Deep Belief Network-Based User and Entity Behavior Analytics (UEBA) for Web Applications
- Title
- Deep Belief Network-Based User and Entity Behavior Analytics (UEBA) for Web Applications
- Creator
- Deepa S.; Umamageswari A.; Neelakandan S.; Bhukya H.; Sai Lakshmi Haritha I.V.; Shanbhog M.
- Description
- Machine learning (ML) is currently a crucial tool in the field of cyber security. Through the identification of patterns, the mapping of cybercrime in real time, and the execution of in-depth penetration tests, ML is able to counter cyber threats and strengthen security infrastructure. Security in any organization depends on monitoring and analyzing user actions and behaviors. Due to the fact that it frequently avoids security precautions and does not trigger any alerts or flags, it is much more challenging to detect than traditional malicious network activity. ML is an important and rapidly developing anomaly detection field in order to protect user security and privacy, a wide range of applications, including various social media platforms, have incorporated cutting-edge techniques to detect anomalies. A social network is a platform where various social groups can interact, express themselves, and share pertinent content. By spreading propaganda, unwelcome messages, false information, fake news, and rumours, as well as by posting harmful links, this social network also encourages deviant behavior. In this research, we introduce Deep Belief Network (DBN) with Triple DES, a hybrid approach to anomaly detection in unbalanced classification. The results show that the DBN-TDES model can typically detect anomalous user behaviors that other models in anomaly detection cannot. 2024 World Scientific Publishing Company.
- Source
- International Journal of Cooperative Information Systems, Vol-33, No. 2
- Date
- 2024-01-01
- Publisher
- World Scientific
- Subject
- Anomaly detection; deep belief network (dbn); federated identity management; user and entity behavior analytics
- Coverage
- Deepa S., Department of Computer Science and Engineering, Srm Institute of Science and Technology, Ramapuram Campus, Tamil Nadu, Chennai, India; Umamageswari A., Department of Computer Science and Engineering, Srm Institute of Science and Technology, Ramapuram Campus, Tamil Nadu, Chennai, India; Neelakandan S., Department of Computer Science and Engineering, R.M.K Engineering College, Kavaraipettai, Tamil Nadu, Chennai, India; Bhukya H., Department of Computer Science and Engineering (Networks), Kakatiya Institute of Technology and Science, Telangana, Warangal, India; Sai Lakshmi Haritha I.V., Department of Information Technology, Mlr Institute of Technology, Telangana, Hyderabad, India; Shanbhog M., Department of Sciences, Christ (Deemed to Be University), Delhi NCR, Uttar Pradesh, Ghaziabad, 201003, India
- Rights
- Restricted Access
- Relation
- ISSN: 2188430
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Deepa S.; Umamageswari A.; Neelakandan S.; Bhukya H.; Sai Lakshmi Haritha I.V.; Shanbhog M., “Deep Belief Network-Based User and Entity Behavior Analytics (UEBA) for Web Applications,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/13116.