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

【Author】 Patel, Dev; Sanghvi, Harshil; Jadav, Nilesh Kumar; Gupta, Rajesh; Tanwar, Sudeep; Florea, Bogdan Cristian; Taralunga, Dragos Daniel; Altameem, Ahmed; Altameem, Torki; Sharma, Ravi

【Source】MATHEMATICS

【影响因子】2.592

【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

【发表时间】2022 SEP

【收录时间】2022-09-15

【文献类型】理论模型

【主题类别】

区块链应用-实体经济-犯罪领域