Noise Resilient Learning for Attack Detection in Smart Grid Pmu Infrastructure

Falsified data from compromised Phasor Measurement Units (PMUs) in a smart grid induce Energy Management Systems (EMS) to have an inaccurate estimation of the state of the grid, disrupting various operations of the power grid. Moreover, the PMUs deployed at the distribution layer of a smart grid show dynamic fluctuations in their data streams, which make it extremely challenging to design effective learning frameworks for anomaly-based attack detection. In this paper, we propose a noise resilient learning framework for anomaly-based attack detection specifically for distribution layer PMU infrastructure, that show real time indicators of data falsifications attacks while offsetting the effect of false alarms caused by the noise. Specifically, we propose a feature extraction framework that uses some Pythagorean Means of the active power from a cluster of PMUs, reducing multi-dimensional nature of the PMU data streams via quick big data summarization. We also propose a robust and noise resilient methodology for learning thresholds based on generalized robust estimation theory of our invariant feature. We experimentally validate our approach and demonstrate improved reliability performance using two completely different datasets collected from real distribution level PMU infrastructures

Active Learning Augmented Folded Gaussian Model for Anomaly Detection in Smart Transportation

Smart transportation networks have become instrumental in smart city applications with the potential to enhance road safety, improve the traffic management system and driving experience. A Traffic Message Channel (TMC) is an IoT device that records the data collected from the vehicles and forwards it to the Roadside Units (RSUs). This data is further processed and shared with the vehicles to inquire the fastest route and incidents that can cause significant delays. The failure of the TMC sensors can have adverse effects on the transportation network. In this paper, we propose a Gaussian distribution-based trust scoring model to identify anomalous TMC devices. Then we propose a semi-supervised active learning approach that reduces the manual labeling cost to determine the threshold to classify the honest and malicious devices. Extensive simulation results using real-world vehicular data from Nashville are provided to verify the accuracy of the proposed method

Specialized odorant receptors in social insects that detect cuticular hydrocarbon cues and candidate pheromones.

Eusocial insects use cuticular hydrocarbons as components of pheromones that mediate social behaviours, such as caste and nestmate recognition, and regulation of reproduction. In ants such as Harpegnathos saltator, the queen produces a pheromone which suppresses the development of workers' ovaries and if she is removed, workers can transition to a reproductive state known as gamergate. Here we functionally characterize a subfamily of odorant receptors (Ors) with a nine-exon gene structure that have undergone a massive expansion in ants and other eusocial insects. We deorphanize 22 representative members and find they can detect cuticular hydrocarbons from different ant castes, with one (HsOr263) that responds strongly to gamergate extract and a candidate queen pheromone component. After systematic testing with a diverse panel of hydrocarbons, we find that most Harpegnathos saltator Ors are narrowly tuned, suggesting that several receptors must contribute to detection and discrimination of different cuticular hydrocarbons important in mediating eusocial behaviour.Cuticular hydrocarbons (CHC) mediate the interactions between individuals in eusocial insects, but the sensory receptors for CHCs are unclear. Here the authors show that in ants such as H. saltator, the 9-exon subfamily of odorant receptors (HsOrs) responds to CHCs, and ectopic expression of HsOrs in Drosophila neurons imparts responsiveness to CHCs

A Comparative Study of Hooks in the Ya Rns Produced by Different Spinning Technologies

This article presents a comparative study of hooks’ characteristics of ring, rotor, air-jet and open-end friction spun yarns. Hook types and their extent, spinning in-coefficient and mean fibre extent in the yarns produced on different spinning technologies are investigated. The results show that the hook extents for open-end friction spun yarn are the highest followed by rotor, ring and air-jet spun yarns. Ring and air-jet spun yarns have higher percentage and extent of trailing hook as compared with leading hook, whereas, rotor and friction spun yarns show the reverse trend

Real Time Stream Mining based Attack Detection in Distribution Level PMUs for Smart Grids

Reliable automation of smart grids depends on decisions based on situational awareness extracted via real time system monitoring and accurate state estimation. The Phasor Measurement Units (PMU) at distribution and transmission layers of the smart grid provide high velocity real time information on voltage and current magnitudes and angles in a three phase electrical grid. Naturally, the authenticity of the PMU data is of utmost operational importance. Data falsification attacks on PMU data can cause the Energy Management Systems (EMS) to take wrong decisions, potentially having drastic consequences on the power grid\u27s operation. The need for an automated data falsification attack detection and isolation is key for EMS protection from PMU data falsification. In this paper, we propose an automated distributed stream mining approach to time series anomaly based attack detection that identifies attacks while distinguishing from legitimate changes in PMU data trends. Specifically, we provide a real time learning invariant that reduces the multi-dimensional nature of the PMU data streams for quick big data summarization using a Pythagorean means of the active power from a cluster of PMUs. Thereafter, we propose a methodology that learns thresholds of the invariant automatically, to prove the predictive power of distinguishing between small attacks versus legitimate changes. Extensive simulation results using real PMU data are provided to verify the accuracy of the proposed method

Resilience Against Bad Mouthing Attacks in Mobile Crowdsensing Systems via Cyber Deception

Mobile Crowdsensing System (MCS) applications deploy rating feedback mechanisms to help quantify the trustworthiness of published events which over time improve decision accuracy and establish user reputation. In this paper, we first show that factors such as sparseness, inherent error probabilities of rating feedback labelers, and prior knowledge of the event trust scoring models, can be used by strategic adversaries to hijack the feedback labeling mechanism itself with bad mouthing attacks. Then, we propose a randomized rating sub-sampling technique inspired from moving target defense and cyber deception to mitigate the degradation in the resulting event trust scores of truthful events. We offer a game theoretic strategy under various knowledge levels of an adversary and the MCS in regards to picking an optimal sub-sample size for bad mouthing attacks and event trust calculations respectively, by using a vehicular crowdsensing as a proof-of-concept

Episodes of acute respiratory deterioration in patients with idiopathic pulmonary fibrosis in the UK – mortality and contributing factors

Episodes of acute respiratory deterioration (ARD) in idiopathic pulmonary fibrosis are associated with significant morbidity and mortality. The incidence and potential associated risk factors for these episodes remains unknown. This large retrospective study provides an estimate of the incidence, mortality and factors associated with these episodes in patients with IPF in the UK. Linked routinely-collected primary care data on 5,276 patients diagnosed with IPF between 2000 and 2014 were used. Incidence and mortality of ARD episodes were estimated using Poisson and Cox regression methods respectively. 4.2% of patients with IPF had an ARD episode over the study period. The overall incident rate was 11.6 per 10,000 patient years (95%CI 10.1-13.2). The overall year-on-year increase in mortality was ~39% (rate ratio 1.39, 95% CI 1.21-1.60). Age, COPD, death and hospital admission were significantly associated with incidence of ARD. The number of exacerbation episodes i.e. one, two and three exacerbations and hospital admission were associated with increased mortality. Episodes of ARD in patients with IPF are significantly associated with mortality. Mortality is also associated with male sex and increasing age. The overall incidence of episodes of ARD in this large study increased between 2000 and 2014 while survival got worse. COPD was an associated risk factor of ARD. The findings of this study are consistent with clinical experience but do require a further prospective validation cohort study

Influence Spread Control in Complex Networks via Removal of Feed Forward Loops

Selective removal of certain subgraphs called motifs based on the spread function value is one of the most powerful approaches to curb the overall influence spread in any complex network. In this paper, we first prove that any general spread function preserves both monotonicity and submodularity properties even under motif removal operations. Next, we propose a scoring mechanism as a novel spread function that quantifies the relative importance of a given motif within the overall influence spread dynamics on the complex network. We design a novel algorithm that eliminates motifs with high spread scores to curb influence spread. We evaluate the performance of our proposed spread control algorithm using simulation experiments in the context of 3-node motifs called feed forward loops (FFLs) in both real and synthetic network topologies. We demonstrate that high-scoring motifs intercept a high number of short paths from the pre-assigned source and sinks, because of which their elimination results in a significant effect on curbing the influence spread. Furthermore, we empirically evaluate the run-time and cost versus performance trade-off of the proposed algorithm