Autotheft is a crime that can be mitigated using artificial intelligence as a scientific approach. In this case, we assess the drivers driving pattern using both deep neural network and swarm intelligence algorithms. From the analysis we are able to obtain the driving signature of the driver which can be associated with the vehicle. The vehicle is then tracked and monitored. Next, a deviation from the usual driving signature of the owner or assigned driver would signify a possible instance of autotheft. Subsequently, the vehicle can be traced and reclaimed by the owner. The algorithms are evaluated based on their performance in analysing the datasets bearing variable features. The variations in features enable us to verify the efficacy and accuracy levels of the various algorithms that are used in the study. The metrics used for evaluation are the Mean Squared Error and the F1 Score for precision, accuracy and recall functionality.

[1] Aishwarya K. and Manjesh R., “A Novel Technique for Vehicle Theft Detection System Using MQTT on IoT,” in proceeding of International Conference on Communication, Computing and Electronics Systems, Singapore, pp. 725-733, 2020.

[2] Alhussein M., Aurangzeb K., and Haider S., “Vehicle License Plate Detection and Perspective Rectification,” Elektronika Ir Elektrotechnika, vol. 25 no. 5, pp. 47-56, 2019.

[3] Alsrehin N., Klaib A., and Magableh A., “Intelligent Transportation and Control Systems Using Data Mining and Machine Learning Techniques: A Comprehensive Study,” IEEE Access, vol. 7, pp. 49830-49857, 2019.

[4] Bangyal W., Ahmad J., Rauf H., and Pervaiz S., “An Improved Bat Algorithm Based on Novel Initialization Technique for Global Optimization Problem,” International Journal of Advanced Computer Science and Applications, vol. 9 no. 7, pp. 158-166, 2018.

[5] Bernardi M., Cimitile M., Martinelli F., and Mercaldo F., “Driver and Path Detection Through Time-Series Classification,” Journal of Advanced Transportation, pp. 1-20, 2018.

[6] Bhalerao J., Kadam A., Shinde A., Mugalikar V., and Bhan H., “Proposed Design on Driver Behavioral Analysis,” International Journal of Engineering Research and Technology, vol. 9, no. 5, pp. 554-557, 2020.

[7] Bhuyan H. and Pani S., Intelligent Data Analytics for Terror Threat Prediction: Architectures, Methodologies, Techniques and Applications, Wiley Online Library, 2021.

[8] Bosire A. and Maingi D., “Using Deep Analysis of Driver Behavior for Vehicle Theft Detection and Recovery,” in proceedind of International Arab Conference on Information Technology, Oman, pp. 1-6, 2021.

[9] Chalapathy R. and Chawla S., “Deep Learning for Anomaly Detection: A Survey,” arXiv:1901.03407v2

[cs.LG], 2019.

[10] Chicco D., Sadowski P., and Baldi P., “Deep Autoencoder Neural Networks for Gene Ontology Annotation Predictions,” in proceeding of The 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, New York, pp. 533-540, 2014.

[11] Dong Z., Shi W., Tong G., and Yang K., “Collaborative Autonomous Driving: Vision and Challenges,” in proceeding of International Conference on Connected and Autonomous Driving, USA, pp. 17-26, 2020.

[12] Du K. and Swamy M., Neural Networks and Statistical Learning, Springer, 2019.

[13] Elngar A. and Kayed M., “Vehicle Security Systems Using Face Recognition Based on Internet of Things,” Open Computer Science, vol. 10, no. 1, pp. 17-29, 2020.

[14] Feng M., Zhen J., Ren J., Hussein A., Li X., Xi Y., and Liu, Q., “Big Data Analytics and Mining for Effective Visualization and Trends Forecasting of Crime Data,” IEEE Access, vol. 7, pp. 106111- 106123, 2019.

[15] Gao D., Li X., and Chen H., “Application of Improved Particle Swarm Optimization,” Mathematical Problems in Engineering, vol. 2019, pp. 1-10, 2019.

[16] Girma A., Yan X., and Homaifar A., “Driver Identification Based on Vehicle Telematics Data using LSTM-Recurrent Neural Network,” in proceedind of the IEEE 31st International Conference on Tools with Artificial Intelligence, USA, pp. 894-902, 2019. 420 The International Arab Journal of Information Technology, Vol. 19, No. 3A, Special Issue 2022

[17] Hakli H. and Kiran M., “An improved artificial bee colony algorithm for balancing local and global search behaviors in continuous optimization,” International Journal of Machine Learning and Cybernetics, vol. 11, n. 9, pp. 2051-2076, 2020.

[18] Jiang J., Zhang J., Zhang L., Ran X., Jiang J., and Wu Y., “DBN Structure Design Algorithm for Different Datasets Based on Information Entropy and Reconstruction Error,” Entropy, vol. 20, no. 12, pp. 1-18, 2018.

[19] Kang Y., Park K., and Kim H., “Automobile Theft Detection by Clustering Owner Driver Data,” in Procceding of the "17th Escar Europe : Embedded Security in Cars, Ruhr-Universität Bochum, pp. 185-199, 2019.

[20] Khan A., Sohail A., Zahoora U., and Qureshi A., “A Survey of the Recent Architectures of Deep Convolutional Neural Networks,” Artificial Intelligence Review, vol. 53, no. 8, pp. 5455-5516, 2020.

[21] Kommaraju R., Kommanduri R., Lingeswararao S., Sravanthi B., and Srivalli C., “IoT Based Vehicle (Car) Theft Detection,” in proceeding of International Conference on Image Processing and Capsule Networks, pp. 620-628, 2020.

[22] Koutsoukas A., Monaghan K., Li X., and Huan J., “Deep-learning: Investigating Deep Neural Networks Hyper-Parameters and Comparison of Performance to Shallow Methods for Modeling Bioactivity Data,” Journal of Cheminformatics, vol. 9 no. 42, pp. 1-13, 2017.

[23] Kumar V. and Veerala, K., “Aggressive driving dataset,” Retrieved from https://www.kaggle.com/datasets/veeralakrishna/ aggressive-driving-data, 2022.

[24] Li J., Cheng H., Guo H., and Qiu S., “Survey on Artificial Intelligence for Vehicles,” Automotive Innovation, vol. 1, no. 1, pp. 2-14, 2018.

[25] Martinelli F., Mercaldo F., Orlando A., Nardone V., Santone A., and Sangaiah A., “Human Behavior Characterization for Driving Style Recognition in Vehicle System,” Computers and Electrical Engineering, vol. 83, pp.102504, 2020.

[26] Mavrovouniotis M., Muller F., and Yang S., “Ant Colony Optimization With Local Search for Dynamic Traveling Salesman Problems,” IEEE Transactions on Cybernetics, vol. 47, no. 7, pp. 1743-1756, 2016.

[27] Mirjalili S. and Lewis A., “The Whale Optimization Algorithm.” Advances in Engineering Software, vol. 95, pp. 51-67, 2016.

[28] Mohammed H., Umar S., and Rashid T., “A Systematic and Meta-Analysis Survey of Whale Optimization Algorithm,” Computational Intelligence and Neuroscience, vol. 2019, pp. 1- 27, 2019.

[29] Ramesh M., Akruthi S., Nandhini K., Meena S., Gladwin S., and Rajavel R, “Implementation of Vehicle Security System using GPS,GSM and Biometric,” in proceeding of Women Institute of Technology Conference on Electrical and Computer Engineering, India, pp. 71-75, 2019.

[30] Rettore P., “Vehicular Trace Data, ” Retrieved from http://www.rettore.com.br/prof/vehicular- trace, 2022.

[31] Rizk Y., Hajj N., Mitri N., and Awad M., “Deep Belief Networks and Cortical Algorithms: A Comparative Study for Supervised Classification” Applied Computing and Informatics, vol. 15, no. 2, 81-93, 2019.

[32] Rodg J. and Jaiswal S., “Comprehensive Overview of Neural Networks and its Applications in Autonomous Vehicles,” Computational Intelligence in the Internet of Things, pp. 159-173, 2019.

[33] Sherstinsky A., “Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network,” Physica D: Nonlinear Phenomena, vol. 404, pp. 1-43, 2020.

[34] Shrestha A. and Mahmood A., “Review of Deep Learning Algorithms and Architectures,” IEEE Access, vol. 7, pp. 53040-53065, 2019.

[35] Singh S. and Kumar, P., “Automatic Car Theft Detection System Based on GPS and GSM Technology,” International Journal of Trend in Scientific Research and Development, vol. 3, n. 4, pp. 689-692, 2019.

[36] Villa M., Gofman M., and Mitra S., “Survey of Biometric Techniques for Automotive Applications,” Information Technology - New Generations, vol. 738, pp. 475-481, 2018.

[37] Wang W., Xi J., and Zhao D., “Driving Style Analysis Using Primitive Driving Patterns With Bayesian Nonparametric Approaches,” IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 8, pp. 2986-2998, 2018.

[38] Yüksel A. and Atmaca S., “Driving Behavior Dataset,” Retrieved from https://doi.org/10.17632/jj3tw8kj6h.3, 2022.

[39] Zhang J., Wu Z., Li F., Xie C., Ren,T., Chen J., and Liu L, “A Deep Learning Framework for Driving Behavior Identification on In-Vehicle CAN-BUS Sensor Data,” Sensors, vol. 19, no. 6, pp. 1-17, 2019.

[40] Zinebi K., Souissi N., and Tikito K., “Driver Behavior Analysis Methods: Applications oriented study,” in proceeding of The 3rd International Conference on Big Data, Cloud and Applications - BDCA'18, Morocco, 2018.