The low-cost, flexible nature of Internet-of-Things (IoT) hardware has resulted in widespread usage in a variety of applications from smart-home systems to industrial process-regulation controllers. As the number of networkconnected IoT devices has proliferated, they have become increasingly likely to be the target of widespread cyber-attacks. Since these devices are often low-resource, embedded or bare metal systems, conventional profiling techniques used by Personal Computers (PCs) and workstations have become highly impractical means for security. As a result, an IoT device could provide intruders with an unprotected backdoor into a network. Effectively protecting IoT hardware requires that alternative security protocols be developed and utilized to protect the IoT and the networks they are integrated with. One potential way of improving the security of IoT devices is by monitoring their side-channel emissions to observe device behavior. As these devices operate, they will produce multi-spectral phenomenon, or side-channel emissions, that correlate with program execution. By combining spectral analysis techniques with powerful machine learning algorithms, side-channel emissions can be utilized to bolster IoT device security and deny an intruder access to the network. This paper will review current state-of-the-art techniques used to monitor and classify the behavior of IoT devices. The paper will conclude by discussing several real-world applications presented in literature that have been shown to benefit from these techniques.
As the Internet of Things (IoT) grows to include billions of connected devices, securing these devices from executing malicious code has become a primary concern. Traditional methods of security such as anti-malware software and firewall protection are often impractical due to the limited computing resources these devices often feature. Given these conditions, one possible approach to securing IoT devices is external monitoring for detection of anomalous behavior. Much like spectral signatures used in remote sensing for object identification, Internet of Things (IoT) devices unintentionally generate a unique signature in the radio frequency (RF) spectrum based on the code being executed. This study investigates methods for processing time domain RF data into a set of machine learning features that can be used to distinguish between a set of known instructions, sub-routines, and programs. A feature clustering approach using the magnitude of points in the frequency spectrum is presented along with other feature extraction methods.
Analog phase shifters are investigated with a periodic structure that includes Barium Strontium Titanate ferroelectric thin film varactors in shunt or serial connection to the coplanar waveguide transmission line. The phase shift is achieved by applying a DC bias to the varactors and changing the reactance in the circuit. The goal of this paper is to characterize the shunt capacitive varactors regarding the voltage dependence of the capacitance, loss tangent, and insertion losses at different bias voltages. Quality factor analysis is also conducted taking the parasitic effects into account. Repeated measurements show that the capacitance of a single cell is tuned from 0.8pF to 0.2pF under a DC bias of 0-10V while the loss tangent is kept under 0.01 in the frequency range of 0-40GHz. Insertion loss is tuned from -4dB to less than -0.6dB from 0 to 10V with a Figure of Merit of 14 degrees/dB at 10GHz and the total quality factor of the unit cell is around 6.7 to 10 at 10GHz with matched port impedance. By cascading 10-25 single unit cells, the phase shift is expected to reach 360 degrees with minimum insertion loss.