DeepSign: Deep Learning for Automatic Malware Signature Generation and Classification (1711.08336v2)

Abstract: This paper presents a novel deep learning based method for automatic malware signature generation and classification. The method uses a deep belief network (DBN), implemented with a deep stack of denoising autoencoders, generating an invariant compact representation of the malware behavior. While conventional signature and token based methods for malware detection do not detect a majority of new variants for existing malware, the results presented in this paper show that signatures generated by the DBN allow for an accurate classification of new malware variants. Using a dataset containing hundreds of variants for several major malware families, our method achieves 98.6% classification accuracy using the signatures generated by the DBN. The presented method is completely agnostic to the type of malware behavior that is logged (e.g., API calls and their parameters, registry entries, websites and ports accessed, etc.), and can use any raw input from a sandbox to successfully train the deep neural network which is used to generate malware signatures.

• The paper presents DeepSign, a deep learning approach using a Deep Belief Network (DBN) to automatically generate compact and invariant malware signatures from raw data.
• DeepSign achieves a high classification accuracy of 98.6% on a dataset of hundreds of malware variants across six major families, demonstrating its effectiveness in detecting new variants.
• The methodology is robust and adaptable, capable of processing various types of raw input data from sandbox environments, making it applicable to diverse malware behaviors.

Overview of "DeepSign: Deep Learning for Automatic Malware Signature Generation and Classification"

The paper presents a deep learning approach named DeepSign, focused on the automatic generation and classification of malware signatures. The authors utilize a Deep Belief Network (DBN) built on stacks of deep denoising autoencoders to create invariant representations of malware behavior. This approach addresses the limitations of traditional signature-based or token-based methods, which often fail to detect new malware variants effectively.

Key Contributions

1. Deep Belief Network for Signature Generation: The primary innovation is the use of a DBN, which utilizes a deep stack of denoising autoencoders to convert raw malware data into a highly compact and distinct signature. The representation generated is relatively invariant to small modifications in malware code, thereby enhancing detection accuracy for new variants.
2. High Classification Accuracy: Using a dataset of hundreds of malware variants across several major families, DeepSign achieves a classification accuracy of 98.6%. This suggests that the methodology can potentially offer significant improvements over existing malware detection systems.
3. Robustness to Input Variability: The methodology is designed to be agnostic of the type of malware behavior logged, whether it involves API calls, file system alterations, or network activity. This adaptability implies that any raw input data from a sandbox environment can effectively train the neural network to generate relevant malware signatures.

Methodology

The proposed system converts sandbox-generated text files into binary bit-strings by employing techniques analogous to unigram extraction in NLP. After processing, these vectors form input to the DBN, which is trained layer-wise, producing a 30-dimensional floating point vector as the malware 'signature'. A deep denoising autoencoder framework ensures the noise-tolerant nature of the learned signatures, allowing them to retain robustness against minor perturbations typically seen in evolved malware.

Experimental Evaluation

The empirical analysis of DeepSign leverages six major malware categories with 300 variants each. Using this dataset, the DBN derived signatures were evaluated for classification efficacy, revealing that such compact representations maintain close proximity for variants within a class, thereby managing to differentiate between classes with impressive accuracy. The DBN results were further refined using supervised training that utilized the pre-trained weights, yielding an even higher classification accuracy.

The paper effectively demonstrates the potential of deep learning techniques to substantially enhance malware detection and signature generation. The ability to detect malware variants reliably is especially valuable given the frequent and subtle variations that malware developers employ to bypass traditional detection systems.

Implications and Future Work

The success of DeepSign opens avenues for applying deep learning in cybersecurity, particularly in tasks requiring automated pattern recognition amidst high variability. Practically speaking, it heralds a move towards more dynamic and adaptive security models that reduce reliance on labor-intensive manual signature crafting. Theoretically, the research reinforces the versatility and potency of deep learning frameworks beyond their traditionally envisioned domains.

Future work could explore broader datasets, including more varied malware families and sandbox environments, to assess generalizability further. Additionally, integrating this approach with real-time threat detection systems and cloud-based services might offer enhanced, scalable defensive measures within organizational cybersecurity infrastructures. As deep learning continues to evolve, its potential in cybersecurity applications like those explored in this work remains expansive and promising.