Volume 11 , Issue 2 , PP: 27-35, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
Ahmed Hatip 1 * , Karla Zayood 2 , Rabah Scharif 3
Doi: https://doi.org/10.54216/JCIM.110203
Recently, there has been a significant increase in the use of collaborative environments for managing and sharing information. However, these environments often present significant security risks due to the potential for unauthorized access, data leakage, and other security breaches. To address these risks, machine learning (ML) techniques have been increasingly used to secure information management in collaborative environments. We propose a novel ML approach to be applied to detect and prevent security threats in collaborative environments. Our approach integrates temporal convolution to detect and prevent security threats by analyzing spatial-temporal patterns in data from various sources, such as network traffic, system logs, and user behavior. Furthermore, we present a case study demonstrating the effectiveness of our model in securing collaborative information management. The case study involves the development of our system for detecting insider threats in a collaborative environment. Extensive experimentation on this case study demonstrates the efficiency and effectiveness of the proposed system for securing information management and promoting further developments.
Security Risks , Information Management , Collaborative Environments , Network Traffic , Machine Learning
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