Fatal attraction: identifying mobile devices through electromagnetic emissions

Smartphones are increasingly augmented with sensors for a variety of purposes. In this paper, we show how magnetic field emissions can be used to fingerprint smartphones. Previous work on identification rely on specific characteristics that vary with the settings and components available on a device. This limits the number of devices on which one approach is effective. By contrast, all electronic devices emit a magnetic field which is accessible either through the API or measured through an external device. We conducted an in-the-wild study over four months and collected mobile sensor data from 175 devices. In our experiments we observed that the electromagnetic field measured by the magnetometer identifies devices with an accuracy of 98.9%. Furthermore, we show that even if the sensor was removed from the device or access to it was discontinued, identification would still be possible from a secondary device in close proximity to the target. Our findings suggest that the magnetic field emitted by smartphones is unique and fingerprinting devices based on this feature can be performed without the knowledge or cooperation of users

iPTF15eqv: Multi-wavelength Expos\'e of a Peculiar Calcium-rich Transient

The progenitor systems of the class of "Ca-rich transients" is a key open issue in time domain astrophysics. These intriguing objects exhibit unusually strong calcium line emissions months after explosion, fall within an intermediate luminosity range, are often found at large projected distances from their host galaxies, and may play a vital role in enriching galaxies and the intergalactic medium. Here we present multi-wavelength observations of iPTF15eqv in NGC 3430, which exhibits a unique combination of properties that bridge those observed in Ca-rich transients and Type Ib/c supernovae. iPTF15eqv has among the highest [Ca II]/[O I] emission line ratios observed to date, yet is more luminous and decays more slowly than other Ca-rich transients. Optical and near-infrared photometry and spectroscopy reveal signatures consistent with the supernova explosion of a < 10 solar mass star that was stripped of its H-rich envelope via binary interaction. Distinct chemical abundances and ejecta kinematics suggest that the core collapse occurred through electron capture processes. Deep limits on possible radio emission made with the Jansky Very Large Array imply a clean environment ($n <$ 0.1 cm$^{-3}$) within a radius of $\sim 10^{17}$ cm. Chandra X-ray Observatory observations rule out alternative scenarios involving tidal disruption of a white dwarf by a black hole, for masses > 100 solar masses). Our results challenge the notion that spectroscopically classified Ca-rich transients only originate from white dwarf progenitor systems, complicate the view that they are all associated with large ejection velocities, and indicate that their chemical abundances may vary widely between events.Comment: 24 pages, 16 figures. Closely matches version published in The Astrophysical Journa

Radio Frequency Based Programmable Logic Controller Anomaly Detection

The research goal involved developing improved methods for securing Programmable Logic Controller (PLC) devices against unauthorized entry and mitigating the risk of Supervisory Control and Data Acquisition (SCADA) attack by detecting malicious software and/or trojan hardware. A Correlation Based Anomaly Detection (CBAD) process was developed to enable 1) software anomaly detection discriminating between various operating conditions to detect malfunctioning or malicious software, firmware, etc., and 2) hardware component discrimination discriminating between various hardware components to detect malfunctioning or counterfeit, trojan, etc., components

Detecting Impersonation Attacks in a Static WSN

The current state of security found in the IoT domain is highly flawed, a major problem being that the cryptographic keys used for authentication can be easily extracted and thus enable a myriad of impersonation attacks. In this MSc thesis a study is done of an authentication mechanism called device fingerprinting. It is a mechanism which can derive the identity of a device without relying on device identity credentials and thus detect credential-based impersonation attacks. A proof of concept has been produced to showcase how a fingerprinting system can be designed to function in a resource constrained IoT environment. A novel approach has been taken where several fingerprinting techniques have been combined through machine learning to improve the system’s ability to deduce the identity of a device. The proof of concept yields high performant results, indicating that fingerprinting techniques are a viable approach to achieve security in an IoT system

PLC Hardware Discrimination using RF-DNA fingerprinting

Programmable Logic Controllers are used to control and monitor automated process in many Supervisory Control and Data Acquisition (SCADA) critical applications. As with virtually all electronic devices, PLCs contain Integrated Circuits (IC) that are often manufactured overseas. ICs that have been unknowingly altered (counterfeited, manufactured with hardware Trojans, etc.) pose a significant security vulnerability. To mitigate this risk, the RF-Distinct Native Attribute (RF-DNA) fingerprinting process is applied to PLC hardware devices to augment bit-level security. RF-DNA fingerprints are generated using two independent signal collection platforms. Two different classifiers are applied for device classification. A verification process is implemented for analysis of Authorized Device Identification and Rogue Device Rejection. Fingerprint feature dimensional reduction is evaluated both Qualitatively and Quantitatively to enhance experimental-to-operational transition potential. The findings of this research are that the higher quality signal collection platform had a classification performance gain of approximately 10dB SNR. Performance of the classifiers varied between signal collection platforms, and also with the application of fingerprint dimensional reduction. The lower quality signal collection platform saw a maximum gain of 5dB SNR using reduced dimensional feature sets compared against the full dimensional feature set

Exploitation of Unintentional Ethernet Cable Emissions Using Constellation Based-Distinct Native Attribute (CB-DNA) Fingerprints to Enhance Network Security

This research contributed to the AFIT\u27s Radio Frequency Intelligence (RFINT) program by developing a new device discrimination technique called Constellation-Based Distinct Native Attribute (CB-DNA) Fingerprinting. This is of great interest to the Air Force Research Lab (AFRL), Sensor Directorate, who supported the research and now have new method for improving network security. CB-DNA fingerprints are used to authenticate wired network device identities, thwart unauthorized access, and augment traditional bit-level security measures that area easily bypassed by skilled hackers. Similar to human fingerprint features that uniquely identify individuals, CB-DNA uniquely identifies communication devices and improves the rate at which unauthorized rogue devices are granted network access