Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Micro-Moment Classification for Anomalous Power Consumption Detection using 1D CNN(Institute of Electrical and Electronics Engineers Inc., 2023) Nayak, R.; Jaidhar, C.D.Identifying anomalous power consumption is essential in improving energy efficiency in buildings. With the help of sensors and other intelligent systems installed in buildings (including smart homes), identifying anomalous power consumption becomes easy. In this work, 1 Dimensional Convolutional Neural Network (1D CNN)-based classification model is proposed to classify the micro-moments to identify the anomalous power consumption in the presence and absence of the consumer. The SimDataset values are normalized, and each instance with ten features is given as input to the 1D CNN. The robustness of the proposed model is defined by experimenting with varying the hyperparameter to obtain the best performance in the standard performance evaluation metrics. The results depicted that the suggested model outperformed the state-of-the-art, producing an accuracy of 96.4% and a weighted average F1-score of 0.962. © 2023 IEEE.Item Classification of Micro-Moment-Based Anomalous Power Consumption Using Transfer Learning(Institute of Electrical and Electronics Engineers Inc., 2023) Nayak, R.; Jaidhar, C.D.The identification of unusual power usage in buildings is crucial for improving energy efficiency. Using an electrical consumption monitoring system can help with energy conservation by identifying unusual energy consumption patterns. This paper suggests a micro-moment-based methodology for detecting abnormal power use. This study makes use of a benchmark dataset called SimDataset, which is used in most of the micro-moment classification-related works. On the images created from the dataset labeled with two classes and five classes, binary and multi-class classifications have both been used. Transfer learning is used by employing pre-trained CNN models, namely DenseNet121, ResNet50V2, and Xception model. The results depicted that the DenseNet121 model has outperformed all other models by giving the best accuracy of 99% and F1-score of 0.984. © 2023 IEEE.Item Anomaly Detection in Electric Powertrain System Software Behaviour(Institute of Electrical and Electronics Engineers Inc., 2023) Vyas, A.; Ghorpade, V.; Kamble, S.; Johnson, P.S.; Kamath, A.; Rawat, K.A software-in-loop (SIL) testing is a method of early testing of control software of a car in virtual environment. A system level testing is carried out on regular basis and it is important to see, if system is behaving as expected or unexpected. For unexpected behaviors, which test engineers not easily notice, modern techniques such as machine learning can give an advantage. This paper presents an application of machine learning algorithms that helps in identifying the abnormal patterns in time series data generated from electric powertrain system testing done in SIL environment for a Mercedes Benz Electric Car. Output of the SIL testing, results in time series data that is a collection of observations that are ordered chronologically and can be used to analyze trends, patterns, and changes over time. Anomaly detection in time series data is a process in machine learning that identifies data points, events, and observations that deviate from a dataset's normal behavior. By monitoring the expected and unexpected behavior of the electric powertrain system, anomaly detection can be a valuable tool for identifying potential issues. This study aims at coming up with an efficient process for anomaly detection in SIL. In order to get this process, various anomaly detection techniques are compared to detect a defined anomaly in time series data. Data pre-processing methods are also discussed before training the model. At the end, we conclude a best-fit method for identified anomaly. With finally identified method, a model was trained and used further in application. © 2023 IEEE.Item Smart Appliance Abnormal Electrical Power Consumption Detection(Springer Science and Business Media Deutschland GmbH, 2024) Nayak, R.; Jaidhar, C.D.Potential cyber threats now have an immensely larger attack surface due to the widespread use of smart devices and smart environments. Smart home appliances build a network of linked objects that exchange information and communicate with each other. Detecting abnormal electrical power consumption becomes a first line of protection for bolstering the security of smart homes. Using Machine Learning (ML), anomalous electrical power consumption of the Smart Appliance can be identified. This work proposes an ML-based anomalous electrical power consumption detection to identify the security breach of the Smart Appliances. SimDataset is used for anomalous power consumption detection as a proof of concept for experimentation, and results depicted that Random Forest (RF) classifier outperformed other ML-based classifiers while detecting the abnormal electrical power usage. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.Item Enhancing Big Data Security Through Anomaly Detection(Institute of Electrical and Electronics Engineers Inc., 2024) Vakkund, S.; Kumar, S.; Rao, S.; Anusha Hegde, H.; Bhowmik, B.Securing the massive and fast-moving data streams typical in Big Data environments presents unique challenges that traditional static security measures simply can't handle. To effectively protect these data flows, we need methods that can analyze traffic in real-time and respond swiftly to potential threats. Anomaly detection is one such method, offering an automated way to identify unusual or suspicious activities within Big Data systems. In this study, we explore several widely-used anomaly detection algorithms, evaluating their effectiveness in identifying anomalies within large datasets. Specifically, we will assess these algorithms using the UNSW-NB15 Dataset, aiming to pinpoint which algorithm, or combination of algorithms, is best suited for the demands of Big Data security. © 2024 IEEE.Item Experimental Study on Impact of Appliance ID-Based Normalization on SimDataset for Anomalous Power Consumption Classification(Institute of Electrical and Electronics Engineers Inc., 2024) Nayak, R.; Jaidhar, C.D.In terms of annual worldwide energy consumption, buildings use more energy than any other sector. Enhancing buildings' energy efficiency and ensuring security of the appliances requires iden-tifying abnormal power usage. Identifying anomalous power usage is essential for energy conservation. This study suggests an experimental analysis of SimDataset used for detecting micro-moment-based abnormal power usage. Five machine learning-based classifiers-Random Forest (RF), Support Vector Ma-chine (SVM), K Nearest Neighbors (KNN), Naive Bayes (NB), and Decision Tree (DT)-are used to detect unusual consumption of electricity. The Sim-Dataset has undergone binary and multi-class classi-fication. Effect on the performance of the classifiers after the inclusion of new features is examined. Computational complexity of the classifiers is also analyzed. Experimental results showed, the binary and multi-class classification using the RF model with the original dataset, with Min-Max Normalized Power feature and Appliance Id-based Normalized Power feature, produced identical and maximum accuracy, precision, recall, and F1-Score. © 2024 IEEE.