The Internet of Things (IoT) and the Internet of Everything (IoE) have driven processors into nearly every powered device, from thermostats to refrigerators to light bulbs. From a security perspective, the IoT and IoE create a new layer of signals and systems that can provide insight into the internal operations of a device via analog side channels. Our research focuses on leveraging these analog ...

Field Programmable Gate Arrays (FPGAs) are increasingly vital components of electronic systems used in numerous industries. FPGAs possess well-documented logic and hardware vulnerabilities that could allow an adversary to penetrate and manipulate FPGA-based electronic infrastructure. To detect such attacks against FPGA firmware, we developed a technique that exploits the unintended RF side-channel emitted from an FPGA. Our approach presumes that malicious modification to a trusted FPGA bitstream will result in changes in radio frequency (RF) emissions-changes that our technique can detect and measure using signal processing and machine learning. The development of our RF side-channel technique was divided into three tasks: (1) determine if firmware changes can be detected using side-channel emissions, (2) determine the minimum firmware change that can be detected, and (3) extend our approach to work across multiple devices of the same type. We used the Digilent Arty development board to accomplish these tasks. We developed baseline firmware for the board and then generated additional bitstreams that incorporated quantifiable changes in the logic and placement. We then collected RF side-channel emissions for each bitstream using the Riscure EM Probe Station, which uses a 1 GHz bandwidth near-field antenna. Using our RF side-channel approach, we were able to detect the movement of a single register or lookup table element by one slice. We proved the effectiveness of our technique to detect changes across multiple FPGAs of the same type by achieving detection accuracy greater than 98%.

We applied machine learning to detect changes in state of key registers in digital devices from their analog RF emissions. As digital devices operate, they emit information via analog side channels. We collected the RF side channel with a 500-MHz shielded loop probe from Riscure, placed in the nearfield (< 1mm) of the device under test (DuT). We investigated a number of Internet-of-Thing (IoT) DuTs including Arduino Uno and PIC24 processors. Conventional processors implement instructions as a sequence of subtasks. The first subtasks include incrementing the program counter (PC) register and fetching the next instruction from program memory to the instruction register (IR). These two subtasks occur almost every instruction cycle. We ran programs on the DuT and collected the RF emissions. We parsed the object code of the programs to determine the state of key registers including the PC and IR during each instruction cycle and observed that the RF signal of each cycle is strongly correlated with the Hamming Distance (HD) (i.e., the number of bits changing) in the PC and IR registers. Based on this result, we developed classifiers to extract the HD of the PC, IR, as well as the stack pointer (SP). The classification results vary with true HD as some values are rare and have few examples in the training set. The classification accuracy exceeds 99% for the PC and the IR. Due to the relatively few HD in the training set for the SP, its results slightly exceeded 97%.

All digital devices leak information through unintended emissions into analog side channels. The RF side channel enables passive collection of high-bandwidth information about the digital state of the device. We collected these RF emissions with a 500-MHz Riscure probe placed in the nearfield of the device under test (DuT) and applied machine learning to detect what program is running on the processor to identify malware intrusions. We explored the applicability of a generalized algorithm classification infrastructure built from a training set of similar DuTs to a similar device from a different production batch (same model number, different serial number.) We collected RF-SC data for five programs running on 28 distinct Arduino Unos (and 28 MSP430 processors.) We trained program classifiers on RF data from all but one DuT and tested the classifiers on the device withheld from the training set. The high-SNR signal provided by the Riscure probe enabled almost perfect classification results when we trained and tested on the same device. Our classification results remained above 99% when we generalized testing to the new DuT of the same model but a different serial number. The classifier was trained on 27 of the devices and tested to determine its ability to detect deviations from a baseline algorithm on a withheld device. The worst misclassification rate was a mere 0.08%.

We use machine learning to characterize the state of digital devices based on their analog emissions. As digital devices operate, they emit internal information into a number of analog side channels. Remote sensing of these unintended signals leads to low signal-to-noise-ratio (SNR) and significant clutter. We developed classifiers to determine which program is executing on a digital device based on analog radio-frequency (RF) emissions collected via a 500-MHz Riscure RF probe. A standard algorithm was developed to serve as a baseline program and intrusions were simulated by introducing minor modifications to this program. We collected a thousand RF traces from each of these modified programs running on ten different devices for thousands of instruction cycles. The ten devices tested are representative of the Internet of Things (IoT) devices including Arduino Unos and PIC24 processors. Our primary approach to mitigating the impact of low SNR is to extend the program execution and signal collection time. Collecting a training set with more traces than samples is not practical. Even after down-sampling the raw data to thirty samples per instruction, the number of samples exceeds the number of traces by orders of magnitude. Such a training set nearly guarantees overlearning. To mitigate this, we present our Whitened Mean Classifier as a method to whiten this sparse training set and avoid overlearning. Classification accuracy exceeded 90% for the modified programs on a subset of the ten devices.

Internet of Things (IoT) and other similar devices often have little to no security and thus can be readily exploited in any number of ways. In this work, we collect radio frequency (RF) emissions from simple processors on several IoT devices and apply machine learning techniques to detect modifications (corrupted or injected via malware) in ‘known’ software running on the processor. We can detect these modifications due to the correlation between RF emissions and the digital state of the devices. Every bit flip produces a small but potentially detectable electrical pulse. Our approach to developing the recognition algorithm is to adapt to the variability created by the input data by recognizing the sequences in which instruction blocks are executed. Seemingly minor changes to input values can have a detectable effect on the measured RF side channel. We collect RF data from a variety of IoT devices with clock speeds varying from 16-96 MHz. A 1-GHz Riscure RF near-field antenna probe was placed within a millimeter of the IoT device, RF emissions were acquired, and software controls triggered data collection. A classification architecture was trained using object code portioned into blocks to develop the truth data. We then applied new data to the trained block classifier. This approach detects deviations in individual blocks and block sequences as a whole, allowing a greater level of detection resolution than just binary ‘Yes/No’ classification. Initial testing results showed greater than 90% classification accuracy for block-level modifications, and we can detect deviations from truth data with 100% accuracy.

The Internet of Things (IoT) and Internet of Everything (IoE) has driven the proliferation of processors into nearly every powered device around us: from thermostats to refrigerators to light bulbs. From a security perspective, IoT/IoE creates a new layer of signals and systems that can be exploited to access supporting network layers. Our research focuses on leveraging the analog side channels of IoT/IoE processors, for defensive purposes. We apply signal-processing and machine-learning techniques to collected RF emissions to detect if code running on the processor has been modified (i.e., corrupted or injected with malware). The paper describes our process for positioning a wide-bandwidth RF probe over the device under test (DuT). Classifiers are implemented for identifying the code running on the device. We demonstrate the ability to detect, identify, and isolate instructions based on signatures learned during initial DuT characterization. The probe is positioned to capture RF signals that support-vector machine (SVM) classifiers can accurately discriminate between instructions, rather than relying on raw power leakage. At this well-discriminated location, the signatures of each instruction are extracted by applying principal component analysis (PCA) to separate its signal into components (fetch, opcode, operands, and values). These signatures are used to identify instructions in the test code. Additionally, this paper discusses applying our methodology to blocks of code/algorithms using sequence learning algorithms. These techniques enable significant reduction in feature dimensions improving speed and accuracy of instruction level classification of low-SNR RF sidechannels.