Efficient Malware Detection for Embedded Computing Systems with Limited Exposure

核心概念

A code-aware data generation technique is introduced to efficiently detect emerging malware in embedded systems, even with limited exposure to malware samples.

要約

The paper addresses the challenge of detecting malware in embedded computing systems, where there is limited exposure to malware samples. The key highlights are:

1. The authors introduce a code-aware data generation technique that generates mutated samples of the limitedly seen malware. This helps mitigate the need for a large training dataset.

2. Loss minimization is employed to ensure the generated samples closely mimic the features and functionality of the limited malware data.

3. Few-shot learning is used to efficiently classify complex stealthy malware and code obfuscated malware, even with limited training samples.

The proposed approach can achieve up to 89.52% accuracy in detecting complex malware, which is 7% higher compared to models trained only on limited samples. The authors also provide ASIC implementation results for different classifier models, demonstrating the efficiency of the proposed technique.

統計

The paper reports that there have been more than 5 billion malware attacks worldwide in 2020 alone.
Adversaries generate millions of new malware signatures each year to stay undetectable.

引用

"With the exponential increase in the generation of newer malware families each year, it is complex to obtain a sufficient number of malware samples for each new malware class."
"Adversaries use code obfuscation, metamorphism, and polymorphism to mutate malware binary executables, making it complex to detect."

抽出されたキーインサイト

Effective Malware Detection for Embedded Computing Systems with Limited Exposure

by Sreenitha Ka... 場所 arxiv.org 04-04-2024

https://arxiv.org/pdf/2404.02344.pdf

深掘り質問

How can the proposed code-aware data generation technique be extended to handle zero-day malware attacks

The proposed code-aware data generation technique can be extended to handle zero-day malware attacks by incorporating real-time threat intelligence feeds and dynamic code analysis. By integrating a mechanism to continuously update the training dataset with the latest malware samples and patterns, the system can adapt to new and previously unseen threats. Additionally, leveraging anomaly detection algorithms and behavior analysis can help identify suspicious activities that align with zero-day attacks. Implementing a feedback loop that allows the system to learn from new zero-day attacks and adjust its detection capabilities accordingly will enhance its resilience against evolving threats.

What are the potential limitations of the few-shot learning approach in detecting highly sophisticated and targeted malware

The few-shot learning approach, while effective in classifying limitedly seen malware data, may face limitations when detecting highly sophisticated and targeted malware due to the following reasons:

Complexity of Malware Variants: Highly sophisticated malware variants may exhibit intricate obfuscation techniques, polymorphic behavior, and stealth mechanisms that can challenge the model's ability to generalize from limited samples.
Feature Extraction: Few-shot learning relies on extracting relevant features from the data. In cases of targeted malware with unique characteristics, the model may struggle to capture these specific features with limited training instances.
Adversarial Attacks: Advanced malware developers can craft attacks specifically to evade machine learning detection systems, making it challenging for the model to accurately classify such targeted threats.

To address these limitations, a combination of few-shot learning with ensemble methods, transfer learning, and continual learning approaches can be employed. By integrating diverse models and leveraging ongoing learning from new data, the system can enhance its adaptability and robustness in detecting highly sophisticated and targeted malware.

How can the hardware implementation of the proposed malware detection system be further optimized for energy-constrained embedded devices

To optimize the hardware implementation of the proposed malware detection system for energy-constrained embedded devices, several strategies can be employed:

Model Compression: Utilize techniques like quantization, pruning, and knowledge distillation to reduce the model size and computational complexity, thereby lowering energy consumption.
Hardware Acceleration: Implement specialized hardware accelerators like FPGAs or ASICs tailored for deep learning tasks to improve performance and energy efficiency.
Low-Power Design: Opt for low-power components, optimize clock frequencies, and minimize unnecessary operations to reduce power consumption.
Dynamic Voltage and Frequency Scaling (DVFS): Implement DVFS techniques to adjust the voltage and frequency of the processor based on workload requirements, optimizing energy usage.
Sleep Modes: Incorporate sleep modes and power gating mechanisms to minimize power consumption during idle periods.
Efficient Data Processing: Streamline data processing pipelines, reduce data movement, and optimize memory access patterns to minimize energy overhead.

By integrating these optimization techniques, the hardware implementation of the malware detection system can be tailored to operate efficiently within the constraints of energy-constrained embedded devices.