Automated Dynamic Analysis of Ransomware: Benefits, Limitations and use for Detection (1609.03020v1)

Abstract: Recent statistics show that in 2015 more than 140 millions new malware samples have been found. Among these, a large portion is due to ransomware, the class of malware whose specific goal is to render the victim's system unusable, in particular by encrypting important files, and then ask the user to pay a ransom to revert the damage. Several ransomware include sophisticated packing techniques, and are hence difficult to statically analyse. We present EldeRan, a machine learning approach for dynamically analysing and classifying ransomware. EldeRan monitors a set of actions performed by applications in their first phases of installation checking for characteristics signs of ransomware. Our tests over a dataset of 582 ransomware belonging to 11 families, and with 942 goodware applications, show that EldeRan achieves an area under the ROC curve of 0.995. Furthermore, EldeRan works without requiring that an entire ransomware family is available beforehand. These results suggest that dynamic analysis can support ransomware detection, since ransomware samples exhibit a set of characteristic features at run-time that are common across families, and that helps the early detection of new variants. We also outline some limitations of dynamic analysis for ransomware and propose possible solutions.

• The paper presents EldeRan, a framework that leverages runtime behavior analysis to identify and classify ransomware.
• It employs regularized logistic regression and achieves a ROC-AUC of 0.995, outperforming traditional static analysis methods.
• The study highlights the practical benefits of dynamic analysis in countering sophisticated ransomware obfuscation techniques.

Automated Dynamic Analysis of Ransomware: Benefits, Limitations, and Use for Detection

The paper presents an in-depth examination of dynamic analysis techniques for ransomware identification and proposes a machine learning-based framework called EldeRan for detecting and classifying ransomware. The increasing prevalence of ransomware, a form of malware that encrypts a user's files and demands payment for decryption, underscores the need for effective detection methods. As ransomware becomes more sophisticated, relying primarily on static analysis becomes insufficient, driving the adoption of dynamic analysis methodologies.

Structure and Approach

EldeRan is designed based on the observation that ransomware exhibits distinctive features during the early stages of execution. The system captures a wide range of runtime behaviors, categorizing them into various feature sets, including API calls, file system operations, and registry key interactions. The feature selection process leverages the Mutual Information criterion to pinpoint the most significant characteristics that differentiate ransomware from legitimate software.

Subsequently, the authors employ Regularized Logistic Regression for classification, emphasizing its balance between performance and simplicity. This algorithm not only provides competitive accuracy compared to more complex models like SVM but also simplifies the integration of new observations into the model without the need for extensive retraining.

Key Findings

The empirical evaluation of EldeRan against a dataset of 582 ransomware samples from 11 families, alongside 942 benign software applications, demonstrated compelling results. EldeRan achieved an impressive ROC-AUC of 0.995, surpassing the detection capabilities of certain AV systems, which typically rely on static signature analysis. In terms of detection accuracy, EldeRan identified new ransomware families with an average detection rate of 93.3%, showcasing its potential in early-stage ransomware detection. The paper underscores the importance of runtime behavior analysis, particularly in scenarios where ransomware packers and obfuscation techniques undermine static analysis efficacy.

Practical and Theoretical Implications

This research suggests several critical implications for cybersecurity strategies against ransomware. Practically, it provides a robust tool for augmenting current antivirus solutions, potentially reducing the initial damage incurred from ransomware outbreaks. Theoretically, it contributes to the broader narrative that dynamic features can effectively capture ransomware behavior, offering a reliable basis for automated detection models. The paper reinforces the paradigm shift towards behavior-based detection strategies in cybersecurity.

Prospective Developments

Looking forward, the evolution of ransomware will likely necessitate advancements in dynamic analysis techniques. Potential areas for future development include improving resilience against evasion tactics, optimizing the analysis timeframe to enhance real-time detection capabilities, and refining feature selection processes to maintain classifier efficiency. These enhancements could further bolster EldeRan's utility as a versatile tool for combating ransomware, accommodating the perpetual evolution of malware techniques and safeguarding digital environments.

In conclusion, the EldeRan framework represents a significant step forward in the fight against ransomware, shedding light on the strengths and limitations of dynamic analysis and emphasizing the necessity for adaptive, machine learning-driven detection systems in the ongoing battle against increasingly sophisticated cyber threats.