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Spoofing a passive Hall sensor with fake magnetic fields can inject false data into the downstream of connected systems. Several works have tried to provide a defense against the intentional spoofing to different sensors over the last six years. However, they either only work on active sensors or against externally injected unwanted weak signals (e.g., EMIs, acoustics, ultrasound, etc.), which can only spoof sensor output in its linear region. However, they do not work against a strong magnetic spoofing attack that can drive the passive Hall sensor output in its saturation region. We name this as the saturation attack. In the saturation region, the output gets flattened, and no information can be retrieved, resulting in a denial-of-service attack on the sensor. Our work begins to fill this gap by providing a defense named PreMSat against the saturation attack on passive Hall sensors. The core idea behind PreMSat is that it cangenerate an internal magnetic field having the same strength but in opposite polarity to external magnetic fields injected by an attacker. Therefore, the generated internal magnetic field by PreMSat can nullify the injected external field while preventing: (i) intentional spoofing in the sensor’s linear region, and (ii) saturation attack in the saturation region. PreMSat integrates a low-resistance magnetic path to collect the injected external magnetic fields and utilizes a finely tuned PID controller to nullify the external fields in real-time. PreMSat can prevent the magnetic saturation attack having a strength up to ∼4200 A-t within a frequency range of 0 Hz–30 kHz with low cost (∼$14), whereas the existing works cannot prevent saturation attacks with any strength. Moreover, it works against saturation attacks originating from any type, such as constant, sinusoidal, and pulsating magnetic fields. We did over 300 experiments on ten different industry-used Hall sensors from four different manufacturers to prove the efficacy of PreMSat and found that the correlation coefficient between the signals before the attack and after the attack is greater than 0.94 in every test case. Moreover, we create a prototype of PreMSat and evaluate its performance in a practical system — a grid-tied solar inverter. We find that PreMSat can satisfactorily prevent the saturation attack on passive Hall sensors in real-time.

This paper presents a novel multi-modal sabotage attack detection system for Additive Manufacturing (AM) machines. By utilizing multiple side-channels, we improve system state estimation significantly in comparison to uni-modal techniques. Besides, we analyze the value of each side-channel for performing attack detection in terms of mutual information shared with the machine control parameters. We evaluate our system on real-world test cases and achieve an attack detection accuracy of 98.15%. AM, or 3D Printing, is seeing practical use for the rapid prototyping and production of industrial parts. The digitization of such systems not only makes AM a crucial technology in Industry 4.0 but also presents a broad attack surface that is vulnerable to kinetic cyberattacks. In the field of AM security, sabotage attacks are cyberattacks that introduce inconspicuous defects to a manufactured component at any specific process of the AM digital process chain, resulting in the compromise of the component's structural integrity and load-bearing capabilities. Defense mechanisms that detect such attacks using side-channel analysis have been studied. However, most current works focus on modeling the state of AM systems using a single side-channel, thus limiting their effectiveness at attack detection. In this paper, we demonstrate the value of a multi-modal sabotage attack detection system in comparison to uni-modal techniques.

The present work describes the phenomenological approach to automatically determine the frequency range for positive and negative dielectrophoresis (DEP)—an electrokinetic force that can be used for massively parallel micro- and nano-assembly. An experimental setup consists of the microfabricated chip with gold microelectrode array connected to a function generator capable of digitally controlling an AC signal of 1 V (peak-to-peak) and of various frequencies in the range between 10 kHz and 1 MHz. The suspension of latex microbeads (3-μm diameter) is either attracted or repelled from the microelectrodes under the influence of DEP force as a function of the applied frequency. The video of the bead movement is captured via a digital camera attached to the microscope. The OpenCV software package is used to digitally analyze the images and identify the beads. Positions of the identified beads are compared for successive frames via Artificial Intelligence (AI) algorithm that determines the cloud behavior of the microbeads and algorithmically determines if the beads experience attraction or repulsion from the electrodes. Based on the determined behavior of the beads, algorithm will either increase or decrease the applied frequency and implement the digital command of the function generator that is controlled by the computer. Thus, the operation of the study platform is fully automated. The AI-guided platform has determined that positive DEP (pDEP) is active below 500 kHz frequency, negative DEP (nDEP) is evidenced above 1 MHz frequency and the crossover frequency is between 500 kHz and 1 MHz. These results are in line with previously published experimentally determined frequency-dependent DEP behavior of the latex microbeads. The phenomenological approach assisted by live AI-guided feedback loop described in the present study will assist the active manipulation of the system towards the desired phenomenological outcome such as, for example, collection of the particles at the electrodes, even if, due to the complexity and plurality of the interactive forces, model-based predictions are not available.

Power grid design and maintenance are conducted to solve the problems caused by load growth over time and to stay within the constraints of voltage drop, power factor, etc. Typically, solutions to these problems are optimised individually. Considering multiple problems simultaneously and applying different solutions require vast design space exploration. This exclusively needs advanced algorithms and complex global optimisation methods which are not easily-applicable in different scenarios. In the state-of-the-art methods, for solving multiple problems simultaneously, these individually optimised solutions are applied sequentially to the power grid. In this so-called uncoordinated method, the final solution may not be optimal solution considering all the variables, since it is considering the overlapping effect of the solutions on the power grid. To validate the compartmentalisation method, a detailed distribution grid has been modeled. After analysing the possible solutions and optimisation, power loss was reduced 45% and total cost decreased by 71%, compared to the uncoordinated method.