Malware Detection by Eating a Whole EXE

Abstract: In this work we introduce malware detection from raw byte sequences as a fruitful research area to the larger machine learning community. Building a neural network for such a problem presents a number of interesting challenges that have not occurred in tasks such as image processing or NLP. In particular, we note that detection from raw bytes presents a sequence problem with over two million time steps and a problem where batch normalization appear to hinder the learning process. We present our initial work in building a solution to tackle this problem, which has linear complexity dependence on the sequence length, and allows for interpretable sub-regions of the binary to be identified. In doing so we will discuss the many challenges in building a neural network to process data at this scale, and the methods we used to work around them.

• The paper presents MalConv, a deep learning model that directly processes raw executable bytes to detect malware without relying on handcrafted features.
• Using large convolutional filters and max-pooling, the model handles over two million time steps and achieves an AUC of 98.5 on heterogeneous data sets.
• The method reduces preprocessing complexity by analyzing multi-modal data directly, opening pathways for scalable and automated malware detection improvements.

Malware Detection by Eating a Whole EXE

In this paper, the authors introduce a novel approach to malware detection by leveraging neural networks to analyze raw byte sequences of executable files without reliance on domain-specific feature extraction. Unlike traditional methods that utilize manually crafted rules or dynamic analysis requiring execution in a specialized environment, this approach focuses on static analysis by directly processing the raw byte structure of the binary files. This introduces significant challenges, particularly due to the sequence length exceeding two million time steps and the complexity of spatial correlation within binaries.

Introduction to the MalConv Architecture

The core contribution of the paper is the MalConv model, which is capable of processing the entire raw byte sequence of executables efficiently. MalConv employs a simple yet effective architecture consisting of an embedding layer followed by convolutional filters and max-pooling, which enables it to capture both global and local contextual information across the entire binary. The model addresses the challenge of lengthy input sequences by using large convolutional filters (500 bytes) with an equivalent stride, thereby allowing the entire file to be processed in one pass while maintaining linear complexity relative to sequence length.

Figure 1: Simple demonstration of the spatio-temporal problem caused by creating malware "images". The red dashed area shows the receptive field of a convolution, mapped from the malware image form (top) back to the raw byte sequence (bottom).

Challenges and Methodology

The paper identifies several intrinsic challenges when attempting to leverage deep neural networks for malware detection directly from raw bytes, such as the multi-modal nature of binaries. Binaries can include a variety of data types, such as ASCII text, machine code, or multimedia resources embedded within the same file. MalConv processes these different data modalities without the need for prior conversion to another domain representation like feature vectors, thereby reducing pre-processing complexity.

The architecture also deals with the inefficacy of batch normalization in this context, thought to be due to the highly non-Gaussian distribution of activation responses within the network compared to image or signal processing tasks. Through extensive experimentation, it is shown that standard batch normalization techniques fail, requiring alternative regularization.

Figure 2: Full architecture diagram of MalConv model.

Evaluation and Results

The evaluation demonstrates that MalConv outperforms baselines based on byte n-grams, though it initially appeared to converge poorly when using batch normalization. A notable finding is the model's ability to generalize across heterogeneous test sets, showing robust performance on files obtained from different environments (Group A and Group B data sets), and reaching an AUC of 98.5 on Group A data.

Results also highlight the model's resilience to overfitting, achieving competitive performance without the need for significant regularization techniques. The paper emphasizes the potential for optimization and improvement by using larger datasets, showing enhanced performance with a larger 2 million file training set.

Practical Implications and Future Work

This research underscores the potential of deep learning to transform traditional malware detection approaches by reducing reliance on handcrafted signatures and rules. The MalConv architecture provides a foundation for further exploration into scalable neural approaches for binary analysis, offering possibilities for integration into existing security infrastructure to tackle evolving malware threats.

Future work is directed towards refining the MalConv model by exploring new normalization techniques to cope with multi-modal data distributions and reducing the computational burden associated with extensive sequence lengths. Continued development in this direction may reveal new methodologies for automated malware detection and provide insights into adversarial strategies to circumvent detection systems.

Conclusion

Overall, the paper presents an innovative method for malware detection leveraging deep learning frameworks to process raw exec files, with implications beyond the immediate domain of malicious software detection. It opens pathways not only for enhancing detection accuracy but also for reducing the operational complexity and resources required for analyzing malicious binaries.