Journal of Multidisciplinary Applied Natural Science
an Open Access Journal
Scopus CiteScore 2025
2.1
Calculated on 05 May, 2025
SJR 2025
0.25
Powered by scimagojr.com
an Open Access Journal
2.1
Calculated on 05 May, 2025
0.25
Powered by scimagojr.com
https://doi.org/10.47352/jmans.2774-3047.455
Vidyarani Hamppayanamalige Jayaprakash
Girija Sanjeevaiah
Mohankumar Venugopal
Ganesh Hamppatanamalige Jayaprakash
Nishchitha Malligere Harendra Kumar
The rapid emergence of digital technologies and the increased of using intelligent devices have led to a significant increase in crime incidents. Therefore, there is an emerging need to develop highly accurate and efficient detection frameworks. Hence, this work introduces an intelligent drone-assisted crime-detection system based on a novel Puma Multilayer Perceptron Detection Framework. The images are collected from a Kaggle dataset. The image dataset is pre-processed in a Python environment, with image quality improved by removing noise and normalizing the images. Further, discriminative image features are selected by the Puma Optimization Algorithm. It efficiently selects the optimal features by balancing between detection accuracy and dimensionality reduction. The optimized feature set is then classified using a multilayer perceptron. All these processes aim to accurately detect criminal activities. The efficiency of the proposed model is evaluated using standard metrics, including accuracy, precision, recall, F-score, and error rate, and the results are compared with those of traditional detection approaches. The experimental results show that the proposed framework improves detection accuracy and reliability, making it effective for intelligent drone-based crime monitoring.
[1] S. C. Hassler, U. A. Mughal, and M. Ismail. (2023). "Cyber-physical intrusion detection system for unmanned aerial vehicles". IEEE Transactions on Intelligent Transportation Systems. 25 (6): 6106-6117. 10.1109/TITS.2023.3339728
DOI: https://doi.org/10.1109/TITS.2023.3339728[2] N. Alturki, T. Aljrees, M. Umer, A. Ishaq, S. Alsubai, O. Saidani, S. Djuraev, and I. Ashraf. (2023). "An intelligent framework for cyber-physical satellite system and iot-aided aerial vehicle security threat detection". Sensors. 23 (16): 7154. 10.3390/s23167154
DOI: https://doi.org/10.3390/s23167154[3] A. Vajravelu, N. Ashok Kumar, S. Sarkar, and S. Degadwala. (2023). "Security threats of unmanned aerial vehicles". Wireless Networks: Cyber Security Threats and Countermeasures. Cham: Springer International Publishing. 133-164. 10.1007/978-3-031-33631-7_5
DOI: https://doi.org/10.1007/978-3-031-33631-7_5[4] S. A. H. Mohsan, N. Q. H. Othman, Y. Li, M. H. Alsharif, and M. A. Khan. (2023). "Unmanned aerial vehicles (UAVs): Practical aspects, applications, open challenges, security issues, and future trends". Intelligent Service Robotics. 16 (1): 109-137. 10.1007/s11370-022-00452-4
DOI: https://doi.org/10.1007/s11370-022-00452-4[5] E. Torba, and H. Jahankhani. (2025). "Securing systems from aerial threats: cybersecurity in the drone era". Autonomous Revolution: Strategies, Threats and Challenges. Cham: Springer Nature Switzerland. 261-306. 10.1007/978-3-031-96782-5_9
DOI: https://doi.org/10.1007/978-3-031-96782-5_9[6] N. d'Ambrosio, , G. Perrone, S. P. Romano, and A. Urraro. (2025). "A cyber-resilient open architecture for drone control". Computers & Security. 150 : 104205. 10.1016/j.cose.2024.104205
DOI: https://doi.org/10.1016/j.cose.2024.104205[7] I. Anagnostis, P. Kotzanikolaou, and C. Douligeris. (2025). "Understanding and securing the risks of unmanned aerial vehicle services". IEEE Access. 13 : 47955-47995. 10.1109/ACCESS.2025.3549861
DOI: https://doi.org/10.1109/ACCESS.2025.3549861[8] S. N. Ashraf, , S. Manickam, S. S. Zia, A. A. Abro, M. Obaidat, M. Uddin, M. Abdelhaq, and R. Alsaqour. (2023). "IoT empowered smart cybersecurity framework for intrusion detection in internet of drones". Scientific Reports. 13 (1): 18422. 10.1038/s41598-023-45065-8
DOI: https://doi.org/10.1038/s41598-023-45065-8[9] A. Alzahrani. (2024). "Novel approach for intrusion detection attacks on small drones using ConvLSTM model". IEEE Access. 12 : 149238-149253. 10.1109/ACCESS.2024.3471806
DOI: https://doi.org/10.1109/ACCESS.2024.3471806[10] M. S. Bhargavi, P. Hemavathi, C. Jaganath, and K. Narendra. (2025). "Unlocking the Potential of Machine Learning for Enhanced Security in Drone-Enabled IoT Networks". Machine Learning for Drone-Enabled IoT Networks: Opportunities, Developments, and Trends. Cham: Springer Nature Switzerland. 107-120. 10.1007/978-3-031-80961-3_6
DOI: https://doi.org/10.1007/978-3-031-80961-3_6[11] M. Nankya, R. Chataut, and R. Akl. (2023). "Securing industrial control systems: Components, cyber threats, and machine learning-driven defense strategies". Sensors. 23 (21): 8840. 10.3390/s23218840
DOI: https://doi.org/10.3390/s23218840[12] R. Morshedi, and S. Mojtaba Matinkhah. (2025). "Cybersecurity Challenges and Solutions in Unmanned Aerial Vehicles (UAVs)". Journal of Field Robotics. 43 (1): 314-329. 10.1002/rob.70040
DOI: https://doi.org/10.1002/rob.70040[13] D. A. H. Ollachica, B. K. A. Asante, and H. Imamura. (2024). "Autonomous UAV implementation for facial recognition and tracking in GPS-denied environments". IEEE Access. 12 : 119464-119487. 10.1109/ACCESS.2024.3447899
DOI: https://doi.org/10.1109/ACCESS.2024.3447899[14] D. Alsadie. (2025). "Cybersecurity and Artificial Intelligence in Unmanned Aerial Vehicles: Emerging Challenges and Advanced Countermeasures". IET Information Security. 2025 (1): 2046868. 10.1049/ise2/2046868
DOI: https://doi.org/10.1049/ise2/2046868[15] R. A. AL-Syouf, R. M. Bani-Hani, and O. Y. AL-Jarrah. (2024). "Machine learning approaches to intrusion detection in unmanned aerial vehicles (UAVs)". Neural Computing and Applications. 36 (29): 18009-18041. 10.1007/s00521-024-10306-y
DOI: https://doi.org/10.1007/s00521-024-10306-y[16] H. J. Hadi, Y. Cao, M. K. Khan, N. Ahmad, Y. Hu, and C. Fu. (2025). "UAV-NIDD: A Dynamic Dataset for Cybersecurity and Intrusion Detection in UAV Networks". IEEE Transactions on Network Science and Engineering. 12 (4): 2739-2757. 10.1109/TNSE.2025.3553442
DOI: https://doi.org/10.1109/TNSE.2025.3553442[17] E. Ntizikira, W. Lei, F. Alblehai, K. Saleem, and M. A. Lodhi. (2023). "Secure and privacy-preserving intrusion detection and prevention in the internet of unmanned aerial vehicles". Sensors. 23 (19): 8077. 10.3390/s23198077
DOI: https://doi.org/10.3390/s23198077[18] F. Tlili, S. Ayed, and L. C. Fourati. (2024). "Exhaustive distributed intrusion detection system for UAVs attacks detection and security enforcement (E-DIDS)". Computers & Security. 142 : 103878. 10.1016/j.cose.2024.103878
DOI: https://doi.org/10.1016/j.cose.2024.103878[19] R. Fu, X. Ren, Y. Li, Y. Wu, H. Sun, and M. A. Al-Absi. (2023). "Machine-learning-based UAV-assisted agricultural information security architecture and intrusion detection". IEEE Internet of Things Journal. 10 (21): 18589-18598. 10.1109/JIOT.2023.3236322
DOI: https://doi.org/10.1109/JIOT.2023.3236322[20] Y. Xu, Y. Liu, H. Li, L. Wang, and J. Ai. (2024). "A deep learning approach of intrusion detection and tracking with UAV-based 360 camera and 3-axis gimbal". Drones. 8 (2): 68.
DOI: https://doi.org/10.3390/drones8020068[21] T. Wisanwanichthan, and M. Thammawichai. (2025). "A lightweight intrusion detection system for iot and UAV using deep neural networks with knowledge distillation". Computers. 14 (7): 291. 10.3390/computers14070291
DOI: https://doi.org/10.3390/computers14070291[22] A. S. Nair, and S. M. Thampi. (2025). "SoCoMNNet: A SocioCognitive and Memristive Neural Network-Based Context-Aware GPS Spoofing Detection and Mitigation in the Internet of Drones". Vehicular Communications. 56 : 100980. 10.1016/j.vehcom.2025.100980
DOI: https://doi.org/10.1016/j.vehcom.2025.100980[23] D. S. Prasad, P. Jyothi, G. Suryanarayana, and S. N. Mohanty. (2023). "Algorithms to Mitigate Cyber Security Threats by Employing Intelligent Machine Learning Models in the Design of IoT‐Aided Drone". Drone Technology: Future Trends and Practical Applications. 257-300. 10.1002/9781394168002.ch11
DOI: https://doi.org/10.1002/9781394168002.ch11[24] L. R. Vuyyuru, N. M. R. Purimetla, K. Y. Reddy, S. S. Vellela, S. K. Basha, and R. Vatambeti. (2025). "Advancing automated street crime detection: a drone-based system integrating CNN models and enhanced feature selection techniques". International Journal of Machine Learning and Cybernetics. 16 (2): 959-981. 10.1007/s13042-024-02315-z
DOI: https://doi.org/10.1007/s13042-024-02315-z[25] F. S. Alsubaei, A. A. Almazroi, and N. Ayub. (2024). "Enhancing phishing detection: A novel hybrid deep learning framework for cybercrime forensics". IEEE Access. 12 : 8373-8389. 10.1109/ACCESS.2024.3351946
DOI: https://doi.org/10.1109/ACCESS.2024.3351946[26] M. M. Mukto, M. Hasan, M. M. Al Mahmud, I. Haque, A. Ahmed, T. Jabid, S. Ali, M. R. A. Rashid, M. M. Islam, and M. Islam. (2024). "Design of a real-time crime monitoring system using deep learning techniques". Intelligent Systems with Applications. 21 : 200311. 10.1016/j.iswa.2023.200311
DOI: https://doi.org/10.1016/j.iswa.2023.200311[27] M. Y. Alzahrani. (2024). "Enhancing drone security through multi-sensor anomaly detection and machine learning". SN Computer Science. 5 (5): 651. 10.1007/s42979-024-02983-2
DOI: https://doi.org/10.1007/s42979-024-02983-2[28] A. Alsumayt, N. Nagy, S. Alsharyofi, N. Al Ibrahim, R. Al-Rabie, R. Alahmadi, R. A. Alesse, A. A. Alahmadi. (2024). "Detecting Denial of Service Attacks (DoS) over the Internet of Drones (IoD) Based on Machine Learning". Sci. 6 (3): 56. 10.3390/sci6030056
DOI: https://doi.org/10.3390/sci6030056[29] B. Abdollahzadeh, N. Khodadadi, S. Barshandeh, P. Trojovský, F. S. Gharehchopogh, E. S. M. El-kenawy, L. Abualigah, and S. Mirjalili. (2024). "Puma optimizer (PO): a novel metaheuristic optimization algorithm and its application in machine learning". Cluster Computing. 27 (4): 5235-83. 10.1007/s10586-023-04221-5
DOI: https://doi.org/10.1007/s10586-023-04221-5[30] L. R. Vuyyuru, N. M. R. Purimetla, K. Y. Reddy, S. S. Vellela, S. K. Basha, and R. Vatambeti. (2025). "Advancing automated street crime detection: a drone-based system integrating CNN models and enhanced feature selection techniques". International Journal of Machine Learning and Cybernetics. 16 (2): 959-81. 10.1007/s13042-024-02315-z
DOI: https://doi.org/10.1007/s13042-024-02315-z[31] M. Qasim, and E. Verdu. (2023). "Video anomaly detection system using deep convolutional and recurrent models". Results in Engineering. 18 : 101026. 10.1016/j.rineng.2023.101026
DOI: https://doi.org/10.1016/j.rineng.2023.101026[32] E. Aslan, Y. Ozupak, F. Alpsalaz, and Z. M. S. Elbarbary. (2025). "A hybrid machine learning approach for predicting power transformer failures using internet of things based monitoring and explainable artificial intelligence". IEEE Access. 13 : 113618-113633. 10.1109/ACCESS.2025.3583773
DOI: https://doi.org/10.1109/ACCESS.2025.3583773[33] F. Alpsalaz, Y. Özüpak, E. Aslan, and H. Uzel. (2025). "Classification of maize leaf diseases with deep learning: Performance evaluation of the proposed model and use of explicable artificial intelligence". Chemometrics and Intelligent Laboratory Systems. 262 : 105412. 10.1016/j.chemolab.2025.105412
DOI: https://doi.org/10.1016/j.chemolab.2025.105412[34] Y. Oezuepak, and E. Aslan. (2024). "Using artificial neural networks to improve the efficiency of transformers used in wireless power transmission systems for different coil positions". Revue Roumaine des Sciences Techniques-Série Électrotechnique et Énergétique. 69 (2): 195-200. 10.59277/RRST-EE.2024.2.13
DOI: https://doi.org/10.59277/RRST-EE.2024.2.13[35] F. Alpsalaz, Y. Özüpak, E. Aslan, and H. Uzel. (2026). "Hybrid Machine Learning Approach for Enhanced Fault Detection and Power Estimation in Photovoltaic Systems". IET Renewable Power Generation. 20 (1): e70153. 10.1049/rpg2.70153
DOI: https://doi.org/10.1049/rpg2.70153[36] E. Aslan, and Y. Özüpak. (2025). "Comparison of machine learning algorithms for automatic prediction of Alzheimer disease". Journal of the Chinese Medical Association. 88 (2): 98-107. 10.1097/JCMA.0000000000001188
DOI: https://doi.org/10.1097/JCMA.0000000000001188[37] N. Gelirli, and H. Tangül. (2025). "A Study on Analyzing the Level of Public Compliance with Drone Laws in Northern Cyprus". Journal of Aviation. 9 (1): 53-63. 10.30518/jav.1543124
DOI: https://doi.org/10.30518/jav.1543124