An Insight into Machine Learning Techniques to Detect Anomalous Users
- Resource Type
- Conference
- Authors
- Kumar, Pradeep; Kumar, Ajay; Banerjee, Kakoli; Paharia, Ayush; Singh, Arushi; Chaudhary, Anushka
- Source
- 2023 6th International Conference on Information Systems and Computer Networks (ISCON) Information Systems and Computer Networks (ISCON), 2023 6th International Conference on. :1-6 Mar, 2023
- Subject
- Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineering Profession
Fields, Waves and Electromagnetics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Support vector machines
Deep learning
Machine learning algorithms
Social networking (online)
Forestry
Computer networks
Ensemble learning
Cyber security
machine learning
abnormal behaviour detection
user profiling
account classification
RF (Random Forest)
DT (Decision Tree)
anomaly detection
SVM (Support Vector Machine)
- Language
- ISSN
- 2832-143X
Anomalous user profile detection is a challenging problem in machine learning. Fake user accounts can be used for malicious activities and can cause extensive damage. This paper reviews the existing literature on user profile detection, categorizing the methods into supervised and unsupervised approaches. It also discusses the challenges and weaknesses of existing approaches and suggests possible areas for improvement. The paper provides a comprehensive overview and proposes a model to classify profiles as real or fake. Social media has made it easier for attackers to steal information and spread malicious content, which makes it important to detect and stop malicious profiles.