Today's networks are not strictly speaking "safe" places. New attacks on connected infrastructures are reported literally every day leading to loss of connectivity, reduced performance or violation of privacy. Moreover, while these attacks used to originate mostly from the Internet, nowadays however, the majority of them are performed by insiders, acting directly from within the network. The advent of the Internet-of-Things (IoT) obviously does not help — many of these devices have serious security vulnerabilities.
Our group investigates how we can build more secure networked systems by: (i) analyzing the effects of important attack vectors such as Internet routing attacks; and (ii) enabling the network itself to detect and mitigate insider attacks instead of relying purely on perimeter-based protection.
Maria Apostolaki, IEEE S&P 2017
Maria Apostolaki, NDSS Symposium 2019
Roland Meier, USENIX Security 2018
Yixin Sun, USENIX Security 2015
Armasuisse & Zurich Information Security and Privacy Center (ZISC)
“Improving network security through programmability”
ACM HotNets 2019. Princeton, NJ, USA (November 2019).
Traditional network control planes can be slow and require manual tinkering from operators to change their behavior. There is thus great interest in a faster, data-driven approach that uses signals from real-time traffic instead. However, the promise of fast and automatic reaction to data comes with new risks: malicious inputs designed towards negative outcomes for the network, service providers, users, and operators.
Adversarial inputs are a well-recognized problem in other areas; we show that networking applications are susceptible to them too. We characterize the attack surface of data-driven networks and examine how attackers with different privileges—from infected hosts to operator-level access—may target network infrastructure, applications, and protocols. To illustrate the problem, we present case studies with concrete attacks on recently proposed data-driven systems.
Our analysis urgently calls for a careful study of attacks and defenses in data-driven networking, with a view towards ensuring that their promise is not marred by oversights in robust design.
NDSS Symposium 2019. San Diego, CA, USA (February 2019).
Nowadays Internet routing attacks remain practically effective as existing countermeasures either fail to provide protection guarantees or are not easily deployable. Blockchain systems are particularly vulnerable to such attacks as they rely on Internet-wide communications to reach consensus. In particular, Bitcoin---the most widely-used cryptocurrency---can be split in half by any AS-level adversary using BGP hijacking.
In this paper, we present SABRE, a secure and scalable Bitcoin relay network which relays blocks worldwide through a set of connections that are resilient to routing attacks. SABRE runs alongside the existing peer-to-peer network and is easily deployable. As a critical system, SABRE design is highly resilient and can efficiently handle high bandwidth loads, including Denial of Service attacks.
We built SABRE around two key technical insights. First, we leverage fundamental properties of inter-domain routing (BGP) policies to host relay nodes: (i) in networks that are inherently protected against routing attacks; and (ii) on paths that are economically-preferred by the majority of Bitcoin clients. These properties are generic and can be used to protect other Blockchain-based systems. Second, we leverage the fact that relaying blocks is communication-heavy, not computation-heavy. This enables us to offload most of the relay operations to programmable network hardware (using the P4 programming language). Thanks to this hardware/software co-design, SABRE nodes operate seamlessly under high load while mitigating the effects of malicious clients.
We present a complete implementation of SABRE together with an extensive evaluation. Our results demonstrate that SABRE is effective at securing Bitcoin against routing attacks, even with deployments of as few as 6 nodes.
USENIX Security 2018. Baltimore, MD, USA (August 2018).
Simple path tracing tools such as traceroute allow malicious users to infer network topologies remotely and use that knowledge to craft advanced denial-of-service (DoS) attacks such as Link-Flooding Attacks (LFAs). Yet, despite the risk, most network operators still allow path tracing as it is an essential network debugging tool.
In this paper, we present NetHide, a network topology obfuscation framework that mitigates LFAs while preserving the practicality of path tracing tools. The key idea behind NetHide is to formulate network obfuscation as a multi-objective optimization problem that allows for a flexible tradeoff between security (encoded as hard constraints) and usability (encoded as soft constraints). While solving this problem exactly is hard, we show that NetHide can obfuscate topologies at scale by only considering a subset of the candidate solutions and without reducing obfuscation quality. In practice, NetHide obfuscates the topology by intercepting and modifying path tracing probes directly in the data plane. We show that this process can be done at line-rate, in a stateless fashion, by leveraging the latest generation of programmable network devices.
We fully implemented NetHide and evaluated it on realistic topologies. Our results show that NetHide is able to obfuscate large topologies (> 150 nodes) while preserving near-perfect debugging capabilities. In particular, we show that operators can still precisely trace back > 90% of link failures despite obfuscation.
David Gugelmann, David Sommer, Vincent Lenders, Markus Happe, Laurent Vanbever
NATO CCD COE CyCon 2018. Tallinn, Estonia (May 2018).
Organizations not only need to defend their IT systems against external cyber attackers, but also from malicious insiders, that is, agents who have infiltrated an organization or malicious members stealing information for their own profit. In particular, malicious insiders can leak a document by simply opening it and taking pictures of the document displayed on the computer screen with a digital camera. Using a digital camera allows a perpetrator to easily avoid a log trail that results from using traditional communication channels, such as sending the document via email. This makes it difficult to identify and prove the identity of the perpetrator. Even a policy prohibiting the use of any device containing a camera cannot eliminate this threat since tiny cameras can be hidden almost everywhere.
To address this leakage vector, we propose a novel screen watermarking technique that embeds hidden information on computer screens displaying text documents. The watermark is imperceptible during regular use, but can be extracted from pictures of documents shown on the screen, which allows an organization to reconstruct the place and time of the data leak from recovered leaked pictures. Our approach takes advantage of the fact that the human eye is less sensitive to small luminance changes than digital cameras. We devise a symbol shape that is invisible to the human eye, but still robust to the image artifacts introduced when taking pictures. We complement this symbol shape with an error correction coding scheme that can handle very high bit error rates and retrieve watermarks from cropped and compressed pictures. We show in an experimental user study that our screen watermarks are not perceivable by humans and analyze the robustness of our watermarks against image modifications.
NATO CCD COE CyCon 2018. Tallinn, Estonia (May 2018).
Organizations increasingly rely on cyber threat intelligence feeds to protect their infrastructure from attacks. These feeds typically list IP addresses or domains associated with malicious activities such as spreading malware or participating in a botnet. Today, there is a rich ecosystem of commercial and free cyber threat intelligence feeds, making it difficult, yet essential, for network defenders to quantify the quality and to select the optimal set of feeds to follow. Selecting too many or low-quality feeds results in many false alerts, while considering too few feeds increases the risk of missing relevant threats. Naïve individual metrics like size and update rate give a somewhat good overview about a feed, but they do not allow conclusions about its quality and they can easily be manipulated by feed providers.
In this paper, we present FeedRank, a novel ranking approach for cyber threat intelligence feeds. In contrast to individual metrics, FeedRank is robust against tampering attempts by feed providers. FeedRank’s key insight is to rank feeds according to the originality of their content and the reuse of entries by other feeds. Such correlations between feeds are modelled in a graph, which allows FeedRank to find temporal and spatial correlations without requiring any ground truth or an operator’s feedback.
We illustrate FeedRank’s usefulness with two characteristic examples: (i) selecting the best feeds that together contain as many distinct entries as possible; and (ii) selecting the best feeds that list new entries before they appear on other feeds. We evaluate FeedRank based on a large set of real feeds. The evaluation shows that FeedRank identifies dishonest feeds as outliers and that dishonest feeds do not achieve a better FeedRank score than the top-rated real feeds.
Pavlos Lamprakis, Ruggiero Dargenio, David Gugelmann, Vincent Lenders, Markus Happe, Laurent Vanbever
DIMVA 2017. Bonn, Germany (July 2017).
HTTP is the main protocol used by attackers to establish a command and control (C&C) channel to infected hosts in a network. Identifying such C&C channels in network trffiac is however a challenge because of the large volume and complex structure of benign HTTP requests emerging from regular user browsing activities. A common approach to C&C channel detection has been to use supervised learning techniques which are trained on old malware samples. However, these techniques require large training datasets which are generally not avail- able in the case of advanced persistent threats (APT); APT malware are often custom-built and used against selected targets only, making it dicult to collect malware artifacts for supervised machine learning and thus rendering supervised approaches ineffective at detecting APT traffic.
In this paper, we present a novel and highly effective unsupervised approach to detect C&C channels in Web traffic. Our key observation is that APT malware typically follow a specific communication pattern that is different from regular Web browsing. Therefore, by reconstructing the dependencies between Web requests, that is the Web request graphs, and filering away the nodes pertaining to regular Web browsing, we can identify malware requests without training a malware model. We evaluated our approach on real Web traces and show that it can detect the C&C requests of nine APTs with a true positive rate of 99.5- 100% and a true negative rate of 99.5-99.7%. These APTs had been used against several hundred organizations for years without being detected.
IEEE Symposium on Security and Privacy 2017. San Jose, CA, USA (May 2017).
As the most successful cryptocurrency to date, Bitcoin constitutes a target of choice for attackers. While many attack vectors have already been uncovered, one important vector has been left out though: attacking the currency via the Internet routing infrastructure itself. Indeed, by manipulating routing advertisements (BGP hijacks) or by naturally intercepting traffic, Autonomous Systems (ASes) can intercept and manipulate a large fraction of Bitcoin traffic.
This paper presents the first taxonomy of routing attacks and their impact on Bitcoin, considering both small-scale attacks, targeting individual nodes, and large-scale attacks, targeting the network as a whole. While challenging, we show that two key properties make routing attacks practical: (i) the efficiency of routing manipulation; and (ii) the significant centralization of Bitcoin in terms of mining and routing. Specifically, we find that any network attacker can hijack few (<100) BGP prefixes to isolate 50% of the mining power—even when considering that mining pools are heavily multi-homed. We also show that on-path network attackers can considerably slow down block propagation by interfering with few key Bitcoin messages.
We demonstrate the feasibility of each attack against the deployed Bitcoin software. We also quantify their effectiveness on the current Bitcoin topology using data collected from a Bitcoin supernode combined with BGP routing data.
The potential damage to Bitcoin is worrying. By isolating parts of the network or delaying block propagation, attackers can cause a significant amount of mining power to be wasted, leading to revenue losses and enabling a wide range of exploits such as double spending. To prevent such effects in practice, we provide both short and long-term countermeasures, some of which can be deployed immediately.
ACM SOSR 2017. Santa Clara, CA, USA (April 2017).
Advances in layer 2 networking technologies have fostered the deployment of large, geographically distributed LANs. Due to their large diameter, such LANs provide many vantage points for wiretapping. As an example, Google's internal network was reportedly tapped by governmental agencies, forcing the Web giant to encrypt its internal traffic. While using encryption certainly helps, eavesdroppers can still access traffic metadata which often reveals sensitive information, such as who communicates with whom and which are the critical hubs in the infrastructure.
This paper presents iTAP, a system for providing strong anonymity guarantees within a network. iTAP is network-based and can be partially deployed. Akin to onion routing, iTAP rewrites packet headers at the network edges by leveraging SDN devices. As large LANs can see millions of flows, the key challenge is to rewrite headers in a way that guarantees strong anonymity while, at the same time, scaling the control-plane (number of events) and the data-plane (number of flow rules). iTAP addresses these challenges by adopting a hybrid rewriting scheme. Specifically, iTAP scales by reusing rewriting rules across distinct flows and by distributing them on multiple switches. As reusing headers leaks information, iTAP monitors this leakage and adapts the rewriting rules before any eavesdropper could provably de-anonymize any host.
We implemented iTAP and evaluated it using real network traffic traces. We show that iTAP works in practice, on existing hardware, and that deploying few SDN switches is enough to protect a large share of the network traffic.
Yixin Sun, Anne Edmundson, Laurent Vanbever, Oscar Li, Jennifer Rexford, Mung Chiang, Prateek Mittal
USENIX Security 2015. Washington, D. C. , USA (August 2015).
Laurent Vanbever, Oscar Li, Jennifer Rexford, Prateek Mittal
ACM HotNets 2014. Los Angeles, CA, USA (October 2014).
Anonymity systems like Tor are known to be vulnerable to malicious relay nodes. Another serious threat comes from the Autonomous Systems (ASes) that carry Tor traffic due to their powerful eavesdropping capabilities. Indeed, an AS (or set of colluding ASes) that lies between the client and the first relay, and between the last relay and the destination, can perform timing analysis to compromise user anonymity. In this paper, we show that AS-level adversaries are much more powerful than previously thought. First, routine BGP routing changes can significantly increase the number of ASes that can analyze a user's traffic successfully. Second, ASes can actively manipulate BGP announcements to put themselves on the paths to and from relay nodes. Third, an AS can perform timing analysis even when it sees only one direction of the traffic at both communication ends. Actually, asymmetric routing increases the fraction of ASes able to analyze a user's traffic. We present a preliminary evaluation of our attacks using measurements of BGP and Tor. Our findings motivate the design of approaches for anonymous communication that are resilient to AS-level adversaries.