Advanced Network Anomaly Detection in Cisco Secure Workload Environments using AI and Machine Learning | [International Journal of Communication Networks and Information Security • 2024]

Author(s):

1. M. Ramana Kumar: Dept. of CSE, Kommuri Pratap Reddy Institute of Technology,Hyderabad, Telangana, Pakistan

Abstract:

With the rapid evolution of modern computing environments, network security has become a paramount concern to protect data and prevent cyberattacks. Cisco Secure Workload, as part of Cisco's comprehensive security ecosystem, plays a crucial role in safeguarding data center and cloud networks. However, detecting anomalies in network behavior, which may signal security breaches or vulnerabilities, presents a significant challenge. Traditional network anomaly detection methods, including rulebased and signature-based techniques, often struggle to adapt to the dynamic nature and complexity of modern network traffic. This study explores the need for advanced anomaly detection approaches in Cisco Secure Workload environments to address emerging cyber threats and the sophisticated tactics used by attackers. By leveraging cutting-edge data analytics, machine learning, and artificial intelligence, the project aims to develop adaptive and accurate methods for identifying network anomalies in real-time. The proposed approach utilizes machine learning models to analyze traffic patterns, detect unwanted access attempts, and identify deviations in network behavior, ensuring rapid response to potential security issues. This research contributes to enhancing network security in Cisco Secure Workload environments, providing a more robust defense against evolving cyber threats.

Page(s): 205-211

DOI: DOI not available

Published: Journal: International Journal of Communication Networks and Information Security, Volume: 16, Issue: 2, Year: 2024

Keywords:

Supervised Learning , Decision Trees , Network anomalies , Secure workload , Computing Hosts , Ensemble modelling

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