<i>BlockCrime</i>: Blockchain and Deep Learning-Based Collaborative Intelligence Framework to Detect Malicious Activities for Public Safety
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 m...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/10/17/3195 |
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| author | Dev Patel Harshil Sanghvi Nilesh Kumar Jadav Rajesh Gupta Sudeep Tanwar Bogdan Cristian Florea Dragos Daniel Taralunga Ahmed Altameem Torki Altameem Ravi Sharma |
| author_facet | Dev Patel Harshil Sanghvi Nilesh Kumar Jadav Rajesh Gupta Sudeep Tanwar Bogdan Cristian Florea Dragos Daniel Taralunga Ahmed Altameem Torki Altameem Ravi Sharma |
| author_sort | Dev Patel |
| collection | DOAJ |
| description | 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%. |
| first_indexed | 2024-03-10T01:31:44Z |
| format | Article |
| id | doaj.art-83c625f5b9f248698adcc8a3c9292228 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T01:31:44Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-83c625f5b9f248698adcc8a3c92922282023-11-23T13:40:09ZengMDPI AGMathematics2227-73902022-09-011017319510.3390/math10173195<i>BlockCrime</i>: Blockchain and Deep Learning-Based Collaborative Intelligence Framework to Detect Malicious Activities for Public SafetyDev Patel0Harshil Sanghvi1Nilesh Kumar Jadav2Rajesh Gupta3Sudeep Tanwar4Bogdan Cristian Florea5Dragos Daniel Taralunga6Ahmed Altameem7Torki Altameem8Ravi Sharma9Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, IndiaDepartment of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, IndiaDepartment of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, IndiaDepartment of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, IndiaDepartment of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, IndiaDepartment of Applied Electronics and Information Engineering, Faculty of Electronics, Telecommunications and Information Technology, Politehnica University of Bucharest, 061071 Bucharest, RomaniaDepartment of Applied Electronics and Information Engineering, Faculty of Electronics, Telecommunications and Information Technology, Politehnica University of Bucharest, 061071 Bucharest, RomaniaComputer Science Department, Community College, King Saud University, Riyadh 11437, Saudi ArabiaComputer Science Department, Community College, King Saud University, Riyadh 11437, Saudi ArabiaCentre for Inter-Disciplinary Research and Innovation, University of Petroleum and Energy Studies, P.O. Bidholi Via-Prem Nagar, Dehradun 248001, IndiaDetecting 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%.https://www.mdpi.com/2227-7390/10/17/3195convolutional neural networkdeep learningtransfer learningblockchainsmart contractspublic safety |
| spellingShingle | Dev Patel Harshil Sanghvi Nilesh Kumar Jadav Rajesh Gupta Sudeep Tanwar Bogdan Cristian Florea Dragos Daniel Taralunga Ahmed Altameem Torki Altameem Ravi Sharma <i>BlockCrime</i>: Blockchain and Deep Learning-Based Collaborative Intelligence Framework to Detect Malicious Activities for Public Safety Mathematics convolutional neural network deep learning transfer learning blockchain smart contracts public safety |
| title | <i>BlockCrime</i>: Blockchain and Deep Learning-Based Collaborative Intelligence Framework to Detect Malicious Activities for Public Safety |
| title_full | <i>BlockCrime</i>: Blockchain and Deep Learning-Based Collaborative Intelligence Framework to Detect Malicious Activities for Public Safety |
| title_fullStr | <i>BlockCrime</i>: Blockchain and Deep Learning-Based Collaborative Intelligence Framework to Detect Malicious Activities for Public Safety |
| title_full_unstemmed | <i>BlockCrime</i>: Blockchain and Deep Learning-Based Collaborative Intelligence Framework to Detect Malicious Activities for Public Safety |
| title_short | <i>BlockCrime</i>: Blockchain and Deep Learning-Based Collaborative Intelligence Framework to Detect Malicious Activities for Public Safety |
| title_sort | i blockcrime i blockchain and deep learning based collaborative intelligence framework to detect malicious activities for public safety |
| topic | convolutional neural network deep learning transfer learning blockchain smart contracts public safety |
| url | https://www.mdpi.com/2227-7390/10/17/3195 |
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