Hybrid Deep Learning Model for Multiple Cache Side Channel Attacks Detection: A Comparative Analysis

TL;DR

This paper proposes a hybrid deep learning model to detect cache side channel attacks, achieving high accuracy and outperforming existing models, thereby enhancing security against sophisticated data extraction threats.

Contribution

The study introduces a novel hybrid deep learning approach for detecting cache side channel attacks and compares its performance with five established models, demonstrating superior detection capabilities.

Findings

Hybrid model achieves up to 99.96% detection rate.

Existing models have limitations in detecting sophisticated attacks.

The research provides insights for developing more effective security defenses.

Abstract

Cache side channel attacks are a sophisticated and persistent threat that exploit vulnerabilities in modern processors to extract sensitive information. These attacks leverage weaknesses in shared computational resources, particularly the last level cache, to infer patterns in data access and execution flows, often bypassing traditional security defenses. Such attacks are especially dangerous as they can be executed remotely without requiring physical access to the victim's device. This study focuses on a specific class of these threats: fingerprinting attacks, where an adversary monitors and analyzes the behavior of co-located processes via cache side channels. This can potentially reveal confidential information, such as encryption keys or user activity patterns. A comprehensive threat model illustrates how attackers sharing computational resources with target systems exploit these side channels to compromise sensitive data. To mitigate such risks, a hybrid deep learning model is proposed for detecting cache side channel attacks. Its performance is compared with five widely used deep learning models: Multi-Layer Perceptron, Convolutional Neural Network, Simple Recurrent Neural Network, Long Short-Term Memory, and Gated Recurrent Unit. The experimental results demonstrate that the hybrid model achieves a detection rate of up to 99.96%. These findings highlight the limitations of existing models, the need for enhanced defensive mechanisms, and directions for future research to secure sensitive data against evolving side channel threats.