eXpose: A Character-Level Convolutional Neural Network with Embeddings For Detecting Malicious URLs, File Paths and Registry Keys (1702.08568v1)

Abstract: For years security machine learning research has promised to obviate the need for signature based detection by automatically learning to detect indicators of attack. Unfortunately, this vision hasn't come to fruition: in fact, developing and maintaining today's security machine learning systems can require engineering resources that are comparable to that of signature-based detection systems, due in part to the need to develop and continuously tune the "features" these machine learning systems look at as attacks evolve. Deep learning, a subfield of machine learning, promises to change this by operating on raw input signals and automating the process of feature design and extraction. In this paper we propose the eXpose neural network, which uses a deep learning approach we have developed to take generic, raw short character strings as input (a common case for security inputs, which include artifacts like potentially malicious URLs, file paths, named pipes, named mutexes, and registry keys), and learns to simultaneously extract features and classify using character-level embeddings and convolutional neural network. In addition to completely automating the feature design and extraction process, eXpose outperforms manual feature extraction based baselines on all of the intrusion detection problems we tested it on, yielding a 5%-10% detection rate gain at 0.1% false positive rate compared to these baselines.

Analysis of eXpose: A Character-Level CNN for Security Threat Detection

The paper "eXpose: A Character-Level Convolutional Neural Network with Embeddings For Detecting Malicious URLs, File Paths and Registry Keys" by Joshua Saxe and Konstantin Berlin introduces a convolutional neural network (CNN) approach to detect malicious entities within cybersecurity. Distinct from traditional methods that rely heavily on manual feature engineering, eXpose utilizes deep learning to perform feature extraction and classification directly from raw input strings. This approach is applied to security-relevant artifacts such as URLs, file paths, and registry keys, which are commonly encountered in cybersecurity environments.

Methodology

The eXpose model operates on character-level embeddings, representing each character in a sequence with a continuous vector. It subsequently employs convolutional layers to detect patterns indicative of malicious intent. This pattern detection is akin to n-gram analysis but occurs at a more granular level, capturing semantic similarities within the character sequences. The architecture includes three main components:

1. Character Embedding: Converts input characters to a numeric embedding.
2. Feature Detection: Utilizes CNNs to identify significant character sequences.
3. Classification: Determines the likelihood of maliciousness using dense neural networks.

Evaluation

eXpose was evaluated against baseline models that use traditional n-gram-based and feature-based methods. The results demonstrated a superior performance by eXpose, particularly in scenarios involving URLs, file paths, and registry keys, achieving a 5%-10% higher true positive rate at a 0.1% false positive rate across various datasets.

Implications

The potential implications of leveraging eXpose are multifold. Practically, the method reduces the need for continuous manual feature engineering, potentially lowering the cost and complexity of maintaining and updating cybersecurity systems in response to evolving threats. Theoretically, this work indicates a broader applicability of character-level convolutional neural networks in domains beyond image processing, highlighting their capability in understanding and exploiting local patterns in text-like data inputs.

Future Directions

While eXpose shows promise, its performance on longer and more complex sequences is constrained by computational costs. Future work can explore optimized training methodologies and the integration of distributed computing techniques to scale the model's capability. Additionally, improving character representations for non-ASCII inputs could broaden the utility of eXpose across different linguistic environments.

This paper lays foundational work for future exploration in using deep learning architectures directly on raw data inputs, a direction that could greatly enhance automated feature extraction and classification in cybersecurity and other domains.

Conclusion

The introduction of eXpose as a character-level neural network for detecting malicious artifacts marks a significant step toward automating and improving the accuracy of cybersecurity threat detection. The paper effectively demonstrates the advantages of CNNs in automating feature extraction, encouraging further innovation in applying deep learning within cybersecurity contexts. As hardware capabilities evolve, such approaches will likely play an increasingly pivotal role in adaptive security measures.