Doctor of Philosophy

dissertationCross layer system design represents a paradigm shift that breaks the traditional layer-boundaries in a network stack to enhance a wireless network in a number of di erent ways. Existing work has used the cross layer approach to optimize a wireless network in terms of packet scheduling, error correction, multimedia quality, power consumption, selection of modulation/coding and user experience, etc. We explore the use of new cross layer opportunities to achieve secrecy and e ciency of data transmission in wireless networks. In the rst part of this dissertation, we build secret key establishment methods for private communication between wireless devices using the spatio-temporal variations of symmetric-wireless channel measurements. We evaluate our methods on a variety of wireless devices, including laptops, telosB sensor nodes, and Android smartphones, with diverse wireless capabilities. We perform extensive measurements in real-world environments and show that our methods generate high entropy secret bits at a signi cantly faster rate in comparison to existing approaches. While the rst part of this dissertation focuses on achieving secrecy in wireless networks, the second part of this dissertation examines the use of special pulse shaping lters of the lterbank multicarrier (FBMC) physical layer in reliably transmitting data packets at a very high rate. We rst analyze the mutual interference power across subcarriers used by di erent transmitters. Next, to understand the impact of FBMC beyond the physical layer, we devise a distributed and adaptive medium access control protocol that coordinates data packet tra c among the di erent nodes in the network in a best e ort manner. Using extensive simulations, we show that FBMC consistently achieves an order-of-magnitude performance improvement over orthogonal frequency division multiplexing (OFDM) in several aspects, including packet transmission delays, channel access delays, and e ective data transmission rate available to each node in static indoor settings as well as in vehicular networks

Secret Key Generation Schemes for Physical Layer Security

Physical layer security (PLS) has evolved to be a pivotal technique in ensuring secure wireless communication. This paper presents a comprehensive analysis of the recent developments in physical layer secret key generation (PLSKG). The principle, procedure, techniques and performance metricesare investigated for PLSKG between a pair of users (PSKG) and for a group of users (GSKG). In this paper, a detailed comparison of the various parameters and techniques employed in different stages of key generation such as, channel probing, quantisation, encoding, information reconciliation (IR) and privacy amplification (PA) are provided. Apart from this, a comparison of bit disagreement rate, bit generation rate and approximate entropy is also presented. The work identifies PSKG and GSKG schemes which are practically realizable and also provides a discussion on the test bed employed for realising various PLSKG schemes. Moreover, a discussion on the research challenges in the area of PLSKG is also provided for future research

Using Wireless Link Dynamics to Extract a Secret Key in Vehicular Scenarios

Securing a wireless channel between any two vehicles is a crucial component of vehicular networks security. This can be done by using a secret key to encrypt the messages. We propose a scheme to allow two cars to extract a shared secret from RSSI (Received Signal Strength Indicator) values in such a way that nearby cars cannot obtain the same key. The key is information-theoretically secure, i.e., it is secure against an adversary with unlimited computing power. Although there are existing solutions of key extraction in the indoor or low-speed environments, the unique channel conditions make them inapplicable to vehicular environments. Our scheme effectively and efficiently handles the high noise and mismatch features of the measured samples so that it can be executed in the noisy vehicular environment. We also propose an online parameter learning mechanism to adapt to different channel conditions. Extensive real-world experiments are conducted to validate our solution

Secret Key Generation Rate vs. Reconciliation Cost Using Wireless Channel Characteristics in Body Area Networks

Abstract—In this paper, we investigate the feasibility of real-time derivation of cryptographic keys in body area networks using unique characteristics of the underlying wireless channel. We perform experiments to confirm that motion does indeed provide significant highly correlated randomness on either end of the wireless link between basestation and mobile mote to enable real-time key generation. Furthermore, we demonstrate that channel characteristics for a dynamic body area network consist of two different components, a fast and a slow component, each of which make a qualitatively different contribution to key generation. These components can be isolated to address specific needs of the application scenario: the fast component can yield high entropy keys at a fast rate between basestation and mobile mote with some bit disagreement between the two devices; the slow component generates keys at a lower rate but with very high level of bit agreement. Our experimental results highlight this tradeoff, and our key generation protocol details the key extraction process. I

Authenticating Users Through Fine-Grained Channel Information

User authentication is the critical first step in detecting identity-based attacks and preventing subsequent malicious attacks. However, the increasingly dynamic mobile environments make it harder to always apply cryptographic-based methods for user authentication due to their infrastructural and key management overhead. Exploiting non-cryptographic based techniques grounded on physical layer properties to perform user authentication appears promising. In this work, the use of channel state information (CSI), which is available from off-the-shelf WiFi devices, to perform fine-grained user authentication is explored. Particularly, a user-authentication framework that can work with both stationary and mobile users is proposed. When the user is stationary, the proposed framework builds a user profile for user authentication that is resilient to the presence of a spoofer. The proposed machine learning based user-authentication techniques can distinguish between two users even when they possess similar signal fingerprints and detect the existence of a spoofer. When the user is mobile, it is proposed to detect the presence of a spoofer by examining the temporal correlation of CSI measurements. Both office building and apartment environments show that the proposed framework can filter out signal outliers and achieve higher authentication accuracy compared with existing approaches using received signal strength (RSS)