Network Intrusion Detection in Cloud Environments: A Comparative Analysis of Approaches

Abstract

This research study comprehensively analyzes network intrusion detection in cloud environments by examining several approaches. These approaches have been explored and compared to determine the optimal and appropriate choice based on specific conditions. This research study employs a qualitative approach, specifically conducting a thematic literature analysis from 2020 to 2024. The research material has been exclusively obtained via Google Scholar. The traditional approaches identified in this research include anomaly-based and signaturebased detection, along with innovative technologies and methods such as user behavior monitoring and machine learning. The findings of these studies demonstrate the effectiveness of conventional methods in known threat detection. They also struggle to identify novel attacks and understand the need for hybrid approaches that integrate the strengths of both. In this research study, the authors have addressed challenges such as privacy compliance, performance scalability, and false positives, highlighting the importance of continuous monitoring, privacypreserving technologies, and real-time threat intelligence integration. This study also highlights the importance of stakeholder buy-in and staff training for the successful implementation of a network intrusion detection system (NIDS), especially when determining the evolving nature of cyber threats. This study concludes by defining a balanced approach combining new and old methodologies to offer an effective defense against diverse cyber threats in cloud environments. The future scope of NIDS in cloud environments has also been discussed, including enhancing privacy compliance capabilities and integrating AIdriven anomaly detection to meet emerging threats and regulatory requirements.