Suspicious Activity Detection Using Artificial Intelligence |
Author(s): |
| Sushank Pawar , Sinhgad Institute of Technology And Science; Atharva Borse, Sinhgad Institute of Technology And Science; Gaurav Pandit, Sinhgad Institute of Technology And Science; Vaibhav Pokharkar, Sinhgad Institute of Technology And Science; Mr.N.V.Kamble, Sinhgad Institute of Technology And Science |
Keywords: |
| SDA-Suspicious Activity Detection, CNN-Convolutional Neural Network, DL-Deep Learning |
Abstract |
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The process of locating and notifying suspected malicious or anomalous behaviors within a specific system or environment is known as suspicious activity detection. This work is essential in a number of industries, including social media, finance, healthcare, and security. The examination of multiple data sources, including user behavior logs, network traffic, and sensor readings, using machine learning and data analytics techniques is the foundation for the detection of suspicious activities. The ultimate objectives are identifying and reducing security threats, stopping fraud, and enhancing system performance. An overview of the area of suspicious activity detection, its difficulties, and its applicability in numerous fields is given in this abstract. It emphasizes the significance of creating precise and effective algorithms to deal with the difficulties of spotting suspicious actions. |
Other Details |
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Paper ID: IJSRDV11I30295 Published in: Volume : 11, Issue : 3 Publication Date: 01/06/2023 Page(s): 393-395 |
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