Successful malware campaigns rely on Command-and-Control (C2) infrastructure, enabling attackers to extract sensitive data and give instructions to bots. As a resilient mechanism to obtain C2 endpoints, attackers can employ Domain Generation Algorithms (DGAs), which automatically generate C2 domains instead of relying on static ones. Thus, researchers have proposed network-level detection approaches that reveal DGA usage by differentiating between non-DGA and generated domains. Recent approaches train machine learning (ML) models to recognize DGA domains using pattern recognition at the domain’s character level.

Currently, WHOIS is the main method for identifying which company or individual owns a domain or website. But, WHOIS usefulness is limited due to privacy protection services and data redaction. We present a novel automated approach for domain and website attribution. When WHOIS data does not reveal the owner, our approach leverages information from multiple other sources such as passive DNS, TLS certificates, and the analysis of website content. We propose a novel ranking technique to select the domain owner among multiple identified entities. Our approach identifies the domain owner with an F1 score of 0.94 compared to 0.54 for WHOIS. When applied on 3,001 tracker domains from the popular Disconnect list, it identifies needed updates to the list. It also attributes 84% of previously unattributed tracker domains.

Tags can be used by malware repositories and analysis services to enable searches for samples of interest across different dimensions. Automatically extracting tags from AV labels is an efficient approach to categorize and index massive amounts of samples. Recent tools like AVclass and Euphony have demonstrated that, despite their noisy nature, it is possible to extract family names from AV labels. However, beyond the family name, AV labels contain much valuable information such as malware classes, file properties, and behaviors.

Malicious developers may succeed at publishing their apps in mobile markets, including the official ones. If reported, the apps will be taken down and the developer accounts possibly be banned. Unfortunately, such take-downs do not prevent the attackers to use other developer accounts to publish variations of their malicious apps. This work presents a novel approach for identifying developer accounts, and other indicators of compromise (IOCs) in mobile markets, that belong to the same operation, i.e., to the same owners. Given a set of seed IOCs, our approach explores app and version metadata to identify new IOCs that belong to the same operation. It outputs an attribution graph, which details the attribution inferences, so that they can be reviewed. We have implemented our approach into Retriever, a tool that supports multiple mobile markets including the official GooglePlay and AppleStore. We have evaluated Retriever on 17 rogueware and adware operations. In 94% of the operations, Retriever discovers at least one previously unknown developer account. Furthermore, Retriever reveals that operations that look dead still have active developer accounts.