Fast and Precise Sanitizer Analysis with BEK
Back to Program
Web applications often use special string-manipulating sanitizers on untrusted user data, but it is difficult to reason manually about the behavior of these functions, leading to errors. For example, the Internet Explorer cross-site scripting filter turned out to transform some web pages without JavaScript into web pages with valid JavaScript, enabling attacks. In other cases, sanitizers may fail to commute, rendering one order of application safe and the other dangerous. BEK is a language and system for writing sanitizers that enables precise analysis of sanitizer behavior, including checking idempotence, commutativity, and equivalence. For example, BEK can determine if a target string, such as an entry on the XSS Cheat Sheet, is a valid output of a sanitizer. If so, our analysis synthesizes an input string that yields that target. Our language is expressive enough to capture real web sanitizers used in ASP.NET, the Internet Explorer XSS Filter, and the Google AutoEscape framework, which we demonstrate by porting these sanitizers to BEK. Our analyses use a novel symbolic finite automata representation to leverage fast satisfiability modulo theories (SMT) solvers and are quick in practice, taking fewer than two seconds to check the commutativity of the entire set of Internet Exporer XSS filters, between 36 and 39 seconds to check implementations of HTMLEncode against target strings from the XSS Cheat Sheet, and less than ten seconds to check equivalence between all pairs of a set of implementations of HTMLEncode. Programs written in BEK can be compiled to traditional languages such as JavaScript and C#, making it possible for web developers to write sanitizers supported by deep analysis, yet deploy the analyzed code directly to real applications.

Toward Secure Embedded Web Interfaces
Back to Program
We address the challenge of building secure embedded web interfaces by proposing WebDroid: the first framework specifically dedicated to this purpose. Our design extends the Android Framework, and enables developers to create easily secure web interfaces for their applications. To motivate our work, we perform an in-depth study of the security of web interfaces embedded in consumer electronics devices, uncover significant vulnerabilities in all the devices examined, and categorize the vulnerabilities. We demonstrate how our framework's security mechanisms prevent embedded applications from suffering the vulnerabilities exposed by our audit. Finally we evaluate the efficiency of our framework in terms of performance and security.

ZOZZLE: Fast and Precise In-Browser JavaScript Malware Detection
Back to Program
JavaScript malware-based attacks account for a large fraction of successful mass-scale exploitation happening today. Attackers like JavaScript-based attacks because they can be mounted against an unsuspecting user visiting a seemingly innocent web page. While several techniques for addressing these types of exploits have been proposed, in-browser adoption has been slow, in part because of the performance overhead these methods incur. In this paper, we propose ZOZZLE, a low-overhead solution for detecting and preventing JavaScript malware that is fast enough to be deployed in the browser. Our approach uses Bayesian classification of hierarchical features of the JavaScript abstract syntax tree to identify syntax elements that are highly predictive of malware. Our experimental evaluation shows that ZOZZLE is able to detect JavaScript malware through mostly static code analysis effectively. ZOZZLE has an extremely low false positive rate of 0.0003%, which is less than one in a quarter million. Despite this high accuracy, the ZOZZLE classifier is fast, with a throughput of over one megabyte of JavaScript code per second.

Why (Special Agent) Johnny (Still) Can't Encrypt: A Security Analysis of the APCO Project 25 Two-Way Radio System
Back to Program
APCO Project 25 ("P25") is a suite of wireless communications protocols used in the US and elsewhere for public safety two-way (voice) radio systems. The protocols include security options in which voice and data traffic can be cryptographically protected from eavesdropping. This paper analyzes the security of P25 systems against both passive and active adversaries. We found a number of protocol, implementation, and user interface weaknesses that routinely leak information to a passive eavesdropper or that permit highly efficient and difficult to detect active attacks. We introduce new selective subframe jamming attacks against P25, in which an active attacker with very modest resources can prevent specific kinds of traffic (such as encrypted messages) from being received, while emitting only a small fraction of the aggregate power of the legitimate transmitter. We also found that even the passive attacks represent a serious practical threat. In a study we conducted over a two year period in several US metropolitan areas, we found that a significant fraction of the "encrypted" P25 tactical radio traffic sent by federal law enforcement surveillance operatives is actually sent in the clear, in spite of their users' belief that they are encrypted, and often reveals such sensitive data as the names of informants in criminal investigations.

Dark Clouds on the Horizon: Using Cloud Storage as Attack Vector and Online Slack Space
Back to Program
During the past few years, a vast number of online file storage services have been introduced. While several of these services provide basic functionality such as uploading and retrieving files by a specific user, more advanced services offer features such as shared folders, real-time collaboration, minimization of data transfers or unlimited storage space. Within this paper we give an overview of existing file storage services and examine Dropbox, an advanced file storage solution, in depth. We analyze the Dropbox client software as well as its transmission protocol, show weaknesses and outline possible attack vectors against users. Based on our results we show that Dropbox is used to store copyright-protected files from a popular filesharing network. Furthermore Dropbox can be exploited to hide files in the cloud with unlimited storage capacity. We define this as online slack space. We conclude by discussing security improvements for modern online storage services in general, and Dropbox in particular. To prevent our attacks cloud storage operators should employ data possession proofs on clients, a technique which has been recently discussed only in the context of assessing trust in cloud storage operators.

Comprehensive Experimental Analyses of Automotive Attack Surfaces
Back to Program
Modern automobiles are pervasively computerized, and hence potentially vulnerable to attack. However, while previous research has shown that the internal networks within some modern cars are insecure, the associated threat model — requiring prior physical access — has justifiably been viewed as unrealistic. Thus, it remains an open question if automobiles can also be susceptible to remote compromise. Our work seeks to put this question to rest by systematically analyzing the external attack surface of a modern automobile. We discover that remote exploitation is feasible via a broad range of attack vectors (including mechanics tools, CD players, Bluetooth and cellular radio), and further, that wireless communications channels allow long distance vehicle control, location tracking, in-cabin audio exfiltration and theft. Finally, we discuss the structural characteristics of the automotive ecosystem that give rise to such problems and highlight the practical challenges in mitigating them.

Forensic Triage for Mobile Phones with DEC0DE
Back to Program
We present DEC0DE, a system for recovering information from phones with unknown storage formats, a critical problem for forensic triage. Because phones have myriad custom hardware and software, we examine only the stored data. Via flexible descriptions of typical data structures, and using a classic dynamic programming algorithm, we are able to identify call logs and address book entries in phones across varied models and manufacturers. We designed DEC0DE by examining the formats of one set of phone models, and we evaluate its performance on other models. Overall, we are able to obtain high performance for these unexamined models: an average recall of 97% and precision of 80% for call logs; and average recall of 93% and precision of 52% for address books. Moreover, at the expense of recall dropping to 14%, we can increase precision of address book recovery to 94% by culling results that don't match between call logs and address book entries on the same phone.

mCarve: Carving Attributed Dump Sets
Back to Program
Carving is a common technique in digital forensics to recover data from a memory dump of a device. In contrast to existing approaches, we investigate the carving problem for sets of memory dumps. Such a set can, for instance, be obtained by dumping the memory of a number of smart cards or by regularly dumping the memory of a single smart card during its lifetime. The problem that we define and investigate is to determine at which location in the dumps certain attributes are stored. By studying the commonalities and dissimilarities of these dumps, one can significantly reduce the collection of possible locations for such attributes. We develop algorithms that support in this process, implement them in a prototype, and apply this prototype to reverse engineer the data structure of a public transportation card.

SHELLOS: Enabling Fast Detection and Forensic Analysis of Code Injection Attacks
Back to Program
The availability of off-the-shelf exploitation toolkits for compromising hosts, coupled with the rapid rate of exploit discovery and disclosure, has made exploit or vulnerability-based detection far less effective than it once was. For instance, the increasing use of metamorphic and polymorphic techniques to deploy code injection attacks continues to confound signature-based detection techniques. The key to detecting these attacks lies in the ability to discover the presence of the injected code (or, shellcode). One promising technique for doing so is to examine data (be that from network streams or buffers of a process) and efficiently execute its content to find what lurks within. Unfortunately, current approaches for achieving this goal are not robust to evasion or scalable, primarily because of their reliance on software-based CPU emulators. In this paper, we argue that the use of software-based emulation techniques are not necessary, and instead propose a new framework that leverages hardware virtualization to better enable the detection of code injection attacks. We also report on our experience using this framework to analyze a corpus of malicious Portable Document Format (PDF) files and network-based attacks.

MACE: Model-inference-Assisted Concolic Exploration for Protocol and Vulnerability Discovery
Back to Program
Program state-space exploration is central to software security, testing, and verification. In this paper, we propose a novel technique for state-space exploration of software that maintains an ongoing interaction with its environment. Our technique uses a combination of symbolic and concrete execution to build an abstract model of the analyzed application, in the form of a finite-state automaton, and uses the model to guide further state-space exploration. Through exploration, MACE further refines the abstract model. Using the abstract model as a scaffold, our technique wields more control over the search process. In particular: (1) shifting search to different parts of the search-space becomes easier, resulting in higher code coverage, and (2) the search is less likely to get stuck in small local state-subspaces (e.g., loops) irrelevant to the application's interaction with the environment. Preliminary experimental results show significant increases in the code coverage and exploration depth. Further, our approach found a number of new deep vulnerabilities.

Static Detection of Access Control Vulnerabilities in Web Applications
Back to Program
Access control vulnerabilities, which cause privilege escalations, are among the most dangerous vulnerabilities in web applications. Unfortunately, due to the difficulty in designing and implementing perfect access checks, web applications often fall victim to access control attacks. In contrast to traditional injection flaws, access control vulnerabilities are application-specific, rendering it challenging to obtain precise specifications for static and runtime enforcement. On one hand, writing specifications manually is tedious and time-consuming, which leads to non-existent, incomplete or erroneous specifications. On the other hand, automatic probabilistic-based specification inference is imprecise and computationally expensive in general. This paper describes the first static analysis that automatically detects access control vulnerabilities in web applications. The core of the analysis is a technique that statically infers and enforces implicit access control assumptions. Our insight is that source code implicitly documents intended accesses of each role and any successful forced browsing to a privileged page is likely a vulnerability. Based on this observation, our static analysis constructs sitemaps for different roles in a web application, compares per-role sitemaps to find privileged pages, and checks whether forced browsing is successful for each privileged page. We implemented our analysis and evaluated our tool on several real-world web applications. The evaluation results show that our tool is scalable and detects both known and new access control vulnerabilities with few false positives.

ADsafety: Type-Based Verification of JavaScript Sandboxing
Back to Program
Web sites routinely incorporate JavaScript programs from several sources into a single page. These sources must be protected from one another, which requires robust sandboxing. The many entry-points of sandboxes and the subtleties of JavaScript demand robust verification of the actual sandbox source. We use a novel type system for JavaScript to encode and verify sandboxing properties. The resulting verifier is lightweight and efficient, and operates on actual source. We demonstrate the effectiveness of our technique by applying it to ADsafe, which revealed several bugs and other weaknesses.

Measuring Pay-per-Install: The Commoditization of Malware Distribution
Back to Program
Recent years have seen extensive diversification of the "underground economy" associated with malware and the subversion of Internet-connected systems. This trend towards specialization has compelling forces driving it: miscreants readily apprehend that tackling the entire value-chain from malware creation to monetization in the presence of ever-evolving countermeasures poses a daunting task requiring highly developed skills and resources. As a result, entrepreneurial-minded miscreants have formed pay-per-install (PPI) services—specialized organizations that focus on the infection of victims' systems. In this work we perform a measurement study of the PPI market by infiltrating four PPI services. We develop infrastructure that enables us to interact with PPI services and gather and classify the resulting malware executables distributed by the services. Using our infrastructure, we harvested over a million client executables using vantage points spread across 15 countries. We find that of the world's top 20 most prevalent families of malware, 12 employ PPI services to buy infections. In addition we analyze the targeting of specific countries by PPI clients, the repacking of executables to evade detection, and the duration of malware distribution.

Dirty Jobs: The Role of Freelance Labor in Web Service Abuse
Back to Program
Modern Web services inevitably engender abuse, as attackers find ways to exploit a service and its user base. However, while defending against such abuse is generally considered a technical endeavor, we argue that there is an increasing role played by human labor markets. Using over seven years of data from the popular crowd-sourcing site Freelancer.com, as well data from our own active job solicitations, we characterize the labor market involved in service abuse. We identify the largest classes of abuse work, including account creation, social networking link generation and search engine optimization support, and characterize how pricing and demand have evolved in supporting this activity.

Show Me the Money: Characterizing Spam-advertised Revenue
Back to Program
Modern spam is ultimately driven by product sales: goods purchased by customers online. However, while this model is easy to state in the abstract, our understanding of the concrete business environment—how many orders, of what kind, from which customers, for how much—is poor at best. This situation is unsurprising since such sellers typically operate under questionable legal footing, with "ground truth" data rarely available to the public. However, absent quantifiable empirical data, "guesstimates" operate unchecked and can distort both policy making and our choice of appropriate interventions. In this paper, we describe two inference techniques for peering inside the business operations of spam-advertised enterprises: purchase pair and basket inference. Using these, we provide informed estimates on order volumes, product sales distribution, customer makeup and total revenues for a range of spam-advertised programs.

Secure In-Band Wireless Pairing
Back to Program
This paper presents the first wireless pairing protocol that works in-band, with no pre-shared keys, and protects against MITM attacks. The main innovation is a new key exchange message constructed in a manner that ensures an adversary can neither hide the fact that a message was transmitted, nor alter its payload without being detected. Thus, any attempt by an adversary to interfere with the key exchange translates into the pairing devices detecting either invalid pairing messages or an unacceptable increase in the number of such messages. We analytically prove that our design is secure against MITM attacks, and show that our protocol is practical by implementing a prototype using off-the-shelf 802.11 cards. An evaluation of our protocol on two busy wireless networks (MIT's campus network and a reproduction of the SIGCOMM 2010 network using traces) shows that it can effectively implement key exchange in a real-world environment.

TRESOR Runs Encryption Securely Outside RAM
Back to Program
Current disk encryption techniques store necessary keys in RAM and are therefore susceptible to attacks that target volatile memory, such as Firewire and cold boot attacks. We present TRESOR, a Linux kernel patch that implements the AES encryption algorithm and its key management solely on the microprocessor. Instead of using RAM, TRESOR ensures that all encryption states as well as the secret key and any part of it are only stored in processor registers throughout the operational time of the system, thereby substantially increasing its security. Our solution takes advantage of Intel's new AES-NI instruction set and exploits the x86 debug registers in a non-standard way, namely as cryptographic key storage. TRESOR is compatible with all modern Linux distributions, and its performance is on a par with that of standard AES implementations.

Bubble Trouble: Off-Line De-Anonymization of Bubble Forms
Back to Program
Fill-in-the-bubble forms are widely used for surveys, election ballots, and standardized tests. In these and other scenarios, use of the forms comes with an implicit assumption that individuals' bubble markings themselves are not identifying. This work challenges this assumption, demonstrating that fill-in-the-bubble forms could convey a respondent's identity even in the absence of explicit identifying information. We develop methods to capture the unique features of a marked bubble and use machine learning to isolate characteristics indicative of its creator. Using surveys from more than ninety individuals, we apply these techniques and successfully re-identify individuals from markings alone with over 50% accuracy. This bubble-based analysis can have either positive or negative implications depending on the application. Potential applications range from detection of cheating on standardized tests to attacks on the secrecy of election ballots. To protect against negative consequences, we discuss mitigation techniques to remove a bubble's identifying characteristics. We suggest additional tests using longitudinal data and larger datasets to further explore the potential of our approach in real-world applications.

Measuring and Analyzing Search-Redirection Attacks in the Illicit Online Prescription Drug Trade
Back to Program
We investigate the manipulation of web search results to promote the unauthorized sale of prescription drugs. We focus on search-redirection attacks, where miscreants compromise high-ranking websites and dynamically redirect traffic to different pharmacies based upon the particular search terms issued by the consumer. We constructed a representative list of 218 drug-related queries and automatically gathered the search results on a daily basis over nine months in 2010-2011. We find that about one third of all search results are one of over 7000 infected hosts triggered to redirect to a few hundred pharmacy websites. Legitimate pharmacies and health resources have been largely crowded out by search-redirection attacks and blog spam. Infections persist longest on websites with high PageRank and from .edu domains. 96% of infected domains are connected through traffic redirection chains, and network analysis reveals that a few concentrated communities link many otherwise disparate pharmacies together. We calculate that the conversion rate of web searches into sales lies between 0.3% and 3%, and that more illegal drugs sales are facilitated by search-redirection attacks than by email spam. Finally, we observe that concentration in both the source infections and redirectors presents an opportunity for defenders to disrupt online pharmacy sales.

deSEO: Combating Search-Result Poisoning
Back to Program
We perform an in-depth study of SEO attacks that spread malware by poisoning search results for popular queries. Such attacks, although recent, appear to be both widespread and effective. They compromise legitimate Web sites and generate a large number of fake pages targeting trendy keywords. We first dissect one example attack that affects over 5,000 Web domains and attracts over 81,000 user visits. Further, we develop deSEO, a system that automatically detects these attacks. Using large datasets with hundreds of billions of URLs, deSEO successfully identifies multiple malicious SEO campaigns. In particular, applying the URL signatures derived from deSEO, we find 36% of sampled searches to Google and Bing contain at least one malicious link in the top results at the time of our experiment.

A Study of Android Application Security
Back to Program
The fluidity of application markets complicate smartphone security. Although recent efforts have shed light on particular security issues, there remains little insight into broader security characteristics of smartphone applications. This paper seeks to better understand smartphone application security by studying 1,100 popular free Android applications. We introduce the ded decompiler, which recovers Android application source code directly from its installation image. We design and execute a horizontal study of smartphone applications based on static analysis of 21 million lines of recovered code. Our analysis uncovered pervasive use/misuse of personal/phone identifiers, and deep penetration of advertising and analytics networks. However, we did not find evidence of malware or exploitable vulnerabilities in the studied applications. We conclude by considering the implications of these preliminary findings and offer directions for future analysis.

Permission Re-Delegation: Attacks and Defenses
Back to Program
Modern browsers and smartphone operating systems treat applications as mutually untrusting, potentially malicious principals. Applications are (1) isolated except for explicit IPC or inter-application communication channels and (2) unprivileged by default, requiring user permission for additional privileges. Although inter-application communication supports useful collaboration, it also introduces the risk of permission re-delegation. Permission re-delegation occurs when an application with permissions performs a privileged task for an application without permissions. This undermines the requirement that the user approve each application's access to privileged devices and data. We discuss permission re-delegation and demonstrate its risk by launching real-world attacks on Android system applications; several of the vulnerabilities have been confirmed as bugs. We discuss possible ways to address permission re-delegation and present IPC Inspection, a new OS mechanism for defending against permission re-delegation. IPC Inspection prevents opportunities for permission re-delegation by reducing an application's permissions after it receives communication from a less privileged application. We have implemented IPC Inspection for a browser and Android, and we show that it prevents the attacks we found in the Android system applications.

QUIRE: Lightweight Provenance for Smart Phone Operating Systems
Back to Program
Smartphone apps are often granted to privilege to run with access to the network and sensitive local resources. This makes it difficult for remote endpoints to place any trust in the provenance of network connections originating from a user's device. Even on the phone, different apps with distinct privilege sets can communicate with one another. This can allow one app to trick another into improperly exercising its privileges (resulting in a confused deputy attack). In QUIRE, we engineered two new security mechanisms into Android to address these issues. First, QUIRE tracks the call chain of on device IPCs, allowing an app the choice of operating with the reduced privileges of its callers or exercising its full privilege set by acting explicitly on its own behalf. Second, a lightweight signature scheme allows any app to create a signed statement that can be verified by any app on the same phone. Both of these mechanisms are reflected in network RPCs. This allows remote systems visibility into the state of the phone when the RPC was made. We demonstrate the usefulness of QUIRE with two example applications: an advertising service that runs advertisements separately from their hosting applications, and a remote payment system. We show that QUIRE's performance overhead is minimal.

SMS of Death: From Analyzing to Attacking Mobile Phones on a Large Scale
Back to Program
Mobile communication is an essential part of our daily lives. Therefore, it needs to be secure and reliable. In this paper, we study the security of feature phones, the most common type of mobile phone in the world. We built a framework to analyze the security of SMS clients of feature phones. The framework is based on a small GSM base station, which is readily available on the market. Through our analysis we discovered vulnerabilities in the feature phone platforms of all major manufacturers. Using these vulnerabilities we designed attacks against end-users as well as mobile operators. The threat is serious since the attacks can be used to prohibit communication on a large scale and can be carried out from anywhere in the world. Through further analysis we determined that such attacks are amplified by certain configurations of the mobile network. We conclude our research by providing a set of countermeasures.

Q: Exploit Hardening Made Easy
Back to Program
Prior work has shown that return oriented programming (ROP) can be used to bypass W⊕X, a software defense that stops shellcode, by reusing instructions from large libraries such as libc. Modern operating systems have since enabled address randomization (ASLR), which randomizes the location of libc, making these techniques unusable in practice. However, modern ASLR implementations leave smaller amounts of executable code unrandomized and it has been unclear whether an attacker can use these small code fragments to construct payloads in the general case. In this paper, we show defenses as currently deployed can be bypassed with new techniques for automatically creating ROP payloads from small amounts of unrandomized code. We propose using semantic program verification techniques for identifying the functionality of gadgets, and design a ROP compiler that is resistant to missing gadget types. To demonstrate our techniques, we build Q, an end-to-end system that automatically generates ROP payloads for a given binary. Q can produce payloads for 80% of Linux /usr/bin programs larger than 20KB. We also show that Q can automatically perform exploit hardening: given an exploit that crashes with defenses on, Q outputs an exploit that bypasses both W⊕X and ASLR. We show that Q can harden nine real-world Linux and Windows exploits, enabling an attacker to automatically bypass defenses as deployed by industry for those programs.

Cloaking Malware with the Trusted Platform Module
Back to Program
The Trusted Platform Module (TPM) is commonly thought of as hardware that can increase platform security. However, it can also be used for malicious purposes. The TPM, along with other hardware, can implement a cloaked computation, whose memory state cannot be observed by any other software, including the operating system and hypervisor. We show that malware can use cloaked computations to hide essential secrets (like the target of an attack) from a malware analyst. We describe and implement a protocol that establishes an encryption key under control of the TPM that can only be used by a specific infection program. An infected host then proves the legitimacy of this key to a remote malware distribution platform, and receives and executes an encrypted payload in a way that prevents software visibility of the decrypted payload. We detail how malware can benefit from cloaked computations and discuss defenses against our protocol. Hardening legitimate uses of the TPM against attack improves the resilience of our malware, creating a Catch-22 for secure computing technology.

Detecting Malware Domains at the Upper DNS Hierarchy
Back to Program
In recent years Internet miscreants have been leveraging the DNS to build malicious network infrastructures for malware command and control. In this paper we propose a novel detection system called Kopis for detecting malware-related domain names. Kopis passively monitors DNS traffic at the upper levels of the DNS hierarchy, and is able to accurately detect malware domains by analyzing global DNS query resolution patterns. Compared to previous DNS reputation systems such as Notos [3] and Exposure [4], which rely on monitoring traffic from local recursive DNS servers, Kopis offers a new vantage point and introduces new traffic features specifically chosen to leverage the global visibility obtained by monitoring network traffic at the upper DNS hierarchy. Unlike previous work Kopis enables DNS operators to independently (i.e., without the need of data from other networks) detect malware domains within their authority, so that action can be taken to stop the abuse. Moreover, unlike previous work, Kopis can detect malware domains even when no IP reputation information is available. We developed a proof-of-concept version of Kopis, and experimented with eight months of real-world data. Our experimental results show that Kopis can achieve high detection rates (e.g., 98.4%) and low false positive rates (e.g., 0.3% or 0.5%). In addition Kopis is able to detect new malware domains days or even weeks before they appear in public blacklists and security forums, and allowed us to discover the rise of a previously unknown DDoS botnet based in China.

BOTMAGNIFIER: Locating Spambots on the Internet
Back to Program
Unsolicited bulk email (spam) is used by cybercriminals to lure users into scams and to spread malware infections. Most of these unwanted messages are sent by spam botnets, which are networks of compromised machines under the control of a single (malicious) entity. Often, these botnets are rented out to particular groups to carry out spam campaigns, in which similar mail messages are sent to a large group of Internet users in a short amount of time. Tracking the bot-infected hosts that participate in spam campaigns, and attributing these hosts to spam botnets that are active on the Internet, are challenging but important tasks. In particular, this information can improve blacklist-based spam defenses and guide botnet mitigation efforts. In this paper, we present a novel technique to support the identification and tracking of bots that send spam. Our technique takes as input an initial set of IP addresses that are known to be associated with spam bots, and learns their spamming behavior. This initial set is then "magnified" by analyzing large-scale mail delivery logs to identify other hosts on the Internet whose behavior is similar to the behavior previously modeled. We implemented our technique in a tool, called BOTMAGNIFIER, and applied it to several data streams related to the delivery of email traffic. Our results show that it is possible to identify and track a substantial number of spam bots by using our magnification technique. We also perform attribution of the identified spam hosts and track the evolution and activity of well-known spamming botnets over time. Moreover, we show that our results can help to improve state-of-the-art spam blacklists.

JACKSTRAWS: Picking Command and Control Connections from Bot Traffic
Back to Program
A distinguishing characteristic of bots is their ability to establish a command and control (C&C) channel. The typical approach to build detection models for C&C traffic and to identify C&C endpoints (IP addresses and domains of C&C servers) is to execute a bot in a controlled environment and monitor its outgoing network connections. Using the bot traffic, one can then craft signatures that match C&C connections or blacklist the IP addresses or domains that the packets are sent to. Unfortunately, this process is not as easy as it seems. For example, bots often open a large number of additional connections to legitimate sites (to perform click fraud or query for the current time), and bots can deliberately produce "noise" — bogus connections that make the analysis more difficult. Thus, before one can build a model for C&C traffic or blacklist IP addresses and domains, one first has to pick the C&C connections among all the network traffic that a bot produces. In this paper, we present JACKSTRAWS, a system that accurately identifies C&C connections. To this end, we leverage host-based information that provides insights into which data is sent over each network connection as well as the ways in which a bot processes the information that it receives. More precisely, we associate with each network connection a behavior graph that captures the system calls that lead to this connection, as well as the system calls that operate on data that is returned. By using machine learning techniques and a training set of graphs that are associated with known C&C connections, we automatically extract and generalize graph templates that capture the core of different types of C&C activity. Later, we use these C&C templates to match against behavior graphs produced by other bots. Our results show that JACKSTRAWS can accurately detect C&C connections, even for novel bot families that were not used for template generation.

Telex: Anticensorship in the Network Infrastructure
Back to Program
In this paper, we present Telex, a new approach to resisting state-level Internet censorship. Rather than attempting to win the cat-and-mouse game of finding open proxies, we leverage censors' unwillingness to completely block day-to-day Internet access. In effect, Telex converts innocuous, unblocked websites into proxies, without their explicit collaboration. We envision that friendly ISPs would deploy Telex stations on paths between censors' networks and popular, uncensored Internet destinations. Telex stations would monitor seemingly innocuous flows for a special "tag" and transparently divert them to a forbidden website or service instead. We propose a new cryptographic scheme based on elliptic curves for tagging TLS handshakes such that the tag is visible to a Telex station but not to a censor. In addition, we use our tagging scheme to build a protocol that allows clients to connect to Telex stations while resisting both passive and active attacks. We also present a proof-of-concept implementation that demonstrates the feasibility of our system.

PIR-Tor: Scalable Anonymous Communication Using Private Information Retrieval
Back to Program
Existing anonymous communication systems like Tor do not scale well as they require all users to maintain up-to-date information about all available Tor relays in the system. Current proposals for scaling anonymous communication advocate a peer-to-peer (P2P) approach. While the P2P paradigm scales to millions of nodes, it provides new opportunities to compromise anonymity. In this paper, we step away from the P2P paradigm and advocate a client-server approach to scalable anonymity. We propose PIR-Tor, an architecture for the Tor network in which users obtain information about only a few onion routers using private information retrieval techniques. Obtaining information about only a few onion routers is the key to the scalability of our approach, while the use of private retrieval information techniques helps preserve client anonymity. The security of our architecture depends on the security of PIR schemes which are well understood and relatively easy to analyze, as opposed to peer-to-peer designs that require analyzing extremely complex and dynamic systems. In particular, we demonstrate that reasonable parameters of our architecture provide equivalent security to that of the Tor network. Moreover, our experimental results show that the overhead of PIR-Tor is manageable even when the Tor network scales by two orders of magnitude.

The Phantom Tollbooth: Privacy-Preserving Electronic Toll Collection in the Presence of Driver Collusion
Back to Program
In recent years, privacy-preserving toll collection has been proposed as a way to resolve the tension between the desire for sophisticated road pricing schemes and drivers' interest in maintaining the privacy of their driving patterns. Two recent systems in particular, VPriv (USENIX Security 2009) and PrETP (USENIX Security 2010), use modern cryptographic primitives to solve this problem. In order to keep drivers honest in paying for their usage of the roads, both systems rely on unpredictable spot checks (e.g., by hidden roadside cameras or roaming police vehicles) to catch potentially cheating drivers. In this paper we identify large-scale driver collusion as a threat to the necessary unpredictability of these spot checks. Most directly, the VPriv and PrETP audit protocols both reveal to drivers the locations of spot-check cameras — information that colluding drivers can then use to avoid paying road fees. We describe Milo, a new privacy-preserving toll collection system based on PrETP, whose audit protocol does not have this information leak, even when drivers misbehave and collude. We then evaluate the additional cost of Milo and find that, when compared to naïve methods to protect against cheating drivers, Milo offers a significantly more cost-effective approach.

Differential Privacy Under Fire
Back to Program
Anonymizing private data before release is not enough to reliably protect privacy, as Netflix and AOL have learned to their cost. Recent research on differential privacy opens a way to obtain robust, provable privacy guarantees, and systems like PINQ and Airavat now offer convenient frameworks for processing arbitrary user-specified queries in a differentially private way. However, these systems are vulnerable to a variety of covert-channel attacks that can be exploited by an adversarial querier. We describe several different kinds of attacks, all feasible in PINQ and some in Airavat. We discuss the space of possible countermeasures, and we present a detailed design for one specific solution, based on a new primitive we call predictable transactions and a simple differentially private programming language. Our evaluation, which relies on a proof-of-concept implementation based on the Caml Light runtime, shows that our design is effective against remotely exploitable covert channels, at the expense of a higher query completion time.

Outsourcing the Decryption of ABE Ciphertexts
Back to Program
Attribute-based encryption (ABE) is a new vision for public key encryption that allows users to encrypt and decrypt messages based on user attributes. For example, a user can create a ciphertext that can be decrypted only by other users with attributes satisfying ("Faculty" OR ("PhD Student" AND "Quals Completed")). Given its expressiveness, ABE is currently being considered for many cloud storage and computing applications. However, one of the main efficiency drawbacks of ABE is that the size of the ciphertext and the time required to decrypt it grows with the complexity of the access formula. In this work, we propose a new paradigm for ABE that largely eliminates this overhead for users. Suppose that ABE ciphertexts are stored in the cloud. We show how a user can provide the cloud with a single transformation key that allows the cloud to translate any ABE ciphertext satisfied by that user's attributes into a (constant-size) El Gamal-style ciphertext, without the cloud being able to read any part of the user's messages. To precisely define and demonstrate the advantages of this approach, we provide new security definitions for both CPA and replayable CCA security with outsourcing, several new constructions, an implementation of our algorithms and detailed performance measurements. In a typical configuration, the user saves significantly on both bandwidth and decryption time, without increasing the number of transmissions.

Faster Secure Two-Party Computation Using Garbled Circuits
Back to Program
Secure two-party computation enables two parties to evaluate a function cooperatively without revealing to either party anything beyond the function's output. The garbled-circuit technique, a generic approach to secure two-party computation for semi-honest participants, was developed by Yao in the 1980s, but has been viewed as being of limited practical significance due to its inefficiency. We demonstrate several techniques for improving the running time and memory requirements of the garbled-circuit technique, resulting in an implementation of generic secure two-party computation that is significantly faster than any previously reported while also scaling to arbitrarily large circuits. We validate our approach by demonstrating secure computation of circuits with over 10⁹ gates at a rate of roughly 10 μs per garbled gate, and showing order-of-magnitude improvements over the best previous privacy-preserving protocols for computing Hamming distance, Levenshtein distance, Smith-Waterman genome alignment, and AES.