Chris Grier

Suspended accounts in retrospect: an analysis of twitter spam

In this study, we examine the abuse of online social networks at the hands of spammers through the lens of the tools, techniques, and support infrastructure they rely upon. To perform our analysis, we identify over 1.1 million accounts suspended by Twitter for disruptive activities over the course of seven months. In the process, we collect a dataset of 1.8 billion tweets, 80 million of which belong to spam accounts. We use our dataset to characterize the behavior and lifetime of spam accounts, the campaigns they execute, and the wide-spread abuse of legitimate web services such as URL shorteners and free web hosting. We also identify an emerging marketplace of illegitimate programs operated by spammers that include Twitter account sellers, ad-based URL shorteners, and spam affiliate programs that help enable underground market diversification. Our results show that 77% of spam accounts identified by Twitter are suspended within on day of their first tweet. Because of these pressures, less than 9% of accounts form social relationships with regular Twitter users. Instead, 17% of accounts rely on hijacking trends, while 52% of accounts use unsolicited mentions to reach an audience. In spite of daily account attrition, we show how five spam campaigns controlling 145 thousand accounts combined are able to persist for months at a time, with each campaign enacting a unique spamming strategy. Surprisingly, three of these campaigns send spam directing visitors to reputable store fronts, blurring the line regarding what constitutes spam on social networks.

Design and Evaluation of a Real-Time URL Spam Filtering Service

On the heels of the widespread adoption of web services such as social networks and URL shorteners, scams, phishing, and malware have become regular threats. Despite extensive research, email-based spam filtering techniques generally fall short for protecting other web services. To better address this need, we present Monarch, a real-time system that crawls URLs as they are submitted to web services and determines whether the URLs direct to spam. We evaluate the viability of Monarch and the fundamental challenges that arise due to the diversity of web service spam. We show that Monarch can provide accurate, real-time protection, but that the underlying characteristics of spam do not generalize across web services. In particular, we find that spam targeting email qualitatively differs in significant ways from spam campaigns targeting Twitter. We explore the distinctions between email and Twitter spam, including the abuse of public web hosting and redirector services. Finally, we demonstrate Monarchs scalability, showing our system could protect a service such as Twitter -- which needs to process 15 million URLs/day -- for a bit under

Secure Web Browsing with the OP Web Browser

800/day.

Click Trajectories: End-to-End Analysis of the Spam Value Chain

Current Web browsers are plagued with vulnerabilities, providing hackers with easy access to computer systems via browser-based attacks. Browser security efforts that retrofit existing browsers have had limited success because the design of modern browsers is fundamentally flawed. To enable more secure web browsing, we design and implement a new browser, called the OP Web browser, that attempts to improve the state-of-the-art in browser security. Our overall design approach is to combine operating system design principles with formal methods to design a more secure Web browser by drawing on the expertise of both communities. Our overall design philosophy is to partition the browser into smaller subsystems and make all communication between subsystems simple and explicit. At the core of our design is a small browser kernel that manages the browser subsystems and interposes on all communications between them to enforce our new browser security features. To show the utility of our browser architecture, we design and implement three novel security features. First, we develop novel and flexible security policies that allows us to include plugins within our security framework. Our policy removes the burden of security from plugin writers, and gives plugins the flexibility to use innovative network architectures to deliver content while still maintaining the confidentiality and integrity of our browser, even if attackers compromise the plugin. Second, we use formal methods to prove that the address bar displayed within our browser user interface always shows the correct address for the current Web page. Third, we design and implement a browser-level information-flow tracking system to enable post-mortem analysis of browser-based attacks. If an attacker is able to compromise our browser, we highlight the subset of total activity that is causally related to the attack, thus allowing users and system administrators to determine easily which Web site lead to the compromise and to assess the damage of a successful attack. To evaluate our design, we implemented OP and tested both performance and filesystem impact. To test performance, we measure latency to verify OPs performance penalty from security features are be minimal from a users perspective. Our experiments show that on average the speed of the OP browser is comparable to Firefox and the audit log occupies around 80 KB per page on average.

Prudent Practices for Designing Malware Experiments: Status Quo and Outlook

Spam-based advertising is a business. While it has engendered both widespread antipathy and a multi-billion dollar anti-spam industry, it continues to exist because it fuels a profitable enterprise. We lack, however, a solid understanding of this enterprises full structure, and thus most anti-Spam interventions focus on only one facet of the overall spam value chain (e.g., spam filtering, URL blacklisting, site takedown).In this paper we present a holistic analysis that quantifies the full set of resources employed to monetize spam email -- including naming, hosting, payment and fulfillment -- usingextensive measurements of three months of diverse spam data, broad crawling of naming and hosting infrastructures, and over 100 purchases from spam-advertised sites. We relate these resources to the organizations who administer them and then use this data to characterize the relative prospects for defensive interventions at each link in the spam value chain. In particular, we provide the first strong evidence of payment bottlenecks in the spam value chain, 95% of spam-advertised pharmaceutical, replica and software products are monetized using merchant services from just a handful of banks.

unfriendly: multi-party privacy risks in social networks

Malware researchers rely on the observation of malicious code in execution to collect datasets for a wide array of experiments, including generation of detection models, study of longitudinal behavior, and validation of prior research. For such research to reflect prudent science, the work needs to address a number of concerns relating to the correct and representative use of the datasets, presentation of methodology in a fashion sufficiently transparent to enable reproducibility, and due consideration of the need not to harm others. In this paper we study the methodological rigor and prudence in 36 academic publications from 2006-2011 that rely on malware execution. 40% of these papers appeared in the 6 highest-ranked academic security conferences. We find frequent shortcomings, including problematic assumptions regarding the use of execution-driven datasets (25% of the papers), absence of description of security precautions taken during experiments (71% of the articles), and oftentimes insufficient description of the experimental setup. Deficiencies occur in top-tier venues and elsewhere alike, highlighting a need for the community to improve its handling of malware datasets. In the hope of aiding authors, reviewers, and readers, we frame guidelines regarding transparency, realism, correctness, and safety for collecting and using malware datasets.

RINSE: The Real-Time Immersive Network Simulation Environment for Network Security Exercises

As the popularity of social networks expands, the information users expose to the public has potentially dangerous implications for individual privacy. While social networks allow users to restrict access to their personal data, there is currently no mechanism to enforce privacy concerns over content uploaded by other users. As group photos and stories are shared by friends and family, personal privacy goes beyond the discretion of what a user uploads about himself and becomes an issue of what every network participant reveals. In this paper, we examine how the lack of joint privacy controls over content can inadvertently reveal sensitive information about a user including preferences, relationships, conversations, and photos. Specifically, we analyze Facebook to identify scenarios where conflicting privacy settings between friends will reveal information that at least one user intended remain private. By aggregating the information exposed in this manner, we demonstrate how a users private attributes can be inferred from simply being listed as a friend or mentioned in a story. To mitigate this threat, we show how Facebooks privacy model can be adapted to enforce multi-party privacy. We present a proof of concept application built into Facebook that automatically ensures mutually acceptable privacy restrictions are enforced on group content.

Understanding the domain registration behavior of spammers

The RINSE simulator is being developed to support large-scale network security preparedness and training exercises, involving hundreds of players and a modeled network composed of hundreds of LANs. The simulator must be able to present a realistic rendering of network behavior as attacks are launched and players diagnose events and try counter measures to keep network services operating. We describe the architecture and function of RINSE and outline how techniques like multiresolution traffic modeling and new routing simulation methods are used to address the scalability challenges of this application. We also describe in more detail new work on CPU/memory models necessary for the exercise scenarios and a latency absorption technique that help when extending the range of client tools usable by the players.

Consequences of Connectivity: Characterizing Account Hijacking on Twitter

Spammers register a tremendous number of domains to evade blacklisting and takedown efforts. Current techniques to detect such domains rely on crawling spam URLs or monitoring lookup traffic. Such detection techniques are only effective after the spammers have already launched their campaigns, and thus these countermeasures may only come into play after the spammer has already reaped significant benefits from the dissemination of large volumes of spam. In this paper we examine the registration process of such domains, with a particular eye towards features that might indicate that a given domain likely has a malicious purpose at registration time, before it is ever used for an attack. Our assessment includes exploring the characteristics of registrars, domain life cycles, registration bursts, and naming patterns. By investigating zone changes from the .com TLD over a 5-month period, we discover that spammers employ bulk registration, that they often re-use domains previously registered by others, and that they tend to register and host their domains over a small set of registrars. Our findings suggest steps that registries or registrars could use to frustrate the efforts of miscreants to acquire domains in bulk, ultimately reducing their agility for mounting large-scale attacks.

Ad Injection at Scale: Assessing Deceptive Advertisement Modifications

In this study we expose the serious large-scale threat of criminal account hijacking and the resulting damage incurred by users and web services. We develop a system for detecting large-scale attacks on Twitter that identifies 14 million victims of compromise. We examine these accounts to track how attacks spread within social networks and to determine how criminals ultimately realize a profit from hijacked credentials. We find that compromise is a systemic threat, with victims spanning nascent, casual, and core users. Even brief compromises correlate with 21% of victims never returning to Twitter after the service wrests control of a victims account from criminals. Infections are dominated by social contagions---phishing and malware campaigns that spread along the social graph. These contagions mirror information diffusion and biological diseases, growing in virulence with the number of neighboring infections. Based on the severity of our findings, we argue that early outbreak detection that stems the spread of compromise in 24 hours can spare 70% of victims.