Online Clustering of Known and Emerging Malware Families to Assist Malware Analysis

Core Concepts

A novel machine learning-based model for the online clustering of malicious samples into known and emerging malware families, enabling faster malware analysis.

Abstract

The paper presents a novel machine learning-based model for the online clustering of malicious samples into known and emerging malware families. The key highlights are:

The model divides streaming data into samples from known and new emerging malware families using a clustering decision rule.

Samples from known malware families are classified using a weighted k-nearest neighbor (WKNN) classifier, while samples from new families are clustered using online clustering algorithms such as online k-means (OKM), self-organizing map (SOM), and basic sequential algorithmic scheme (BSAS).

Experimental results on the EMBER dataset show that the proposed model can achieve high purity of clusters, with OKM reaching up to 93.34% purity for 10 clusters and SOM achieving the highest average silhouette coefficient of 0.99 for 4 clusters.

The computational times for the online clustering of new malware family samples are less than 1 second on average, indicating the model's efficiency in processing daily incoming malware samples.

The proposed approach outperforms directly applying online clustering algorithms to the entire dataset, demonstrating the benefits of leveraging both the fixed unlabeled dataset and the streaming data.

The model aims to simplify the work of malware analysts by providing them with similar malware samples, thereby speeding up the overall analysis process. It also enables the examination of the evolution of individual malware families over time, which can help predict future variants.

Stats

The EMBER dataset contains 400,000 feature vectors corresponding to malicious samples from more than 3,000 malware families.
The training dataset D used in the model consists of samples from the four most prevalent malware families: Xtrat, Zbot, Ramnit, and Sality.
The streaming data S used in the model consists of samples from the four known malware families and three additional new families: Emotet, Ursnif, and Sivis.

Quotes

"Malware attacks have become significantly more frequent and sophisticated in recent years. Therefore, malware detection and classification are critical components of information security."
"Clustering algorithms are thus becoming more widely used in computer security to analyze the behavior of malware variants and discover new malware families."
"Online clustering algorithms help us to understand malware behavior and produce a quicker response to new threats."

Key Insights Distilled From

Online Clustering of Known and Emerging Malware Families

by Olha... at arxiv.org 05-07-2024

https://arxiv.org/pdf/2405.03298.pdf

Deeper Inquiries

How can the proposed model be extended to handle both benign and malicious samples in the streaming data

To extend the proposed model to handle both benign and malicious samples in the streaming data, we can incorporate a malware detection model as a preprocessing step before clustering. This model can classify each sample in the streaming data as either benign or malicious. Once the samples are classified, we can then apply the clustering algorithm to group the malicious samples into malware families. By separating the benign and malicious samples beforehand, we ensure that the clustering is focused solely on the malicious data, improving the accuracy and efficiency of the clustering process.

What other machine learning techniques, such as semi-supervised learning, could be incorporated to further improve the clustering of known and emerging malware families

Incorporating semi-supervised learning techniques can further enhance the clustering of known and emerging malware families. One approach could involve using a small subset of labeled data from the streaming samples to guide the clustering process. By providing some labeled data, the model can learn from both labeled and unlabeled samples, improving the clustering accuracy. Techniques like self-training, co-training, or pseudo-labeling can be employed to iteratively improve the clustering results by leveraging the limited labeled data available.

How can the insights gained from the evolution of malware families over time be used to develop more proactive and adaptive malware detection and prevention strategies

Insights gained from the evolution of malware families over time can be instrumental in developing proactive and adaptive malware detection and prevention strategies. By analyzing the behavior and characteristics of malware families as they evolve, security experts can anticipate future trends and patterns in malware attacks. This knowledge can be used to enhance existing detection algorithms, develop new signatures for malware identification, and improve response mechanisms to emerging threats. Additionally, understanding the evolution of malware families can help in creating more robust cybersecurity measures, such as anomaly detection systems and threat intelligence platforms, to stay ahead of evolving malware tactics and techniques.