Anomadarshi Barua

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 μPMU data. The key technical idea is that the HTM learns a sparse distributed temporal representation of 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% accuracy for three different application profiles, namely Standard, Reward Few False Positive, Reward Few False Negative for 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 ...

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 t...

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 im...

Grid-tied solar inverters continue to proliferate rapidly to tackle the growing environmental challenges. Nowadays, different smart sensors and transducers are tightly integrated with the grid-tied inverter. This integration opens the "Pandora’s Box" of unknown threats that could come from very unconventional ways. This paper demonstrates a noninvasive attack that could come by spoofing the Hall sensor of an inverter in a stealthy way by using an external magnetic field. We demonstrate how an attacker can camouflage his/her attack tool and place it near a target inverter. In doing so, he/she can intentionally perturb grid voltage and frequency and can inject false real and reactive power to the grid. We also show the consequences of the attack on a scaled-down testbed of a power grid with a commercial 140 W grid-tied inverter from Texas Instruments. We are able to achieve a 31.52% change in output voltage, 3.16x (-6dB to -11dB) increase in lowfrequency harmonics power, and...

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 against natural changes 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.

Micro-phasor measurement unit (μPMU) sensors in smart electric grids provide measurements of voltage and current at microsecond timescale across the network and have great potential value for grid diagnostics. In this work, we propose a novel neuro-cognitive inspired architecture based on Hierarchical Temporal Memory (HTM) for real-time anomaly detection in smart grid using μPMU data. The key technical idea is that the HTM learns a sparse distributed temporal representation of sequential data that turns out to be very useful for anomaly detection in real-time.Our numerical results show that the proposed HTM architecture can predict anomalies with 96%, 96%, and 98% accuracy for three different application profiles namely, Standard, Reward Few False Positive, Reward Few False Negative, respectively. The performance is compared with three state-of-the-art real-time anomaly detection algorithms and HTM demonstrates competitive score for real-time anomaly detection in μPMU data.