Leveraging Secure Multiparty Computation in the Internet of Things

Centralized systems in the Internet of Things---be it local middleware or cloud-based services---fail to fundamentally address privacy of the collected data. We propose an architecture featuring secure multiparty computation at its core in order to realize data processing systems which already incorporate support for privacy protection in the architecture

Directed Security Policies: A Stateful Network Implementation

Large systems are commonly internetworked. A security policy describes the communication relationship between the networked entities. The security policy defines rules, for example that A can connect to B, which results in a directed graph. However, this policy is often implemented in the network, for example by firewalls, such that A can establish a connection to B and all packets belonging to established connections are allowed. This stateful implementation is usually required for the network's functionality, but it introduces the backflow from B to A, which might contradict the security policy. We derive compliance criteria for a policy and its stateful implementation. In particular, we provide a criterion to verify the lack of side effects in linear time. Algorithms to automatically construct a stateful implementation of security policy rules are presented, which narrows the gap between formalization and real-world implementation. The solution scales to large networks, which is confirmed by a large real-world case study. Its correctness is guaranteed by the Isabelle/HOL theorem prover.Comment: In Proceedings ESSS 2014, arXiv:1405.055

Transparency for Control Plane Software

Transparency for Control Plane Softwar

Improving Markov-based TCP Traffic Classification

This paper presents an improved variant of our Markov-based TCP traffic classifier and demonstrates its performance using traffic captured in a university network. Payload length, flow direction, and position of the first data packets of a TCP connection are reflected in the states of the Markov models. In addition, we integrate a new "end of connection" state to further improve the classification accuracy. Using 10-fold cross validation, we identify appropriate settings for the payload length intervals and the number of data packets considered in the models. Finally, we discuss the classification results for the different applications