Building Trust in Smart TVs: AI-Enhanced Cybersecurity for User Privacy and Ethical Monetization
محفوظ في:
| الحاوية / القاعدة: | European Conference on Cyber Warfare and Security (Jun 2025), p. 647-656 |
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| المؤلف الرئيسي: | |
| مؤلفون آخرون: | , , |
| منشور في: |
Academic Conferences International Limited
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| الموضوعات: | |
| الوصول للمادة أونلاين: | Citation/Abstract Full Text Full Text - PDF |
| الوسوم: |
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| 100 | 1 | |a Singh, Nakul |u AI and Cybersecurity Practitioner, San Francisco, USA | |
| 245 | 1 | |a Building Trust in Smart TVs: AI-Enhanced Cybersecurity for User Privacy and Ethical Monetization | |
| 260 | |b Academic Conferences International Limited |c Jun 2025 | ||
| 513 | |a Conference Proceedings | ||
| 520 | 3 | |a As Smart TVs evolve into central hubs for IoT ecosystems, ensuring user trust through robust cybersecurity and ethical monetization practices has become paramount. This paper explores the integration of AI-driven cybersecurity features into Smart TVs, enabling them to safeguard user privacy and secure connected devices such as thermostats, smart speakers and home automation systems. By leveraging advanced AI techniques, including anomaly detection, behavioral analytics and federated learning, Smart TVs can monitor network traffic, detect vulnerabilities and mitigate potential cyber threats in real-time. For example, these systems can proactively identify and block IoT-based botnet attacks like Mirai, preventing unauthorized access to home networks. Additionally, AI-driven device typing enables Smart TVs to accurately classify and optimize the performance of connected devices, enhancing interoperability and user experience. The transformation of Smart TVs into trusted IoT hubs also presents significant monetization opportunities for manufacturers. Ethical monetization strategies, such as offering premium AI-powered security subscriptions, personalized automation services and bundled IoT device packages can generate revenue while prioritizing user trust. Privacy-preserving AI techniques such as federated learning and edge computing ensure that insights are monetized without collecting raw user data. Cross-selling and upselling opportunities arise as manufacturers integrate Smart TVs with complementary smart home products, fostering a seamless, secure ecosystem. Additionally, partnerships with cybersecurity firms and IoT developers further expand revenue streams, ensuring sustainable growth. Unlike traditional IoT security solutions, AI-powered Smart TVs provide native, real-time protection without requiring additional hardware, positioning them as the next frontier in home cybersecurity. As the industry advances, embedding privacy by design principles and offering users greater control over their data will be crucial in maintaining trust. This paper highlights how AI-enhanced cybersecurity and responsible monetization can redefine Smart TVs as both intelligent home automation hubs and ethical revenue generators, ensuring security, privacy, and user satisfaction while driving industry growth. | |
| 653 | |a Subscriptions | ||
| 653 | |a Behavior | ||
| 653 | |a Interoperability | ||
| 653 | |a Internet of Things | ||
| 653 | |a User experience | ||
| 653 | |a Threats | ||
| 653 | |a Trends | ||
| 653 | |a Communications traffic | ||
| 653 | |a Edge computing | ||
| 653 | |a Cybersecurity | ||
| 653 | |a Smart buildings | ||
| 653 | |a Malware | ||
| 653 | |a Automation | ||
| 653 | |a Privacy | ||
| 653 | |a Ethics | ||
| 653 | |a Ecosystems | ||
| 653 | |a Use statistics | ||
| 653 | |a Hubs | ||
| 653 | |a Monetization | ||
| 653 | |a Streaming services | ||
| 653 | |a User satisfaction | ||
| 653 | |a Smart houses | ||
| 653 | |a Connectivity | ||
| 653 | |a Household goods | ||
| 653 | |a Anomalies | ||
| 653 | |a Advertising | ||
| 653 | |a Streaming media | ||
| 653 | |a Trustworthiness | ||
| 653 | |a Real time | ||
| 653 | |a Federated learning | ||
| 653 | |a Security systems | ||
| 653 | |a Learning | ||
| 653 | |a Unauthorized | ||
| 653 | |a Keyboarding | ||
| 653 | |a Security | ||
| 653 | |a Revenue | ||
| 653 | |a Intelligence | ||
| 653 | |a Positioning | ||
| 653 | |a Trust | ||
| 653 | |a Transformation | ||
| 653 | |a Prioritizing | ||
| 653 | |a Satisfaction | ||
| 653 | |a Partnerships | ||
| 700 | 1 | |a Kumar, Shreyas |u Department of Computer Science and Engineering, Texas A&M University, USA | |
| 700 | 1 | |a Singh, Tripti |u AI and Software Engineering Practitioner, San Francisco, USA | |
| 700 | 1 | |a Kumar, Pratyush |u Independent Researcher, Noida, India | |
| 773 | 0 | |t European Conference on Cyber Warfare and Security |g (Jun 2025), p. 647-656 | |
| 786 | 0 | |d ProQuest |t Political Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3244089532/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3244089532/fulltext/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3244089532/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |