Matching the profiles of a user across multiple online social networks brings opportunities for new services and applications as well as new insights on user online behavior, yet it raises serious privacy concerns. Prior literature has showed that it is possible to accurately match profiles, but their evaluation focused only on sampled datasets. In this paper, we study the extent to which we can reliably match profiles in practice, across real-world social networks, by exploiting public attributes, i.e., information users publicly provide about themselves. Today's social networks have hundreds of millions of users, which brings completely new challenges as a reliable matching scheme must identify the correct matching profile out of the millions of possible profiles. We first define a set of properties for profile attributes--Availability, Consistency, non-Impersonability, and Discriminability (ACID)--that are both necessary and sufficient to determine the reliability of a matching scheme. Using these properties, we propose a method to evaluate the accuracy of matching schemes in real practical cases. Our results show that the accuracy in practice is significantly lower than the one reported in prior literature. When considering entire social networks, there is a non-negligible number of profiles that belong to different users but have similar attributes, which leads to many false matches. Our paper sheds light on the limits of matching profiles in the real world and illustrates the correct methodology to evaluate matching schemes in realistic scenarios.

Off-the-shelf intrusion detection systems prove an ill fit for protecting industrial control systems, as they do not take their process semantics into account. Specifically, current systems fail to detect recent process control attacks that manifest as unauthorized changes to the configuration of a plant's programmable logic controllers (PLCs). In this work we present a detector that continuously tracks updates to corresponding process variables to then derive variable-specific prediction models as the basis for assessing future activity. Taking a specification-agnostic approach, we passively monitor plant activity by extracting variable updates from the devices' network communication. We evaluate the capabilities of our detection approach with traffic recorded at two operational water treatment plants serving a total of about one million people in two urban areas. We show that the proposed approach can detect direct attacks on process control, and we further explore its potential to identify more sophisticated indirect attacks on field device measurements as well.

The ever-increasing sophistication in network attacks, combined with larger and larger volumes of traffic, presents a dual challenge to network intrusion detection systems (IDSs). On one hand, to take advantage of modern multi-core processing platforms IDSs need to support scalability, by distributing traffic analysis across a large number of processing units. On the other hand, such scalability must not come at the cost of decreased effectiveness in attack detection. In this paper, we present a novel domain-specific concurrency model that addresses this challenge by introducing the notion of detection scope: a unit for partitioning network traffic such that the traffic contained in each resulting "slice" is independent for detection purposes. The notion of scope enables IDSs to automatically distribute traffic processing, while ensuring that information necessary to detect intrusions remains available to detector instances. We show that for a large class of detection algorithms, scope can be automatically inferred via program analysis; and we present scheduling algorithms that ensure safe, scope-aware processing of network events. We evaluate our technique on a set of IDS analyses, showing that our approach can indeed exploit the concurrency inherent in network traffic to provide significant throughput improvements.

When developing networking systems such as firewalls, routers, and intrusion detection systems, one faces a striking gap between the ease with which one can often describe a desired analysis in high-level terms, and the tremendous amount of low-level implementation details that one must still grapple with to come to a robust solution. We present HILTI, a platform that bridges this divide by providing to application developers much of the low-level functionality, without tying it to a specific analysis structure. HILTI consists of two parts: (1) an abstract machine model that we tailor specifically to the networking domain, directly supporting the field's common abstractions and idioms in its instruction set; and (2) a compilation strategy for turning programs written for the abstract machine into optimized, natively executable code. We have developed a prototype of the HILTI compiler toolchain that fully implements the design's functionality, and ported exemplars of networking applications to the HILTI model to demonstrate the aptness of its abstractions. Our evaluation of HILTI's functionality and performance confirms its potential to become a powerful platform for future application development.

Much of the Internet's end-to-end security relies on the SSL/TLS protocol along with its underlying X.509 certificate infrastructure. However, the system remains quite brittle due to its liberal delegation of signing authority: a single compromised certification authority undermines trust globally. Several recent high-profile incidents have demonstrated this shortcoming convincingly. Over time, the security community has proposed a number of counter measures to increase the security of the certificate ecosystem; many of these efforts monitor for what they consider tell-tale signs of man-in-the-middle attacks. In this work we set out to understand to which degree benign changes to the certificate ecosystem share structural properties with attacks, based on a large-scale data set of more than 17 billion SSL sessions. We find that common intuition falls short in assessing the maliciousness of an unknown certificate, since their typical artifacts routinely occur in benign contexts as well. We also discuss what impact our observations have on proposals aiming to improve the security of the SSL ecosystem.

When browsers report TLS errors, they cannot distinguish between attacks and harmless server misconfigurations; hence they leave it to the user to decide whether continuing is safe. However, actual attacks remain rare. As a result, users quickly become used to "false positives" that deplete their attention span, making it unlikely that they will pay sufficient scrutiny when a real attack comes along. Consequently, browser vendors should aim to minimize the number of low-risk warnings they report. To guide that process, we perform a large-scale measurement study of common TLS warnings. Using a set of passive network monitors located at different sites, we identify the prevalence of warnings for a total population of about 300,000 users over a nine-month period. We identify low-risk scenarios that consume a large chunk of the user attention budget and make concrete recommendations to browser vendors that will help maintain user attention in high-risk situations. We study the impact on end users with a data set much larger in scale than the data sets used in previous TLS measurement studies. A key novelty of our approach involves the use of internal browser code instead of generic TLS libraries for analysis, providing more accurate and representative results.

We study how potential attackers can identify accounts on different social network sites that all belong to the same user, exploiting only innocuous activity that inherently comes with posted content. We examine three specific features on Yelp, Flickr, and Twitter: the geo-location attached to a user's posts, the timestamp of posts, and the user's writing style as captured by language models. We show that among these three features the location of posts is the most powerful feature to identify accounts that belong to the same user in different sites. When we combine all three features, the accuracy of identifying Twitter accounts that belong to a set of Flickr users is comparable to that of existing attacks that exploit usernames. Our attack can identify 37% more accounts than using usernames when we instead correlate Yelp and Twitter. Our results have significant privacy implications as they present a novel class of attacks that exploit users' tendency to assume that, if they maintain different personas with different names, the accounts cannot be linked together; whereas we show that the posts themselves can provide enough information to correlate the accounts.

User-supplied content--in the form of photos, videos, and text--is a crucial ingredient to many web sites and services today. However, many users who provide content do not realize that their uploads may be leaking personal information in forms hard to intuitively grasp. Correlation of seemingly innocuous information can create inference chains that tell much more about individuals than they are aware of revealing. We contend that adversaries can systematically exploit such relationships by correlating information from different sources in what we term global inference attacks: assembling a comprehensive understanding from individual pieces found at a variety of locations, Sherlock-style. Not only are such attacks already technically viable given the capabilities that today's multimedia content analysis and correlation technologies readily provide, but we also find business models that provide adversaries with powerful incentives for pursuing them.

In network intrusion detection research, one popular strategy for finding attacks is monitoring a network's activity for anomalies: deviations from profiles of normality previously learned from benign traffic, typically identified using tools borrowed from the machine learning community. However, despite extensive academic research one finds a striking gap in terms of actual deployments of such systems: compared with other intrusion detection approaches, machine learning is rarely employed in operational "real world" settings. We examine the differences between the network intrusion detection problem and other areas where machine learning regularly finds much more success. Our main claim is that the task of finding attacks is fundamentally different from these other applications, making it significantly harder for the intrusion detection community to employ machine learning effectively. We support this claim by identifying challenges particular to network intrusion detection, and provide a set of guidelines meant to strengthen future research on anomaly detection.

Abstract It is becoming increasingly difficult to implement effective systems for preventing network attacks, due to the combination of the rising sophistication of attacks requiring more complex analyses to detect; the relentless growth in the volume of network traffic that we must analyze; and, critically, the failure in recent years for uniprocessor performance to sustain the exponential gains that for so many years CPUs have enjoyed. For commodity hardware, tomorrow's performance gains will instead come frommulti‐corearchitectures in which a whole set of CPUs executes concurrently. Taking advantage of the full power of multi‐core processors for network intrusion prevention requires an in‐depth approach. In this work we frame an architecture customized for parallel execution of network attack analysis. At the lowest layer of the architecture is an ‘Active Network Interface’, a custom device based on an inexpensive FPGA platform. The analysis itself is structured as an event‐based system, which allows us to find many opportunities for concurrent execution, since events introduce a natural asynchrony into the analysis while still maintaining good cache locality. A preliminary evaluation demonstrates the potential of this architecture. Copyright © 2009 John Wiley & Sons, Ltd.

In many situations it can be enormously helpful to archive the raw contents of a network traffic stream to disk, to enable later inspection of activity that becomes interesting only in retrospect. We present a Time Machine (TM) for network traffic that provides such a capability. The TM leverages the heavy-tailed nature of network flows to capture nearly all of the likely-interesting traffic while storing only a small fraction of the total volume. An initial proof-of-principle prototype established the forensic value of such an approach, contributing to the investigation of numerous attacks at a site with thousands of users. Based on these experiences, a rearchitected implementation of the system provides flexible, highperformance traffic stream capture, indexing and retrieval, including an interface between the TM and a real-time network intrusion detection system (NIDS). The NIDS controls the TM by dynamically adjusting recording parameters, instructing it to permanently store suspicious activity for offline forensics, and fetching traffic from the past for retrospective analysis. We present a detailed performance evaluation of both stand-alone and joint setups, and report on experiences with running the system live in high-volume environments.

When installing network intrusion detection systems (NIDSs), operators are faced with a large number of parameters and analysis options for tuning trade-offs between detection accuracy versus resource requirements. In this work we set out to assist this process by understanding and predicting the CPU and memory consumption of such systems.

A key step in the semantic analysis of network traffic is to parse the traffic stream according to the high-level protocols it contains. This process transforms raw bytes into structured, typed, and semantically meaningful data fields that provide a high-level representation of the traffic. However, constructing protocol parsers by hand is a tedious and error-prone affair due to the complexity and sheer number of application protocols.This paper presents binpac, a declarative language and compiler designed to simplify the task of constructing robust and efficient semantic analyzers for complex network protocols. We discuss the design of the binpac language and a range of issues in generating efficient parsers from high-level specifications. We have used binpac to build several protocol parsers for the "Bro" network intrusion detection system, replacing some of its existing analyzers (handcrafted in C++), and supplementing its operation with analyzers for new protocols. We can then use Bro's powerful scripting language to express application-level analysis of network traffic in high-level terms that are both concise and expressive. binpac is now part of the open-source Bro distribution.

Network intrusion detection systems (NIDSs) critically rely on processing a great deal of state. Often much of this state resides solely in the volatile processor memory accessible to a single user-level process on a single machine. In this work, we highlight the power of independent state, i.e., internal fine-grained state that can be propagated from one instance of a NIDS to others running either concurrently or subsequently. Independent state provides us with a wealth of possible applications that hold promise for enhancing the capabilities of NIDSs. We discuss an implementation of independent state for the Bro NIDS and examine how we can then leverage independent state for distributed processing, load parallelization, selective preservation of state across restarts and crashes, dynamic reconfiguration, high level policy maintenance, and support for profiling and debugging. We have experimented with each of these applications in several large environments and are now working to integrate them into the sites' operational monitoring. A performance evaluation shows that our implementation is suitable for use even in large scale environments

... (NIDSs) face extreme challenges with respect to traffic volume, traffic diversity, and resource management. While crucial for acceptance and operational deployment, the research literature mainly omits such practical difficulties. In this paper, we offer an evaluation based on extensive operational experience. More specifically, we identify and explore key factors with respect to resource management and efficient packet processing and highlight their impact using a set of real-world traces. On the one hand, these insights help us gauge the trade-offs of tuning a NIDS. On the other hand, they motivate us to explore several novel ways of reducing resource requirements. These enable us to improve the state management considerably as well as balance the processing load dynamically. Overall this enables us to operate a NIDS successfully in our highvolume network environments.

Evaluating network components such as network intrusion detection systems, firewalls, routers, or switches suffers from the lack of available network traffic traces that on the one hand are appropriate for a specific test environment but on the other hand have the same characteristics as actual traffic. Instead of just capturing traffic and replaying the trace, we identify a set of packet trace manipulation operations that enable us to generate a trace bottom-up: our trace primitives can be traces from different environments or artificially generated ones; our basic operations include merging of two traces, moving a flow across time, duplicating a flow, and stretching a flow's time-scale. After discussing the potential as ell as the dangers of each operation with respect to analysis at different protocol layers, we present a framework within which these operations can be realized and show an example configuration for our prototype.

Many network intrusion detection systems (NIDS) use byte sequences as signatures to detect malicious activity. While being highly efficient, they tend to suffer from a high false-positive rate. We develop the concept of contextual signatures as an improvement of string-based signature-matching. Rather than matching fixed strings in isolation, we augment the matching process with additional context. When designing an efficient signature engine for the NIDS bro, we provide low-level context by using regular expressions for matching, and high-level context by taking advantage of the semantic information made available by bro's protocol analysis and scripting language. Therewith, we greatly enhance the signature's expressiveness and hence the ability to reduce false positives. We present several examples such as matching requests with replies, using knowledge of the environment, defining dependencies between signatures to model step-wise attacks, and recognizing exploit scans.To leverage existing efforts, we convert the comprehensive signature set of the popular freeware NIDS snort into bro's language. While this does not provide us with improved signatures by itself, we reap an established base to build upon. Consequently, we evaluate our work by comparing to snort, discussing in the process several general problems of comparing different NIDSs.

No abstract available.