International Journal of Computer Networks and Applications

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An Automated Intrusion Detection and Prevention Model for Enhanced Network Security and Threat Assessment


Affiliations
1 School of Computing Science and Engineering, Galgotias University, Uttar Pradesh, India
 

Amid the soaring cyber threats and security breaches, we introduce an automated intrusion detection and prevention model to bolster threat assessment and security data solutions. Our model, utilizing the state-of-the-art Automatic Intrusion Detection System (AIDS) and real-time data analysis, promptly identifies and responds to potential security breaches. It gathers security data from multiple sources, such as network traffic, system logs, user behaviour, and external threat intelligence feeds, enhancing overall cybersecurity defenses. The increasing volume of data sharing and network traffic has raised concerns about cybersecurity. To address this issue, we propose the Automatic Intrusion Detection System (AiDS) is defined as monitoring the network for suspicious activity for managing network traffic. The activities detected are monitored based on the alerts, and the operation centres are analyzed using the appropriate actions to remediate the threat. The Automatic intrusion Detection System and the Intrusion Prevention System (IPS) have been used to prevent and secure network data. By using the technique of Automatic intrusion Detection System (AiDS), the identification of the endpoint protection, which is related to the hunting engine, risk management, incident response mobile security, and access management and by using the technique of Intrusion Prevention System (AiPS) the vulnerability of threat management and the analysis of the data in the network is proposed. The result describes the 97.2% of data in the KDD 99 data set, the accuracy and sensitivity of the data from the network is 92.8%, and the system's formation. The approximate data in the database is 75%. The security services' intrusion and the system's data formation in the digital threat data have been accessed successfully.

Keywords

Automated Intrusion Detection, Network Security, Risk Management, Endpoint Protection, Incident Response, Intrusion Prevention System, SOC-As-A Services, Vulnerability Threat Management, Mobile Security.
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  • An Automated Intrusion Detection and Prevention Model for Enhanced Network Security and Threat Assessment

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Authors

K Prabu
School of Computing Science and Engineering, Galgotias University, Uttar Pradesh, India
P Sudhakar
School of Computing Science and Engineering, Galgotias University, Uttar Pradesh, India

Abstract


Amid the soaring cyber threats and security breaches, we introduce an automated intrusion detection and prevention model to bolster threat assessment and security data solutions. Our model, utilizing the state-of-the-art Automatic Intrusion Detection System (AIDS) and real-time data analysis, promptly identifies and responds to potential security breaches. It gathers security data from multiple sources, such as network traffic, system logs, user behaviour, and external threat intelligence feeds, enhancing overall cybersecurity defenses. The increasing volume of data sharing and network traffic has raised concerns about cybersecurity. To address this issue, we propose the Automatic Intrusion Detection System (AiDS) is defined as monitoring the network for suspicious activity for managing network traffic. The activities detected are monitored based on the alerts, and the operation centres are analyzed using the appropriate actions to remediate the threat. The Automatic intrusion Detection System and the Intrusion Prevention System (IPS) have been used to prevent and secure network data. By using the technique of Automatic intrusion Detection System (AiDS), the identification of the endpoint protection, which is related to the hunting engine, risk management, incident response mobile security, and access management and by using the technique of Intrusion Prevention System (AiPS) the vulnerability of threat management and the analysis of the data in the network is proposed. The result describes the 97.2% of data in the KDD 99 data set, the accuracy and sensitivity of the data from the network is 92.8%, and the system's formation. The approximate data in the database is 75%. The security services' intrusion and the system's data formation in the digital threat data have been accessed successfully.

Keywords


Automated Intrusion Detection, Network Security, Risk Management, Endpoint Protection, Incident Response, Intrusion Prevention System, SOC-As-A Services, Vulnerability Threat Management, Mobile Security.

References





DOI: https://doi.org/10.22247/ijcna%2F2023%2F223316