Tamara Bonaci

Node capture attacks in wireless sensor networks: A system theoretic approach

In this paper we address the problem of physical node capture attacks in wireless sensor networks and provide a control theoretic framework to model physical node capture, cloned node detection and revocation of compromised nodes. By combining probabilistic analysis of logical key graphs and linear control theory, we derive a dynamical model that efficiently describes network behavior under attack. Using LQR and LQG optimal control theory tools, we develop a network response strategy, which guarantees secure network connectivity and stability under attack. Detailed simulations are presented to validate the methodology.

Distributed clone detection in wireless sensor networks: An optimization approach

In this paper, we study distributed algorithms for detecting cloned nodes in wireless sensor networks. We consider the impact of leaving undetected cloned nodes in the network, as well as the communication cost and the storage cost incurred by each algorithm. We develop an optimization framework for choosing clone detection parameters based on these costs and analyze existing detection schemes using the developed framework. Simulations are provided to validate the methodology.

A convex optimization approach for clone detection in wireless sensor networks

Abstract When deployed unattended in hostile environments, static and mobile Wireless Sensor Networks (WSNs) are vulnerable to node capture and cloning attacks, where an adversary physically compromises network nodes and extracts all information known to them, including the assigned cryptographic material and the internal states of network protocols. The obtained knowledge is used to disrupt the network by deploying and controlling copies of captured nodes (clones). Recently, a variety of novel clone detection methods have been developed, using concepts such as birthday paradox, sequential detection or random encounters in mobile environments. At present there is no framework to evaluate an individual detection method based on the WSN performance under attack or to compare and choose a method appropriate for a given application. In this paper, we develop an optimization framework for choosing the parameters of a detection method so that the cost of clone detection is minimized. We show that every detection method can be characterized in terms of four costs, namely, the impact of leaving undetected cloned nodes in the network, the cost of revoking nodes falsely identified as compromised, and the costs of communication and storage. A convex combination of these costs defines the cost of clone detection, which is then minimized with respect to the parameters of the detection method. Using this framework, we analyze existing clone detection algorithms and provide efficient methods for obtaining optimal detection parameters.

Securing the exocortex: A twenty-first century cybernetics challenge

An exocortex is a wearable (or implanted) computer, used to augment a brains biological high-level cognitive processes and inform a users decisions and actions. In this paper, we focus on Brain-Computer Interfaces (BCIs), a special type of exocortex used to interact with the environment via neural signals. BCI use ranges from medical applications and rehabilitation to operation of assistive devices. They can also be used for marketing, gaming, and entertainment, where BCIs are used to provide users with a more personalized experience. BCI-enabled technology carries a great potential to improve and enhance the quality of human lives. This technology, however, is not without risk. In this paper, we address a specific class of privacy issues, brain spyware, shown to be feasible against currently available non-invasive BCIs. We show this attack can be mapped into a communication-theoretic setting. We then show that the problem of preventing it is similar to the problem of information hiding in communications. We address it in an information-theoretic framework. Finally, influenced by Professor Wieners computer ethics work, we propose a set of principles regarding appropriate use of exocortex.

Securing the Exocortex: A Twenty-First Century Cybernetics Challenge

An exocortex is a wearable (or implanted) computer, used to augment a brains biological high-level cognitive processes and inform a users decisions and actions. In this paper, we focus on Brain-Computer Interfaces (BCIs), a special type of exocortex used to interact with the environment via neural signals. BCI use ranges from medical applications and rehabilitation to operation of assistive devices. They can also be used for marketing, gaming, and entertainment, where BCIs are used to provide users with a more personalized experience. BCI-enabled technology carries a great potential to improve and enhance the quality of human lives. This technology, however, is not without risk. In this paper, we address a specific class of privacy issues, brain spyware, shown to be feasible against currently available non-invasive BCIs. We show this attack can be mapped into a communication-theoretic setting. We then show that the problem of preventing it is similar to the problem of information hiding in communications. We address it in an information-theoretic framework. Finally, influenced by Professor Wieners computer ethics work, we propose a set of principles regarding appropriate use of exocortex.

On potential security threats against rescue robotic systems

Remotely operated robots can be used for rescue and recovery in natural disasters and man-made catastrophes, including battlefield environments. But, what if the robot is taken over and turned into a weapon? In this paper, we consider the type of attacks that might occur and their implications on rescue and recovery missions. From this, we introduce a notion of telerobotic security and propose some ideas to ensure that rescue systems are “teleoperation secure” against one likely exploit.

Node capture games: a game theoretic approach to modeling and mitigating node capture attacks

Unattended wireless sensor networks are susceptible to node capture attacks, where the adversary physically compromises a node, creates functional copies (clones) of it and deploys such clones back into the network, in order to impact the networks functionality. In the absence of a centralized authority, distributed clone detection methods have been developed to mitigate this attack. In this paper, we show that the node capture attack and the network response can be modeled as a simultaneous, noncooperative, two-player game. In developing the game-theoretic framework, we consider a deterministic, linear dynamical model of the attack, as well as a general, stochastic model. For the deterministic model, we develop three games, all of which have quadratic utility for the valid network, whereas the adversarys utility depends on the assumptions about ist abilities. For the stochastic model, we develop a game with convex utility functions. For each game, we prove the existence of a pure strategy Nash Equilibrium and present an efficient way of solving the game. These equilibria can then be used in choosing the appropriate parameters for detecting and responding to the attack. Simulations are provided to illustrate our approach.