Network accountability, forensic analysis, and failure diagnosis are becoming increasingly important for network management and security. Such capabilities often utilize network provenance -the ability to issue queries over network meta-data. For example, network provenance may be used to trace the path a message traverses on the network as well as to determine how message data were derived and which parties were involved in its derivation. This paper presents the design and implementation of ExSPAN, a generic and extensible framework that achieves efficient network provenance in a distributed environment. We utilize the database notion of data provenance to "explain" the existence of any network state, providing a versatile mechanism for network provenance. To achieve such flexibility at Internet-scale, ExSPAN uses declarative networking in which network protocols can be modeled as continuous queries over distributed streams and specified concisely in a declarative query language. We extend existing data models for provenance developed in database literature to enable distribution at Internet-scale, and investigate numerous optimization techniques to maintain and query distributed network provenance efficiently. The ExSPAN prototype is developed using RapidNet, a declarative networking platform based on the emerging ns-3 toolkit. Experiments over a simulated network and an actual deployment in a testbed environment demonstrate that our system supports a wide range of distributed provenance computations efficiently, resulting in significant reductions in bandwidth costs compared to traditional approaches. ABSTRACTNetwork accountability, forensic analysis, and failure diagnosis are becoming increasingly important for network management and security. Such capabilities often utilize network provenance -the ability to issue queries over network meta-data. For example, network provenance may be used to trace the path a message traverses on the network as well as to determine how message data were derived and which parties were involved in its derivation. This paper presents the design and implementation of ExSPAN, a generic and extensible framework that achieves efficient network provenance in a distributed environment. We utilize the database notion of data provenance to "explain" the existence of any network state, providing a versatile mechanism for network provenance. To achieve such flexibility at Internet-scale, ExSPAN uses declarative networking in which network protocols can be modeled as continuous queries over distributed streams and specified concisely in a declarative query language. We extend existing data models for provenance developed in database literature to enable distribution at Internet-scale, and investigate numerous optimization techniques to maintain and query distributed network provenance efficiently. The ExSPAN prototype is developed using RapidNet, a declarative networking platform based on the emerging ns-3 toolkit. Experiments over a simulated network and an actual deployment in...

This paper introduces secure network provenance (SNP), a novel technique that enables networked systems to explain to their operators why they are in a certain state -e.g., why a suspicious routing table entry is present on a certain router, or where a given cache entry originated. SNP provides network forensics capabilities by permitting operators to track down faulty or misbehaving nodes, and to assess the damage such nodes may have caused to the rest of the system. SNP is designed for adversarial settings and is robust to manipulation; its tamper-evident properties ensure that operators can detect when compromised nodes lie or falsely implicate correct nodes. We also present the design of SNooPy, a general-purpose SNP system. To demonstrate that SNooPy is practical, we apply it to three example applications: the Quagga BGP daemon, a declarative implementation of Chord, and Hadoop MapReduce. Our results indicate that SNooPy can efficiently explain state in an adversarial setting, that it can be applied with minimal effort, and that its costs are low enough to be practical. Disciplines Computer SciencesComments Zhuo, W., Fei, Q., Narayan, A., Haeberlen, A., Loo, B., & Sherr, M., Secure Network Provenance, 23rd ACM Symposium on Operating Systems Principles (SOSP'11), Oct. 2011Oct. , doi: 10.1145 ACM COPYRIGHT NOTICE. Copyright © 2011 by the Association for Computing Machinery, Inc. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Publications Dept., ACM, Inc., fax +1 (212) 869-0481, or permissions@acm.org. Author(s)Wenchao Zhuo, Qiong Fei, Arjun Narayan, Andreas Haeberlen, Boon Thau Loo, and Micah Sherr ABSTRACTThis paper introduces secure network provenance (SNP), a novel technique that enables networked systems to explain to their operators why they are in a certain state -e.g., why a suspicious routing table entry is present on a certain router, or where a given cache entry originated. SNP provides network forensics capabilities by permitting operators to track down faulty or misbehaving nodes, and to assess the damage such nodes may have caused to the rest of the system. SNP is designed for adversarial settings and is robust to manipulation; its tamper-evident properties ensure that operators can detect when compromised nodes lie or falsely implicate correct nodes.We also present the design of SNooPy, a general-purpose SNP system. To demonstrate that SNooPy is practical, we apply it to three example applications: the Quagga BGP daemon, a declarative implementation of Chord, and Hadoop MapReduce. Our r...

Abstract. The performance of an anonymous path can be described using many network metrics -e.g., bandwidth, latency, jitter, loss, etc. However, existing relay selection algorithms have focused exclusively on producing paths with high bandwidth. In contrast to traditional node-based path techniques in which relay selection is biased by relays' node-characteristics (i.e., bandwidth), this paper presents the case for link-based path generation in which relay selection is weighted in favor of the highest performing links. Link-based relay selection supports more flexible routing, enabling anonymous paths with low latency, jitter, and loss, in addition to high bandwidth. Link-based approaches are also more secure than node-based techniques, eliminating "hotspots" in the network that attract a disproportionate amount of traffic. For example, misbehaving relays cannot advertise themselves as "low-latency" nodes to attract traffic, since latency has meaning only when measured between two endpoints. We argue that link-based path selection is practical for certain anonymity networks, and describe mechanisms for efficiently storing and disseminating link information.

The Tor anonymity network is difficult to measure because, if not done carefully, measurements could risk the privacy (and potentially the safety) of the network's users. Recent work has proposed the use of differential privacy and secure aggregation techniques to safely measure Tor, and preliminary proof-of-concept prototype tools have been developed in order to demonstrate the utility of these techniques. In this work, we significantly enhance two such tools-PrivCount and Private Set-Union Cardinality-in order to support the safe exploration of new types of Tor usage behavior that have never before been measured. Using the enhanced tools, we conduct a detailed measurement study of Tor covering three major aspects of Tor usage: how many users connect to Tor and from where do they connect, with which destinations do users most frequently communicate, and how many onion services exist and how are they used. Our findings include that Tor has ∼8 million daily users (a factor of four more than previously believed) while Tor user IPs turn over almost twice in a 4 day period. We also find that ∼40% of the sites accessed over Tor have a torproject.org domain name, ∼10% of the sites have an amazon.com domain name, and ∼80% of the sites have a domain name that is included in the Alexa top 1 million sites list. Finally, we find that ∼90% of lookups for onion addresses are invalid, and more than 90% of attempted connections to onion services fail.

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Abstract-Discovering the causes of incorrect behavior in large networks is often difficult. This difficulty is compounded when some machines in the network are compromised, since these compromised machines may use deception or tamper with data to frustrate forensic analysis. Recently proposed forensic tools enable administrators to learn the causes of some system states in a partially compromised network, but these tools are inherently unable to (1) observe covert communication between compromised nodes or (2) detect attempts to exfiltrate sensitive data.In this paper, we observe that the emergence of Software-Defined Networking (SDN) offers interesting new opportunities for network forensics. We sketch the design of an SDN-based forensic system that can be used to investigate a wide variety of faults in data center networks, including previously unobservable attacks such as data exfiltration and collusion between compromised nodes. Our key insight is that the network itself can be used as a point of observation, which gives us a holistic view of network activity. We show that a collection of lightweight middleboxes would be sufficient to support this functionality, and we discuss several additional challenges and opportunities for SDN-based forensic tools.

This paper introduces a cryptographic protocol for efficiently aggregating a count of unique items across a set of data parties privately-that is, without exposing any information other than the count. Our protocol allows for more secure and useful statistics gathering in privacy-preserving distributed systems such as anonymity networks; for example, it allows operators of anonymity networks such as Tor to securely answer the questions: how many unique users are using the distributed service? and how many hidden services are being accessed? We formally prove the correctness and security of our protocol in the Universal Composability framework against an active adversary that compromises all but one of the aggregation parties. We also show that the protocol provides security against adaptive corruption of the data parties, which prevents them from being victims of targeted compromise. To ensure safe measurements, we also show how the output can satisfy differential privacy. We present a proof-of-concept implementation of the private set-union cardinality protocol (PSC) and use it to demonstrate that PSC operates with low computational overhead and reasonable bandwidth. In particular, for reasonable deployment sizes, the protocol run at timescales smaller than the typical measurement period would be and thus is suitable for distributed measurement.

Despite considerable attention from both the academic and commercial communities, denial-of-service (DoS)