Abstract:
The rapid evolution of cyber threats has made it imperative for organizations to develop robust cybersecurity strategies. While traditional defense mechanisms focus on ne...Show MoreMetadata
Abstract:
The rapid evolution of cyber threats has made it imperative for organizations to develop robust cybersecurity strategies. While traditional defense mechanisms focus on network and system-level protection, recent research has highlighted the critical role of understanding user behavior in preventing and mitigating cyberattacks. This paper introduces a novel approach which utilizes advanced analytics techniques to analyze and interpret user actions, patterns, and anomalies to identify potential threats and enhance overall cybersecurity measures. The methodology employed in this research leverages user behavior analysis (UBA) as a proactive defense mechanism against emerging cyber threats. By collecting and analyzing data from various sources, including user interactions, login activities, system logs, and application usage patterns, the proposed approach aims to identify abnormal behaviors that could indicate the presence of malicious actors or compromised user accounts. Furthermore, by incorporating machine learning algorithms and anomaly detection techniques, the system can adapt and learn from evolving attack vectors, increasing its effectiveness over time.
Published in: 2023 15th International Conference on Electronics, Computers and Artificial Intelligence (ECAI)
Date of Conference: 29-30 June 2023
Date Added to IEEE Xplore: 02 August 2023
ISBN Information:
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Behavioral Analysis ,
- User Behavior ,
- User Behavior Analysis ,
- Machine Learning ,
- Learning Algorithms ,
- Active Users ,
- Human-computer Interaction ,
- Anomaly Detection ,
- Usage In Applications ,
- System Logs ,
- Attack Vector ,
- Typical Behavior ,
- Risk Score ,
- Data Storage ,
- Machine Learning Techniques ,
- User Data ,
- Privacy Issues ,
- Sensitive Data ,
- Access Control ,
- User Profile ,
- Access Patterns ,
- User Identification ,
- Behavioral Profiles ,
- Potential Weakness ,
- CPU Utilization ,
- User Engagement ,
- Security Risks ,
- Incident Response ,
- File Transfer ,
- Potential Security
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Behavioral Analysis ,
- User Behavior ,
- User Behavior Analysis ,
- Machine Learning ,
- Learning Algorithms ,
- Active Users ,
- Human-computer Interaction ,
- Anomaly Detection ,
- Usage In Applications ,
- System Logs ,
- Attack Vector ,
- Typical Behavior ,
- Risk Score ,
- Data Storage ,
- Machine Learning Techniques ,
- User Data ,
- Privacy Issues ,
- Sensitive Data ,
- Access Control ,
- User Profile ,
- Access Patterns ,
- User Identification ,
- Behavioral Profiles ,
- Potential Weakness ,
- CPU Utilization ,
- User Engagement ,
- Security Risks ,
- Incident Response ,
- File Transfer ,
- Potential Security
- Author Keywords