Rademics Research Institute

Abstract

Advanced Persistent Threats (APTs) pose a significant challenge to cloud infrastructures due to their stealthy, multi-stage attack strategies. This chapter explores the role of tensor-based machine learning approaches in identifying APTs by leveraging the multi-dimensional nature of cloud security data. Traditional machine learning models often struggle to analyze large-scale, complex data generated in cloud environments. Tensor-based techniques, such as decomposition and factorization, provide effective methods for extracting hidden patterns, anomalies, and APT indicators across temporal, spatial, and user behavior dimensions. The chapter also addresses critical challenges, including latency, scalability, and real-time implementation of tensor models in dynamic cloud infrastructures. By comparing tensor-based methods with traditional approaches, the advantages in handling high-dimensional data are demonstrated. Finally, optimization strategies and distributed frameworks are discussed to enhance real-time APT detection. This work contributes to advancing cloud security systems through efficient, scalable, and robust tensor-based methodologies.

Introduction

The rapid adoption of cloud infrastructures has revolutionized data storage, computation, and accessibility for businesses and individuals worldwide [1]. However, this widespread adoption has also amplified security concerns, particularly in the face of Advanced Persistent Threats (APTs) [2]. APTs are sophisticated, stealthy, and prolonged cyberattacks that target cloud systems to compromise sensitive data or disrupt services [3]. Unlike traditional cyber threats, APTs are multi-dimensional and evolve over time, making them exceptionally difficult to detect and mitigate [4]. Cloud environments generate massive volumes of multi-dimensional data, including network traffic, user behaviors, logs, and temporal patterns [5,6]. Therefore, effective APT detection requires advanced computational approaches capable of analyzing and extracting actionable insights from this complex data [7-9].

Traditional machine learning models have been widely applied to anomaly detection and threat identification [10]. However, these methods often face limitations when handling high-dimensional and dynamic cloud data [11]. Most conventional approaches rely on simplified, two-dimensional representations, which fail to capture the intricate relationships and latent patterns in multi-dimensional data [12]. The inability to model temporal, spatial, and user-behavioral correlations within cloud infrastructures diminishes their efficacy in identifying subtle APT indicators [13]. This necessitates innovative techniques that can effectively process and analyze multi-dimensional datasets to detect evolving threats with higher precision [14-16].