Early Stage Malware Prediction Using Recurrent Neural Networks (1708.03513v3)

Abstract: Static malware analysis is well-suited to endpoint anti-virus systems as it can be conducted quickly by examining the features of an executable piece of code and matching it to previously observed malicious code. However, static code analysis can be vulnerable to code obfuscation techniques. Behavioural data collected during file execution is more difficult to obfuscate, but takes a relatively long time to capture - typically up to 5 minutes, meaning the malicious payload has likely already been delivered by the time it is detected. In this paper we investigate the possibility of predicting whether or not an executable is malicious based on a short snapshot of behavioural data. We find that an ensemble of recurrent neural networks are able to predict whether an executable is malicious or benign within the first 5 seconds of execution with 94% accuracy. This is the first time general types of malicious file have been predicted to be malicious during execution rather than using a complete activity log file post-execution, and enables cyber security endpoint protection to be advanced to use behavioural data for blocking malicious payloads rather than detecting them post-execution and having to repair the damage.

• The paper demonstrates that an RNN can predict malware behavior within the first 5 seconds of execution with 94% accuracy.
• The approach leverages behavioral data like CPU usage, memory use, and packet transmission, which are less susceptible to obfuscation than API calls.
• The study simulates zero-day detection and uses ensemble methods with extensive hyperparameter tuning to enhance real-time cybersecurity defenses.

An Examination of Early-Stage Malware Prediction Using Recurrent Neural Networks

Matilda Rhode, Pete Burnap, and Kevin Jones present a novel approach in estimating the malicious nature of executable files using Recurrent Neural Networks (RNNs), providing significant advancements in the field of dynamic malware analysis. The work highlights the use of behavioral data captured during the initial stages of file execution, enabling a potentially more responsive and proactive defense mechanism in cybersecurity systems.

Their research investigates the limitations of static analysis, which, though efficient, is prone to obfuscation tactics that allow malware authors to evade detection. In contrast, dynamic analysis, despite being more robust against such obfuscation, typically incurs a latency that hampers real-time protection as it requires extended file execution time. This paper aims to reconcile these shortcomings by leveraging RNNs to make predictive assessments of malicious behavior within a much-reduced timespan—specifically within the first five seconds of execution, demonstrating a 94% accuracy rate.

The authors utilize a dataset comprising a mix of benign and malicious software, with samples initially classified using the VirusTotal API, to ensure a robust evaluation of their model. They simulate a real-world scenario by splitting this dataset based on the first-seen date on VirusTotal, effectively testing their approach against the most recent malware samples.

Key insights from their methodology include the selection of machine activity features such as CPU usage, memory use, and packet transmission, instead of the often-utilized API calls, to feed into the RNN. They argue that continuous data types offer greater resilience to adversarial manipulation and obfuscation, due to their numerical representation which facilitates better interpolation in the face of unseen input values.

Acknowledging the challenge posed by the variable nature of malware, the researchers conduct extensive hyperparameter tuning using a random search methodology. This approach is vital given the necessity for adaptable models in rapidly evolving threat landscapes. The ensemble method, which incorporates multiple RNN configurations, further enhances robustness by amalgamating diverse learned representations of malware indicators.

One of the standout aspects is the paper's simulation of zero-day detection capabilities. By omitting known families from the training dataset, the authors showcase impressive detection rates for unfamiliar variants, reiterating the predictive strength of early-stage behavior patterns. The paper also explores a case paper concerning ransomware detection, reflecting on the model’s capability to identify such malware without prior exposure to explicit ransomware samples.

The implications of integrating such a model into endpoint security systems are considerable. The proposal to use their model as a preliminary layer for quick assessment addresses real-time detection requirements, and offers a framework that could preemptively obstruct the execution of malicious payloads. Furthermore, their discussion on the potential for porting the proposed model onto diverse operating systems and types of malware signifies a commitment to future-proofing and expanding the scope of their research.

One limitation discussed is the potential for adversaries to delay malicious activities to evade early-stage detection systems. The authors suggest that future research could tackle these through continuous live-monitoring strategies such as sliding windows.

To conclude, this paper contributes valuable advancements in the use of RNNs for malware detection, particularly in addressing the latency inherent in dynamic analysis. It sets a precedent for future research aiming to strike a balance between prediction accuracy and processing speed, marking a step towards integrating behavioral analysis seamlessly into real-time cybersecurity defenses.