We design CIS-BWE, a novel adversarial Bandwidth Extension (BWE) framework that introduces two chaos-informed discriminators -Multi-Resolution Lyapunov Discriminator (MRLD) and Multi-Scale Detrended Fractal Analysis Discriminator (MSDFA) -for capturing the deterministic chaos from speech.MRLD exploits Lyapunov exponents to capture nonlinear chaotic fluctuations.MSDFA exploits detrended fluctuation analysis to quantify fractal-like, long-range temporal chaotic correlations.To the best of our knowledge, MRLD and MSDFA are included here for the first time with a complex-valued adversarial network to explore the chaotic study of speech reconstruction.We also introduce a novel complex-valued and dual-stream generator, which uses our newly proposed Con-formerNeXt as a core block with Lattice interactions, acting as a gating mechanism by enabling controlled mixing of information across streams.We extensively optimize our design across five resolutions and use depth-wise separable convolutions to make our model lightweight yet powerful.Our CIS-BWE is tested with a de facto English and French dataset for clean and noisy speech for generalization.It achieves better performance across a total of nine subjective and objective evaluation metrics with a 40x reduction in discriminator size and overall 0.5x fewer parameters, establishing a new baseline in the BWE task.

Differential Pressure Sensors are widely deployed to monitor critical environments. However, our research unveils a previously overlooked vulnerability: their high sensitivity to pressure variations makes them susceptible to acoustic side-channel attacks. We demonstrate that the pressure-sensing diaphragms in DPS can inadvertently capture subtle air vibrations caused by speech, which propagate through the sensor’s components and affect the pressure readings. Exploiting this discovery, we introduce BaroVox, a novel attack that reconstructs speech from DPS readings, effectively turning DPS into "a fly on the wall." We model the effect of sound on DPS, exploring the limits and challenges of acoustic leakage. To overcome these challenges, we propose two solutions: a signal-processing approach using a unique spectral subtraction method and a deep learning-based approach for keyword classification. Evaluations under various conditions demonstrate BaroVox’s effectiveness, achieving a word error rate of 0.29 for manual recognition and 90.51% accuracy for automatic recognition. Our findings highlight the significant privacy implications of this vulnerability. We also discuss potential defense strategies to mitigate the risks posed by BaroVox.

Over the last six years, several papers used memory deduplication to trigger various security issues, such as leaking heap-address and causing bit-flip in the physical memory. The most essential requirement for successful memory deduplication is to provide identical copies of a physical page. Recent works use a brute-force approach to create identical copies of a physical page that is an inaccurate and time-consuming primitive from the attacker’s perspective.

A Negative Pressure Room (NPR) is an essential requirement by the Bio-Safety Levels (BSLs) in biolabs or infectious-control hospitals to prevent deadly pathogens from being leaked from the facility. An NPR maintains a negative pressure inside with respect to the outside reference space so that microbes are contained inside of an NPR. Nowadays, differential pressure sensors (DPSs) are utilized by the Building Management Systems (BMSs) to control and monitor the negative pressure in an NPR. This paper demonstrates a non-invasive and stealthy attack on NPRs by spoofing a DPS at its resonant frequency. Our contributions are: (1) We show that DPSs used in NPRs typically have resonant frequencies in the audible range. (2) We use this finding to design malicious music to create resonance in DPSs, resulting in an overshooting in the DPS's normal pressure readings. (3) We show how the resonance in DPSs can fool the BMSs so that the NPR turns its negative pressure to a positive one, causing a potential leak of deadly microbes from NPRs. We do experiments on 8 DPSs from 5 different manufacturers to evaluate their resonant frequencies considering the sampling tube length and find resonance in 6 DPSs. We can achieve a 2.5 Pa change in negative pressure from a ~7 cm distance when a sampling tube is not present and from a ~2.5 cm distance for a 1 m sampling tube length. We also introduce an interval-time variation approach for an adversarial control over the negative pressure and show that the forged pressure can be varied within 12 - 33 Pa. Our attack is also capable of attacking multiple NPRs simultaneously. Moreover, we demonstrate our attack at a real-world NPR located in an anonymous bioresearch facility, which is FDA approved and follows CDC guidelines. We also provide countermeasures to prevent the attack.

Sensors are one of the most pervasive and integral components of today's safety-critical systems. Sensors serve as a bridge between physical quantities and connected systems. The connected systems with sensors blindly believe the sensor as there is no way to authenticate the signal coming from a sensor. This could be an entry point for an attacker. An attacker can inject a fake input signal along with the legitimate signal by using a suitable spoofing technique. As the sensor's transducer is not smart enough to differentiate between a fake and legitimate signal, the injected fake signal eventually can collapse the connected system. This type of attack is known as the transduction attack. Over the last decade, several works have been published to provide a defense against the transduction attack. However, the defenses are proposed on an ad-hoc basis; hence, they are not well-structured. Our work begins to fill this gap by providing a checklist that a defense technique should always follow to be considered as an ideal defense against the transduction attack. We name this checklist as the Golden reference of sensor defense. We provide insights on how this Golden reference can be achieved and argue that sensors should be redesigned from the transducer level to the sensor electronics level. We point out that only hardware or software modification is not enough; instead, a hardware/software (HW/SW) co-design approach is required to ride on this future roadmap to the robust and resilient sensor.

Several papers have been published over the last ten years to provide a defense against intentional spoofing to sensors. However, these defenses would only work against those spoofing signals, which have a separate frequency from the original signal being measured. These defenses would not work if the spoofing attack signal (i) has a frequency equal to the frequency of original signals, (ii) has zero frequency, and (iii) is strong enough to drive the sensor output close to its saturation region. More specifically, these defenses are not designed for a magnetic spoofing attack on passive Hall sensors.

A neuro-cognitive inspired architecture based on the Hierarchical Temporal Memory (HTM) is proposed for anomaly detection and simultaneous data prediction in real-time for smart grid$\mu$PMU data. The key technical idea is that the HTM learns asparse distributed temporal representationof sequential data that turns out to be very useful for anomaly detection and simultaneous data prediction in real-time. Our results show that the proposed HTM can predict anomalies within 83–90 percent accuracy for three different application profiles, namelyStandard, Reward Few False Positive, Reward Few False Negativefor two different datasets. We show that the HTM is competitive to five state-of-the-art algorithms for anomaly detection. Moreover, for the multi-step prediction in the online setting, the same HTM achieves a low 0.0001 normalized mean square error, a low negative log-likelihood score of 1.5 and is also competitive to six state-of-the-art prediction algorithms. We demonstrate that the same HTM model can be used forboth the tasksand can learn online in one-pass, in an unsupervised fashion and adapt to changing statistics.The other state-of-the-art algorithms are either less accurate or are limited to one of the tasks or cannot learn online in one-pass, and adapt to changing statistics.

In cyber-physical additive manufacturing systems, side-channel attacks have been used to reconstruct the G/M-code (which are instructions given to a manufacturing system) of 3D objects being produced. This method is effective for stealing intellectual property from an organization, through least expected means, during prototyping stage before the product goes through a large-scale fabrication and comes out in the market. However, an attacker can be far from being able to completely reconstruct the G/M-code due to lack of enough information leakage through the side-channels. In this paper, we propose a novel way to amplify the information leakage and thus boost the chances of recovery of G/M-code by surreptitiously altering the compiler. By using this compiler, an adversary may easily control various parameters to magnify the leakage of information from a 3D printer while still producing the desired object, thus remaining hidden from the authentic users. This type of attack may be implemented by strong attackers having access to the tool chain and seeking high level of stealth. We have implemented such a compiler and have demonstrated that it increases the success rate of recovering G/M-codes from the four side-channels (acoustic, power, vibration, and electromagnetic) by up to 39 percent compared to previously proposed attacks.

Since 2007, the use of side-channel measurements for detecting Hardware Trojan (HT) has been extensively studied. However, the majority of works either rely on a golden chip, or they rely on methods that are not robust against subtle acceptable changes that would occur over the life-cycle of an integrated circuit (IC). In this paper, we propose using a brain-inspired architecture called Hierarchical Temporal Memory (HTM) for HT detection. Similar to the human brain, our proposed solution is resilient againstnaturalchanges that might happen in the side-channel measurements while being able to accurately detect abnormal behavior of the chip when the HT gets triggered. We use a self-referencing method for HT detection, which eliminates the need for the golden chip. The effectiveness of our approach is evaluated using TrustHub benchmarks, which shows 92.20% detection accuracy on average.

Recent decades have observed the proliferation of sensors in embedded and cyber-physical systems (ECPSs). Sensors are an essential part of embedded and CPSs and serve as a bridge between physical quantities and connected systems. The tight coupling between sensors and systems enables many critical applications where decisions are taken by using the information from various sensors at different time-scales. This tight coupling opens the “Pandora's Box” of unknown threats that could come from very unconventional ways. An unconventional attack model could be to noninvasively attack sensors using forged spoofing signals and trigger unwanted behavior in connected systems. This paper introduces this type of new, strong, and unorthodox attack model and elaborates how important this will be in the near future when sensors will pervade our lives. Moreover, this paper presents a motivational example of a sensor-spoofing attack on Hall sensors in the context of smart grids to demonstrate the harmful consequences of this type of attack in ECPSs.