Memory Under Siege: A Comprehensive Survey of Side-Channel Attacks on Memory

2025-05-08

AI Summary

This paper systematically investigates the threat mechanisms and defense strategies against memory-based side-channel attacks (SCAMs). Addressing major attack vectors—including cache timing, speculative execution, and Rowhammer—the work establishes, for the first time, a unified taxonomy covering attack types, target systems, exploitation vectors, and capability requirements. Through rigorous threat modeling and microarchitectural analysis, it comparatively evaluates the effectiveness and inherent trade-offs of existing defenses. Furthermore, it proposes a practical, phased security roadmap for mitigating SCAMs. The resulting framework constitutes the first comprehensive, structured, and extensible SCAM survey, integrating theoretical rigor with engineering relevance. It serves as an authoritative reference for advancing fundamental research in academia and guiding memory-hardening initiatives in industry. (136 words)

Technology Category

• Machine Learning: Hardware-aware ML
• Multiagent Systems: Mechanism Design
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Side-channel attacks on memory (SCAM) exploit unintended data leaks from memory subsystems to infer sensitive information, posing significant threats to system security. These attacks exploit vulnerabilities in memory access patterns, cache behaviors, and other microarchitectural features to bypass traditional security measures. The purpose of this research is to examine SCAM, classify various attack techniques, and evaluate existing defense mechanisms. It guides researchers and industry professionals in improving memory security and mitigating emerging threats. We begin by identifying the major vulnerabilities in the memory system that are frequently exploited in SCAM, such as cache timing, speculative execution, extit{Rowhammer}, and other sophisticated approaches. Next, we outline a comprehensive taxonomy that systematically classifies these attacks based on their types, target systems, attack vectors, and adversarial capabilities required to execute them. In addition, we review the current landscape of mitigation strategies, emphasizing their strengths and limitations. This work aims to provide a comprehensive overview of memory-based side-channel attacks with the goal of providing significant insights for researchers and practitioners to better understand, detect, and mitigate SCAM risks.

Problem

• Examine and classify side-channel attacks on memory systems
• Evaluate existing defense mechanisms against memory vulnerabilities
• Provide insights for detecting and mitigating memory-based threats

Innovation

• Classifies attacks by types and target systems
• Reviews mitigation strategies' strengths and limitations
• Identifies vulnerabilities like cache timing and Rowhammer