Inference based techniques are one of the major approaches to analyze DNS data and detecting malicious domains. The key idea of inference techniques is to first define associations between domains based on features extracted from DNS data. Then, an inference algorithm is deployed to infer potential malicious domains based on their direct indirect associations with known malicious ones. The way associations are defined is key to the effectiveness of an inference technique. It is desirable to be both accurate (i.e., avoid falsely associating domains with no meaningful connections) and with good coverage (i.e., identify all associations between domains with meaningful connections). Due to the limited scope of information provided by DNS data, it becomes a challenge to design an association scheme that achieves both high accuracy and good coverage. In this paper, we propose a new association scheme to identify domains controlled by the same entity. Our key idea is an in-depth analysis of active DNS data to accurately separate public IPs from dedicated ones, which enables us to build high-quality associations between domains. Our scheme identifies many meaningful connections between domains that are discarded by existing state-of-the-art approaches. Our experimental results show that the proposed association scheme not only significantly improves the domain coverage compared to existing approaches but also achieves better detection accuracy. Existing path-based inference algorithm is specifically designed for DNS data analysis. It is effective but computationally expensive. As a solution, we investigate the effectiveness of combining our association scheme with the generic belief propagation algorithm. Through comprehensive experiments, we show that this approach offers significant efficiency and scalability improvement with only minor negative impact of detection accuracy.
HTTP is a popular channel for malware to communicate with malicious servers (e.g., Command a Control, drive-by download, drop-zone), as well as to attack benign servers. By utilizing HTTP requests, malware easily disguises itself under a large amount of benign HTTP traffic. Thus, identifying malicious HTTP activities is challenging. We leverage an insight that cyber criminals are increasingly using dynamic malicious infrastructures with multiple servers to be efficient and anonymous in (i) malware distribution (using redirectors and exploit servers), (ii) control (using CaC servers) and (iii) monetization (using payment servers), and (iv) being robust against server takedowns (using multiple backups for each type of servers). Instead of focusing on detecting individual malicious domains, we propose a complementary approach to identify a group of closely related servers that are potentially involved in the same malware campaign, which we term as Associated Server Herd (ASH). Our solution, SMASH (Systematic Mining of Associated Server Herds), utilizes an unsupervised framework to infer malware ASHs by systematically mining the relations among all servers from multiple dimensions. We build a prototype system of SMASH and evaluate it with traces from a large ISP. The result shows that SMASH successfully infers a large number of previously undetected malicious servers and possible zero-day attacks, with low false positives. We believe the inferred ASHs provide a better global view of the attack campaign that may not be easily captured by detecting only individual servers.