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

Response to Modified Antitubercular Drug Regime and Antiretroviral Therapy in a Case of HIV Infection with Disseminated Tuberculosis with Isoniazid Induced Toxic Epidermal Necrolysis

Toxic epidermal necrolysis (TEN) is a potentially life-threatening disorder characterized by widespread erythema, necrosis, and bullous detachment of the epidermis and mucous membranes. Without proper management,TEN can cause sepsis leading to death of the patient. Though TEN is commonly drug induced, Isoniazid (INH) has been uncommonly associated with TEN. As INH is one of the first line drugs in treatment of tuberculosis, TEN induced INH needs modification of antitubercular therapy (ATT) with withdrawal of INH from the treatment regime along with other supportive treatments. Patients with HIV infection and disseminated tuberculosis need to be urgently initiated on an effective ATT on diagnosis of tuberculosis. However, if the patient develops potential life-threatening toxicity to first line antitubercular drugs like INH, an alternative effective ATT combination needs to be started as soon as the condition of the patient stabilizes as most of these patients present in advanced stage of HIV infection and this is to be followed by antiretroviral therapy (ART) as per guidelines. The present case reports the effectiveness of an ATT regime comprising Rifampicin, Pyrazinamide, Ethambutol, and Levofloxacin along with ART in situations where INH cannot be given in disseminated tuberculosis in HIV patients

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