Advanced Persistent Threats (APTs) are sophisticated and long-lived attacks that are often backed by nationstates. Despite the security community’s efforts to design and deploy specialized systems to combat them, APTs have remained prevalent while persisting undetected for significantly more time than commodity cyber threats. In this paper, we measure this difference by conducting the first longitudinal analysis of APT infrastructure by shedding light on the lifecycle of their domain names. To enable this study, we build Atropos, a novel measurement methodology that automatically and accurately labels DNS records of APT domain names, enabling us to understand their lifecycle and gain a more comprehensive and contextualized infrastructure picture than the one that is shared in public reports. Using the comprehensive infrastructure view that Atropos provides, we study 405 APT actors over a period spanning a decade and unveil several novel findings regarding their utilization of network infrastructure that have practical implications. We find that APT actors provision their IPs to their domain names 317 days on average before an attack is publicly reported. Furthermore, $73.6 \%$ of the APT IPs that are part of the attack infrastructure no longer point to their domains at the time of first public disclosure, highlighting that researchers and security practitioners need to consider historic DNS data in order to get a more comprehensive and accurate picture when training network detection, investigation, or attribution systems. Organizations that are more sensitive to APT attacks will need to retain network logs for at least 19 to 25 months in order to have higher probabilities of discovering whether they have been a target of an APT attack. Finally, we provide evidence that APT actors re-use hosting providers, deploy APT network infrastructure close to their intended attack targets, and increasingly utilize more cloud-fronting. These findings are important because they can guide future threat detection and attribution works.

The complex optimizations supported by modern compilers allow for compiler provenance recovery at many levels. For instance, it is possible to identify the compiler family and optimization level used when building a binary, as well as the individual compiler passes applied to functions within the binary. Yet, many downstream applications of compiler provenance remain unexplored. To bridge that gap, we train and evaluate a multi-label compiler provenance model on data collected from over 27,000 programs built using LLVM 14, and apply the model to a number of security-related tasks. Our approach considers 68 distinct compiler passes and achieves an average F-1 score of 84.4%. We first use the model to examine the magnitude of compiler-induced vulnerabilities, identifying 53 information leak bugs in 10 popular projects. We also show that several compiler optimization passes introduce a substantial amount of functional code reuse gadgets that negatively impact security. Beyond vulnerability detection, we evaluate other security applications, including using recovered provenance information to verify the correctness of Rich header data in Windows binaries (e.g., forensic analysis), as well as for binary decomposition tasks (e.g., third party library detection).

This work explores authoritative DNS (AuthDNS) as a new measurement perspective for studying the large-scale epidemiology of the malware ecosystem—when and where infections occur, and what infrastructure spreads and controls malware. Utilizing an AuthDNS dataset from a top registrar, we observe malware heterogeneity (202 families), global infrastructure (399,830 IPs in 151 countries) and infection (40,937 querying Autonomous Systems (ASes)) visibility, as well as breadth of temporal coverage (2017–2021). This combination of factors enables an extensive analysis of the malware ecosystem that reinforces prior work on malware infrastructure and also contributes new perspectives on malware infection distribution and lifecycle. We find that malware families re-use infrastructure, especially in cloud hosting countries, but contrary to prior work, we do not detect targeting of clients by countries or industry sector. Furthermore, our 4-year lifecycle analysis of diverse malware families shows that infection analysis is temporally sensitive: over 90% of ASes first query a malicious domain after public detection, and a median of 38.6% ASes only query after domain expiration or takedown. To fit AuthDNS into the broader context of malware research, we conclude with a comparison of experimental vantage points on four qualitative aspects and discuss their advantages and limitations. Ultimately, we establish AuthDNS as a unique measurement perspective capable of measuring global malware infections.

Tor~\citetor is the most widely used anonymous communication network with millions of daily users~\citetormetrics. Since Tor provides server and client anonymity, hundreds of malware binaries found in the wild rely on it to hide their presence and hinder Command & Control (C&C) takedown operations. We believe Tor is a paramount tool enabling online freedom and privacy, and blocking it to defend against such malware is infeasible for both users and organizations. In this work, we present effective traffic analysis approaches that can accurately identify Tor-based malware communication. We collect hundreds of Tor-based malware binaries, execute and examine more than 47,000 active encrypted malware connections and compare them with benign browsing traffic. In addition to traditional traffic analysis features (which work at the connection level), we propose global host-level network features to capture peculiar malware communication fingerprints across host logs. Our experiments confirm that our models are able to detect "zero-day'' malware connections with 0.7% FPR even when malware connections constitute less than 5% of Tor traces in the test set. Using multi-labeling approaches, we are able to accurately detect the malware behavior-based classes (grayware, ransomware, etc). Finally, we evaluate the robustness of our models on real-world enterprise logs and show that the classifiers can identify infected hosts even with missing features.

Being able to enumerate potentially vulnerable IoT devices across the Internet is important, because it allows for assessing global Internet risks and enables network operators to check the hygiene of their own networks. To this end, in this paper we propose IoTFinder, a system for efficient, large-scalepassiveidentification of IoT devices. Specifically, we leverage distributed passive DNS data collection, and develop a machine learning-based system that aims to accurately identify a large variety of IoT devices based solely on theirDNS fingerprints. Our system is independent of whether the devices reside behind a NAT or other middleboxes, or whether they are assigned an IPv4 or IPv6 address. We design IoTFinder as a multi-label classifier, and evaluate its accuracy in several different settings, including computing detection results over a third-party IoT traffic dataset and DNS traffic collected at a US-based ISP hosting more than 40 million clients. The experimental results show that our approach allows for accurately detecting many diverse IoT devices, even when they are hosted behind a NAT and their traffic is “mixed” with traffic generated by other IoT and non-IoT devices hosted in the same local network.

Over 55% of the world's websites run on Content Management Systems (CMS). Unfortunately, this huge user population has made CMS-based websites a high-profile target for hackers. Worse still, the vast majority of the website hosting industry has shifted to a "backup and restore" model of security, which relies on error-prone AV scanners to prompt users to roll back to a pre-infection nightly snapshot. This research had the opportunity to study these nightly backups for over 300,000 unique production websites. In doing so, we measured the attack landscape of CMS-based websites and assessed the effectiveness of the backup and restore protection scheme. To our surprise, we found that the evolution of tens of thousands of attacks exhibited clear long-lived multi-stage attack patterns. We now propose TARDIS, an automated provenance inference technique, which enables the investigation and remediation of CMS-targeting attacks based on only the nightly backups already being collected by website hosting companies. With the help of our industry collaborator, we applied TARDIS to the nightly backups of those 300K websites and found 20,591 attacks which lasted from 6 to 1,694 days, some of which were still yet to be detected.

Home-based IoT devices have a bleak reputation regarding their security practices.On the surface, the insecurities of IoT devices seem to be caused by integration problems that may be addressed by simple measures, but this work finds that to be a naive assumption.The truth is, IoT deployments, at their core, utilize traditional compute systems, such as embedded, mobile, and network.These components have many unexplored challenges such as the effect of over-privileged mobile applications on embedded devices.Our work proposes a methodology that researchers and practitioners could employ to analyze security properties for home-based IoT devices.We systematize the literature for homebased IoT using this methodology in order to understand attack techniques, mitigations, and stakeholders.Further, we evaluate 45 devices to augment the systematized literature in order to identify neglected research areas.To make this analysis transparent and easier to adapt by the community, we provide a public portal to share our evaluation data and invite the community to contribute their independent findings.