BlockCrime: Blockchain and Deep Learning-Based Collaborative Intelligence Framework to Detect Malicious Activities for Public Safety

Dev Patel, Harshil Sanghvi, Nilesh Kumar Jadav, Rajesh Gupta, Sudeep Tanwar (), Bogdan Cristian Florea (), Dragos Daniel Taralunga, Ahmed Altameem, Torki Altameem and Ravi Sharma
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Dev Patel: Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, India
Harshil Sanghvi: Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, India
Nilesh Kumar Jadav: Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, India
Rajesh Gupta: Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, India
Sudeep Tanwar: Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, India
Bogdan Cristian Florea: Department of Applied Electronics and Information Engineering, Faculty of Electronics, Telecommunications and Information Technology, Politehnica University of Bucharest, 061071 Bucharest, Romania
Dragos Daniel Taralunga: Department of Applied Electronics and Information Engineering, Faculty of Electronics, Telecommunications and Information Technology, Politehnica University of Bucharest, 061071 Bucharest, Romania
Ahmed Altameem: Computer Science Department, Community College, King Saud University, Riyadh 11437, Saudi Arabia
Torki Altameem: Computer Science Department, Community College, King Saud University, Riyadh 11437, Saudi Arabia
Ravi Sharma: Centre for Inter-Disciplinary Research and Innovation, University of Petroleum and Energy Studies, P.O. Bidholi Via-Prem Nagar, Dehradun 248001, India

Mathematics, 2022, vol. 10, issue 17, 1-21

Abstract: Detecting malicious activity in advance has become increasingly important for public safety, economic stability, and national security. However, the disparity in living standards incites the minds of certain undesirable members of society to commit crimes, which may disrupt society’s stability and mental calm. Breakthroughs in deep learning (DL) make it feasible to address such challenges and construct a complete intelligent framework that automatically detects such malicious behaviors. Motivated by this, we propose a convolutional neural network (CNN)-based Xception model, i.e., BlockCrime, to detect crimes and improve public safety. Furthermore, we integrate blockchain technology to securely store the detected crime scene locations and alert the nearest law enforcement authorities. Due to the scarcity of the dataset, transfer learning has been preferred, in which a CNN-based Xception model is used. The redesigned Xception architecture is evaluated against various assessment measures, including accuracy, F1 score, precision, and recall, where it outperforms existing CNN architectures in terms of train accuracy, i.e., 96.57%.

Keywords: convolutional neural network; deep learning; transfer learning; blockchain; smart contracts; public safety (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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