[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119622-en":3,"doc-seo-119622-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119622,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","Quantum Machine Learning for Anomaly Detection in Consumer Electronics - Slides","Anomaly detection is central to cyber security as emerging cyber-physical threats such as network intrusion, financial fraud, identity theft, and property invasion continue to evolve. Classical machine learning methods struggle to keep pace with frequently appearing new anomaly types, motivating Quantum Machine Learning (QML) as an efficient computational approach. The work presents QML principles for anomaly detection in consumer electronics, including a generic framework for deploying QML algorithms and an overview of supervised, unsupervised, and reinforcement-learning-based variants, supported by five case studies.","Quantum Machine Learning for Anomaly Detection  \nin Consumer Electronics  \nSounak Bhowmik  \nDept. of Electrical Engineering and Computer Science  \nUniversity of Tennessee, Knoxville Tennessee, USA  \n[sbhowmi2@vols.utk.edu](sbhowmi2@vols.utk.edu)  \nHimanshu Thapliyal  \nDept. of Electrical Engineering and Computer Science  \nUniversity of Tennessee, Knoxville Tennessee, USA  \n[hthapliyal@utk.edu](hthapliyal@utk.edu)  \narXiv :2409 .00294v1 [ quant-ph] 30 Aug 2024  \nAbstract—Anomaly detection is a crucial task in cyber security. Technological advancement brings new cyber-physical threats like network intrusion, financial fraud, identity theft, and property invasion. In the rapidly changing world, with frequently emerging new types of anomalies, classical machine learning models are insufficient to prevent all the threats. Quantum Machine Learning (QML) is emerging as a powerful computational tool that can detect anomalies more efficiently. In this work, we have introduced QML and its applications for anomaly detection in consumer electronics. We have shown a generic framework for applying QML algorithms in anomaly detection tasks. We have also briefly discussed popular supervised, unsupervised, and reinforcement learning-based QML algorithms and included five case studies of recent works to show their applications in anomaly detection in the consumer electronics field.  \nIndex Terms—Quantum machine learning (QML), Anomaly Detection, Consumer electronics, variational quantum circuit, Quantum kernel, supervised QML, Unsupervised QML, General framework of QML.  \nI. INTRODUCTION  \nAnomaly detection in consumer electronics refers to identifying irregular patterns that deviate from the normal functioning of devices we use daily. These anomalies can range from minor software glitches to significant security vulnerabilities. Consumer electronics, particularly IoT (Internet of Things) devices, are integral to our everyday lives, yet they are susceptible to various disruptions and cyber-attacks. For instance, an irregularity in a smart home system might compromise the security of an entire household. Just as anomalies in network traffic could signal security threats and unexpected patterns in medical scans might indicate health issues, irregularities in consumer electronics can have profound implications, from data breaches to complete system failures. Anomalies are everywhere, from credit card transactions to space-craft data and thermal power stations to our daily email services. Hence, to safeguard against these threats, Anomaly Detection Systems (ADS) is crucial and widely implemented across numerous sectors within the consumer electronics industry [1], such as financial sectors (e.g., fraud detection), medical (e.g., disease diagnosis), surveillance (e.g., theft, robbery, property invasion), and cyber security (e.g., malware and network intrusion) . Over the years, researchers have used ideas from statistics, machine learning, information theory, and spectral analysis to solve anomaly detection problems. Classical machine learning  \nalgorithms like clustering, one-class Support Vector Machines (SVMs), Decision Trees, and Neural Networks have been used successfully to build ADS. However, these algorithms are very resource-intensive and take a long time to train. They also suffer from over-fitting problems and find it challenging to adapt to new anomalies that could be introduced during their functional operation. Therefore, to find a better solution, many researchers are inclined towards a new discipline, Quantum Machine Learning (QML), that combines the power of quantum computing, quantum information, and machine learning algorithms.  \nQuantum machine learning has been deployed for consumer electronics applications such as credit card fraud detection, anomaly detection in surveillance, health care anomaly detection, and crime prevention. Though literature surveys on anomaly detection using QML algorithms exist [2], there is no such comp","cbCailqhztp6v37i","https://ap.wps.com/l/cbCailqhztp6v37i","pdf",1035402,1,7,"English","en",105,"# Introduction\n## Motivation and problem context\n## Limits of classical machine learning\n## Goals and contributions\n# Background on Quantum Machine Learning\n## QML algorithms for anomaly detection","[{\"question\":\"What problem does the paper address in consumer electronics?\",\"answer\":\"It addresses anomaly detection in consumer electronics, where irregular patterns can indicate anything from minor glitches to major security vulnerabilities and system failures.\"},{\"question\":\"Why are classical machine learning models considered insufficient?\",\"answer\":\"They are resource-intensive, slow to train, suffer from overfitting, and have difficulty adapting when new anomaly types appear during deployment.\"},{\"question\":\"What contributions does the proposed work make?\",\"answer\":\"It introduces a generic QML framework for anomaly detection, reviews multiple emerging QML algorithm families, and provides five case studies illustrating QML applications across consumer-electronics-relevant domains.\"}]","Quantum Machine Learning for Anomaly Detection in Consumer Electronics - 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