Multi-Cloud Security Event Aggregation and Normalization Using Advanced AI/ML Techniques

Muthuraman Saminathan; Vincent Kanka; Akhil Reddy Bairi

Authors

Muthuraman Saminathan Muthuraman Saminathan, Compunnel Software Group, USA
Vincent Kanka Vincent Kanka, Homesite, USA
Akhil Reddy Bairi Akhil Reddy Bairi, Nelnet Business Solutions, USA

Keywords:

Multi-cloud security, AI/ML techniques, NLP in cybersecurity

Abstract

The proliferation of multi-cloud environments has introduced a multitude of challenges for cybersecurity, particularly in aggregating, normalizing, and deduplicating security event data across diverse platforms. This research explores the utilization of Natural Language Processing (NLP) and advanced machine learning (ML) models to address these challenges, focusing on the implementation of sophisticated techniques in three major cloud ecosystems: AWS Security Hub, Google Chronicle, and Azure Sentinel. The central premise of this study is the development of a unified framework that employs AI-driven methods to standardize heterogeneous security logs, identify redundancies, and enhance the efficacy of threat detection and response mechanisms.

The paper begins with a comprehensive overview of security log generation in multi-cloud environments, highlighting the complexity and heterogeneity of log formats, schemas, and data volumes. The study identifies key obstacles in achieving seamless log aggregation and normalization, including semantic inconsistencies, variations in data syntax, and the presence of redundant or irrelevant entries. By addressing these issues, organizations can significantly enhance their ability to detect, analyze, and respond to security threats in a timely and efficient manner.

To tackle these challenges, the research employs advanced NLP techniques, such as contextual embedding models like BERT and GPT variants, to parse, understand, and standardize log data from different cloud platforms. These models are used to extract meaningful insights and harmonize security event descriptions, ensuring consistency across logs originating from AWS, Google Cloud, and Azure. Additionally, the study integrates ML-based anomaly detection and clustering algorithms to identify and eliminate duplicate events, reducing noise in the data and improving signal-to-noise ratios for security teams.

A core contribution of this paper is the detailed implementation and evaluation of the proposed framework within AWS Security Hub, Google Chronicle, and Azure Sentinel. Each platform is analyzed for its unique logging mechanisms, APIs, and security event schemas. The paper describes the design and deployment of custom connectors and parsers that interface with these platforms, leveraging cloud-native tools and AI/ML models for real-time log processing. Performance metrics, including log normalization accuracy, deduplication rates, and processing latency, are presented to demonstrate the effectiveness of the framework.

Furthermore, this study emphasizes the scalability and adaptability of the proposed system. By employing transfer learning and modular architectures, the framework can be extended to accommodate emerging cloud platforms and evolving log schemas. The implications of this work extend beyond multi-cloud environments, offering valuable insights for enterprise security operations centers (SOCs) that manage diverse and voluminous security data.

The research concludes by addressing limitations and future directions. Key challenges, such as computational overhead, data privacy concerns, and the need for continual model retraining, are discussed alongside potential solutions, including federated learning and edge AI techniques. Additionally, the paper highlights opportunities for integrating this framework with broader cybersecurity paradigms, such as Security Information and Event Management (SIEM) systems and Threat Intelligence Platforms (TIPs).

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