End-to-end security in active networks

Active network solutions have been proposed to many of the problems caused by the increasing heterogeneity of the Internet. These ystems allow nodes within the network to process data passing through in several ways. Allowing code from various sources to run on routers introduces numerous security concerns that have been addressed by research into safe languages, restricted execution environments, and other related areas. But little attention has been paid to an even more critical question: the effect on end-to-end security of active flow manipulation. This thesis first examines the threat model implicit in active networks. It develops a framework of security protocols in use at various layers of the networking stack, and their utility to multimedia transport and flow processing, and asks if it is reasonable to give active routers access to the plaintext of these flows. After considering the various security problem introduced, such as vulnerability to attacks on intermediaries or coercion, it concludes not. We then ask if active network systems can be built that maintain end-to-end security without seriously degrading the functionality they provide. We describe the design and analysis of three such protocols: a distributed packet filtering system that can be used to adjust multimedia bandwidth requirements and defend against denial-of-service attacks; an efficient composition of link and transport-layer reliability mechanisms that increases the performance of TCP over lossy wireless links; and a distributed watermarking servicethat can efficiently deliver media flows marked with the identity of their recipients. In all three cases, similar functionality is provided to designs that do not maintain end-to-end security. Finally, we reconsider traditional end-to-end arguments in both networking and security, and show that they have continuing importance for Internet design. Our watermarking work adds the concept of splitting trust throughout a network to that model; we suggest further applications of this idea

Lime: Data Lineage in the Malicious Environment

Intentional or unintentional leakage of confidential data is undoubtedly one of the most severe security threats that organizations face in the digital era. The threat now extends to our personal lives: a plethora of personal information is available to social networks and smartphone providers and is indirectly transferred to untrustworthy third party and fourth party applications. In this work, we present a generic data lineage framework LIME for data flow across multiple entities that take two characteristic, principal roles (i.e., owner and consumer). We define the exact security guarantees required by such a data lineage mechanism toward identification of a guilty entity, and identify the simplifying non repudiation and honesty assumptions. We then develop and analyze a novel accountable data transfer protocol between two entities within a malicious environment by building upon oblivious transfer, robust watermarking, and signature primitives. Finally, we perform an experimental evaluation to demonstrate the practicality of our protocol

Limitations of OpenFlow Topology Discovery Protocol

OpenFlow Discovery Protocol (OFDP) is the de-facto protocol used by OpenFlow controllers to discover the underlying topology. In this paper, we show that OFDP has some serious security, efficiency and functionality limitations that make it non suitable for production deployments. Instead, we briefly introduce sOFTD, a new discovery protocol with a built-in security characteristics and which is more efficient than traditional OFDP.Comment: The peer reviewed version can be found here (to be published soon

On the Security of the Automatic Dependent Surveillance-Broadcast Protocol

Automatic dependent surveillance-broadcast (ADS-B) is the communications protocol currently being rolled out as part of next generation air transportation systems. As the heart of modern air traffic control, it will play an essential role in the protection of two billion passengers per year, besides being crucial to many other interest groups in aviation. The inherent lack of security measures in the ADS-B protocol has long been a topic in both the aviation circles and in the academic community. Due to recently published proof-of-concept attacks, the topic is becoming ever more pressing, especially with the deadline for mandatory implementation in most airspaces fast approaching. This survey first summarizes the attacks and problems that have been reported in relation to ADS-B security. Thereafter, it surveys both the theoretical and practical efforts which have been previously conducted concerning these issues, including possible countermeasures. In addition, the survey seeks to go beyond the current state of the art and gives a detailed assessment of security measures which have been developed more generally for related wireless networks such as sensor networks and vehicular ad hoc networks, including a taxonomy of all considered approaches.Comment: Survey, 22 Pages, 21 Figure

DeepMarks: A Digital Fingerprinting Framework for Deep Neural Networks

This paper proposes DeepMarks, a novel end-to-end framework for systematic fingerprinting in the context of Deep Learning (DL). Remarkable progress has been made in the area of deep learning. Sharing the trained DL models has become a trend that is ubiquitous in various fields ranging from biomedical diagnosis to stock prediction. As the availability and popularity of pre-trained models are increasing, it is critical to protect the Intellectual Property (IP) of the model owner. DeepMarks introduces the first fingerprinting methodology that enables the model owner to embed unique fingerprints within the parameters (weights) of her model and later identify undesired usages of her distributed models. The proposed framework embeds the fingerprints in the Probability Density Function (pdf) of trainable weights by leveraging the extra capacity available in contemporary DL models. DeepMarks is robust against fingerprints collusion as well as network transformation attacks, including model compression and model fine-tuning. Extensive proof-of-concept evaluations on MNIST and CIFAR10 datasets, as well as a wide variety of deep neural networks architectures such as Wide Residual Networks (WRNs) and Convolutional Neural Networks (CNNs), corroborate the effectiveness and robustness of DeepMarks framework