| Summary: | The Internet of Things (IoT) revolution is rapidly altering our vision of collecting and analyzing real time data to optimize applications and services related to transportation, environmental monitoring, security, among others. However, the wide adoption of IoT technology faces critical challenges such as lack of compatibility and interoperability among IoT systems, need of dedicated infrastructure, inability to scale to thousands of devices, immature standards, insecure
identification and authentication etc. This dissertation presents our progressive efforts to overcome some of these challenges by designing scalable, energy-efficient as well as tamper-proof authentication and signaling mechanisms. The key contributions of this dissertation include four novel techniques i) 'FreeIoT', a city-scale IoT control signalling over LTE, ii) 'ORACLE', a deep learning based secure device authentication, iii) 'ISK', covert signaling by deep learning of controlled
radio imperfections, and iv) 'CSIscan', a control signalling in WiFi for efficient access point (AP) discovery. FreeIoT provides city-scale control signaling for IoT sensors over LTE without installing any additional infrastructure. FreeIoT encodes control messages by changing the spatial positioning of Almost Blank Subframes (ABS) within a standard-compliant LTE frame. ABS was originally defined in the standard to allow coexistence between the macro-cell eNB and nearby small cells,
which FreeIoT leverages as a side channel for IoT signaling. We implement a proof of concept testbed to validate the operation of FreeIoT using a software defined LTE eNB and custom-designed RF energy harvesting circuit interfaced with off-the-shelf sensors. For secure device authentication, we design ORACLE, an approach for detecting a unique radio from a large pool of bit-similar devices (same hardware, protocol, physical address, MAC ID) using only IQ samples at the physical layer.
We extensively evaluate the performance of the fingerprinting approach on large-scale datasets of WiFi- transmissions collected ``in the wild'', as well as a dataset of nominally-identical (i.e., equal baseband signals) WiFi devices. For covert wireless communications, we present impairment shift keying (ISK) that authenticates a device or exchanges private information between devices. ISK introduces small yet controlled modifications to the radio transmitter hardware, which distorts
regular standards-compliant waveforms, such as WiFi, with only 1% increase in bit error rate. A deep convolutional neural network is trained to learn these overlay signal variations, which serves as a low-overhead classifier returning a binary 0 or 1 per detected impairment pattern. By mapping device-specific injected impairment patterns to signal variations, ISK validates device IDs with only few inphase (I) and quadrature (Q) samples. Finally, we propose CSIscan that embeds discovery
related information within AP's ongoing regular transmissions for its efficient discovery. CSIscan intelligently distorts the transmitted physical layer OFDM frame by inducing perturbations in the preamble. A deep learning framework allocates the optimal level of distortion on a per-subcarrier basis that keeps resulting bit error rate to less than 1%, while also allowing decoding the overlay bits via changes in the perceived channel state information (CSI).
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