Graph modeling allows numerous security problems to be tackled in a general way, however, little work has been done to understand their ability to withstand adversarial attacks. We design and evaluate two novel graph attacks against a state-of-the-art network-level, graph-based detection system. Our work highlights areas in adversarial machine learning that have not yet been addressed, specifically: graph-based clustering techniques, and a global feature space where realistic attackers without perfect knowledge must be accounted for (by the defenders) in order to be practical. Even though less informed attackers can evade graph clustering with low cost, we show that some practical defenses are possible.

As threat detection systems become critical for protecting modern organizations, visualization has emerged as an essential tool for security analysts to understand network threats. However, there is currently little research in designing and evaluating effective network threat analysis visualizations. To address this problem, we take a user-centered approach, starting with designing an open source threat analysis console for DNS-based network threat analysis grounded in both an understanding of analysts' needs and tasks and security visualization best practices. The proposed open source threat analysis console, called THACO (THreat Analysis COnsole), leverages open DNS datasets, domain WHOIS records, and both public malware and domain blacklists. It also uses a visually scalable visualization technique, a multi-grouping, zoomable treemap, to adapt to DNS-based network threat analysis needs. Then, we conduct a user study with 7 in-situ and 31 online IT security practitioners. Our code for THACO and THACO itself will be opened to the community in order to further improve the ability of analysts to perform network threat analysis and better secure their networks.

Devices infected with malicious software typically form botnet armies under the influence of one or more command and control (C&C) servers. The botnet problem reached such levels where federal law enforcement agencies have to step in and take actions against botnets by disrupting (or “taking down”) their C&Cs, and thus their illicit operations. Lately, more and more private companies have started to independently take action against botnet armies, primarily focusing on their DNS-based C&Cs. While well-intentioned, their C&C takedown methodology is in most cases ad-hoc, and limited by the breadth of knowledge available around the malware that facilitates the botnet. With this paper, we aim to bring order, measure, and reason to the botnet takedown problem. We improve an existing takedown analysis system called rza. Specifically, we examine additional botnet takedowns, enhance the risk calculation to use botnet population counts, and include a detailed discussion of policy improvements that can be made to improve takedowns. As part of our system evaluation, we perform a postmortem analysis of the recent 3322.org, Citadel, and No-IP takedowns.

Any individual that re-registers an expired domain implicitly inherits the residual trust associated with the domain's prior use. We find that adversaries can, and do, use malicious re-registration to exploit domain ownership changes - undermining the security of both users and systems. In fact, we find that many seemingly disparate security problems share a root cause in residual domain trust abuse. With this study we shed light on the seemingly unnoticed problem of residual domain trust by measuring the scope and growth of this abuse over the past six years. During this time, we identified 27,758 domains from public blacklists and 238,279 domains resolved by malware that expired and then were maliciously re-registered. To help address this problem, we propose a technical remedy and discuss several policy remedies. For the former, we develop Alembic, a lightweight algorithm that uses only passive observations from the Domain Name System (DNS) to flag potential domain ownership changes. We identify several instances of residual trust abuse using this algorithm, including an expired APT domain that could be used to revive existing infections.

The Something Awful Forums (SAF) is an online community comprised of a loosely connected federation of forums, united in a distinctive brand of humor with a focus on the quality of member contributions. In this case study we find that the site has sustained success while deviating from common conventions and norms of online communities. Humor and the quality of content contributed by SAF members foster practices that seem counterintuitive to the development of a stable and thriving community. In this case study we show how design decisions are contextual and inter-dependent and together these heuristics create a different kind of online third place that challenges common practices.

In this paper, we present an analysis of a new class of domain names: disposable domains. We observe that popular web applications, along with other Internet services, systematically use this new class of domain names. Disposable domains are likely generated automatically, characterized by a "one-time use" pattern, and appear to be used as a way of "signaling" via DNS queries. To shed light on the pervasiveness of disposable domains, we study 24 days of live DNS traffic spanning a year observed at a large Internet Service Provider. We find that disposable domains increased from 23.1% to 27.6% of all queried domains, and from 27.6% to 37.2% of all resolved domains observed daily. While this creative use of DNS may enable new applications, it may also have unanticipated negative consequences on the DNS caching infrastructure, DNSSEC validating resolvers, and passive DNS data collection systems.

Devices infected with malicious software typically form botnet armies under the influence of one or more command and control (C&C) servers. The botnet problem reached such levels where federal law enforcement agencies have to step in and take actions against botnets by disrupting (or "taking down") their C&Cs, and thus their illicit operations. Lately, more and more private companies have started to independently take action against botnet armies, primarily focusing on their DNS-based C&Cs. While well-intentioned, their C&C takedown methodology is in most cases ad-hoc, and limited by the breadth of knowledge available around the malware that facilitates the botnet.

This paper presents MalwareVis, a utility that provides security researchers a method to browse, filter, view and compare malware network traces as entities.

In this paper we describe and evaluate a technique to improve the amount of information gained from dynamic malware analysis systems. By playing network games during analysis, we explore the behavior of malware when it believes its network resources are malfunctioning. This forces the malware to reveal its alternative plan to the analysis system resulting in a more complete understanding of malware behavior. Network games are similar to multipath exploration techniques, but are resistant to conditional code obfuscation. Our experimental results show that network games discover highly useful network information from malware. Of the 161,000 domain names and over three million IP addresses coerced from malware during three weeks, over 95% never appeared on public blacklists. We show that this information is both likely to be malicious and can be used to improve existing domain name and IP address reputation systems, blacklists, and network-based malware clustering systems.

Mobile application markets currently serve as the main line of defense against malicious applications. While marketplace revocations have successfully removed the few overtly malicious applications installed on mobile devices, the anticipated coming flood of mobile malware mandates the need for mechanisms that can respond faster than manual intervention. In this paper, we propose an infrastructure that automatically identifies and responds to malicious mobile applications based on their network behavior. We design and implement a prototype, Airmid, that uses cooperation between in-network sensors and smart devices to identify the provenance of malicious traffic. We then develop sample malicious mobile applications exceeding the capabilities of malware recently discovered in the wild, demonstrate the ease with which they can evade current detection techniques, and then use Airmid to show a range of automated recovery responses ranging from on-device firewalling to application removal.