi.org/10.35940/ijitee.F1115.0486S419.
Roberts, T., & Hall, M. Real-Time Intrusion Detection Systems for High-Security Facilities. Advances in Security Engineering. 2020; 55(1), 98-115
Seung Hyun Kim, Su Chang Lim, Do Yeon Kim, Intelligent intrusion detection system featuring a virtual fence, active intruder detection, classification, tracking, and action recognition, Annals of Nuclear Energy, Volume 112, 2018, Pages 845-855, ISSN 0306-4549, https://doi.org/10.1016/j.anucene.2017.11.026.
P. Shirin Saleem, A. Sushanand, J. A. Renji, M. Aman, D. James and K. S. Lakshmi, “AI-Empowered Intruder Alert System for Enhanced Security Surveillance,” 2023 2nd International Conference on Automation, Computing and Renewable Systems (ICACRS), Pudukkottai, India, 2023, pp. 1-8, https://doi.org/10.1109/ICACRS58579.2023.10404156.
N. Chandra and S. P. Panda, “ A Human Intruder Detection System for Restricted Sensitive Areas,” 2021 2nd International Conference on Range Technology (ICORT), Chandipur, Balasore, India, 2021, pp. 1-4, https://doi.org/10.1109/ICORT52730.2021.9582099.
Prabu and P. Sudhakar, “Design and Implementation of an Automated Control System for Anomaly Detection Using an Enhanced Intrusion Detection System,” 2022 Third International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE), Bengaluru, India, 2022, pp. 1-7, https://doi.org/10.1109/ICSTCEE56972.2022.10100003.
D. Arjun, P. Indukala and K. A. U. Menon, “Integrated Multi-sensor framework for Intruder Detection in Flat Border Area,” 2019 2nd International Conference on Power and Embedded Drive Control (ICPEDC), Chennai, India, 2019, pp. 557-562, https://doi.org/10.1109/ICPEDC47771.2019.9036577.
D. Arjun, P. K. Indukala and K. A. Unnikrishna Menon, “PANCHENDRIYA: A Multi-sensing framework through Wireless Sensor Networks for Advanced Border Surveillance and Human Intruder Detection,” 2019 International Conference on Communication and Electronics Systems (ICCES), Coimbatore, India, 2019, pp. 295-298, https://doi.org/10.1109/ICCES45898.2019.9002161.
Peijun Zhao, Chris Xiaoxuan Lu, Jianan Wang, Changhao Chen, Wei Wang, Niki Trigoni, Andrew Markham, Human tracking and identification through a millimeter wave radar, Ad Hoc Networks, Volume 116, 2021, 102475, ISSN 1570-8705, https://doi.org/10.1016/j.adhoc.2021.102475.
S. Yasukawa and M. Kim, “Intruder Detection Using Radio Wave Propagation Characteristics,” 2018 IEEE International Conference on Consumer Electronics - Asia (ICCE-Asia), JeJu, Korea (South), 2018, pp. 206-212, https://doi.org/10.1109/ICCE-ASIA.2018.8552128.
Raju A Nadafa, S.M. Hatturea, Vasudha M Bonala, Susen P Naikb, Home Security against Human Intrusion using Raspberry Pi, Procedia Computer Science, Volume 167, 2020, Pages 1811-1820, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2020.03.200.
N. Archana, R. Menaka, R. Jothiraj and S. Kalidass, “Smart Home Surveillance System and Intruder Detection Using Local Binary Pattern Histogram,” 2022 International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI), Chennai, India, 2022, pp. 1-5, https://doi.org/10.1109/ICDSAAI55433.2022.10028866.
Z. Tian, Y. Li, M. Zhou and Z. Li, “WiFi-Based Adaptive Indoor Passive Intrusion Detection,” 2018 IEEE 23rd International Conference on Digital Signal Processing (DSP), Shanghai, China, 2018, pp. 1-5, https://doi.org/10.1109/ICDSP.2018.8631613.
Almonfrey D, do Carmo AP, de Queiroz FM, Picoreti R, Vassallo RF, Salles EOT. A flexible human detection service suitable for Intelligent Spaces based on a multi-camera network. International Journal of Distributed Sensor Networks. 2018;14(3). https://doi.org/10.1177/1550147718763550.
C. Okoli and K. Schabram, “A Guide to Conducting a Systematic Literature Review of Information Systems Research,” Communications of the Association for Information Systems, vol. 37, No. 43, pp. 879-910, 2015. https://doi.org/10.2139/ssrn.1954824
Muhasin, Haifaa Jassim et al. A systematic literature review for smart hydroponic system. Bulletin of Electrical Engineering and Informatics, [Sl], v. 13, n. 1, p. 656-664, feb. 2024. https://doi.org/10.11591/eei.v13i1.4738
Khan, A., and Gupta, R, “Artificial Intelligence in Security Systems: Enhancing Intruder Detection,” Journal of Advanced Security Studies, 2021, 45(3), 12-25
Saleem, P. S., et al. “Human Intruder Detection Using CNN Algorithm in Video Surveillance Systems.” Journal of Security Technology, 2022.
Xie, Z., et al. “IoT-Based Perimeter Intrusion Detection System for High-Security Areas.” International Journal of IoT and Smart Technology, 2020.
Hoang, M. L. “Multi-Sensor Decision Fusion Using Hidden Markov Models for Intruder Detection.” Journal of Advanced Sensor Applications, 2021.
Chandra, N., & Panda, S. P. “YOLO Algorithm for Intruder Detection in Security Areas.” Proceedings of Advanced Computing Conference, 2021.
Prabu, S., & Susakar, P. “Deep Learning-Based Object Detection for Restricted Area Security.” International Journal of Computer Vision Applications, 2022.
Arjun, D., et al. “Infrared Sensor-Based Multi-Sensor Framework for Intrusion Detection.” Journal of Advanced Security Systems, 2021.
Zhao, P., et al. “Millimeter-Wave Radar for Intruder Tracking and Identification.” IEEE Transactions on Security Systems, 2021.
I. Setiawati, M. T. Hermanto, and E. Ujianto, “Digital Watermarking Implementation Of Digital Watermarking On Images Using The Least Significant Bit Method”, Int. J. Eng. Technol. Nat. Sci., vol. 5, no. 1, pp. 10 - 18, Jul. 2023. https://doi.org/10.46923/ijets.v5i1.191
M. A. Aprihartha, S. P. Azzahro, R. Azizah, and M. R. Andrianza, “Comparison of Discrete Adaptive Boosting Algorithms for Classification and Regression Tree and Naive Bayes in Pistachio Nut Classification”, Int. J. Eng. Technol. Nat. Sci., vol. 7, no. 1, pp. 28-36, Jul. 2025. https://doi.org/10.46923/ijets.v7i1.396
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