R&D - ?ng d?ng Smart OTT
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This document outlines the goals and objectives of the VTC Mobile - OTT Project. It discusses customizing the OpenFire server platform and implementing clustering and DNS load balancing. It also covers using a bot framework to add intelligence and demonstrates this. The document concludes with an evaluation and discussion of next steps.
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? Functional expertise includes
o Designing Solution Architecture, Database.
o Implementation in various Java-J2EE technologies like Core Java, Struts, Hibernate, Spring MVC, AJAX, EJB, Restful Web Services, Flex, OFBiz, JSF-Seam, Jboss Portal
o Webcontent Management using Alfresco Content Management, Interwoven Web Content Management
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The increasing use of Industrial Internet of Things (IIoT) devices has brought about new security vulnerabilities, emphasizing the need to create strong and effective security solutions. This research proposes a two-layered approach to enhance security in IIoT networks by combining lightweight encryption and RNN-based attack detection. The first layer utilizes Improved Elliptic Curve Cryptography (IECC), a novel encryption scheme tailored for IIoT devices with limited computational resources. IECC employs a Modified Windowed Method (MWM) to optimize key generation, reducing computational overhead and enabling efficient secure data transmission between IIoT sensors and gateways. The second layer employs a Recurrent Neural Network (RNN) for real-time attack detection. The RNN model is trained on a comprehensive dataset of IIoT network traffic, including instances of Distributed Denial of Service (DDoS), Man-in-the-Middle (MitM), ransomware attacks, and normal communications. The RNN effectively extracts contextual features from IIoT nodes and accurately predicts and classifies potential attacks. The effectiveness of the proposed two-layered approach is evaluated using three phases. The first phase compares the computational efficiency of IECC to established cryptographic algorithms including RSA, AES, DSA, Diffie-Hellman, SHA-256 and ECDSA. IECC outperforms all competitors in key eneration speed, encryption and decryption time, throughput, memory usage, information loss, and overall processing time. The second phase evaluates the prediction accuracy of the RNN model compared to other AI-based models DNNs, DBNs, RBFNs, and LSTM networks. The proposed RNN achieves the highest overall accuracy of 96.4%, specificity of 96.5%, precision of 95.2%, and recall of 96.8%, and the lowest false positive of 3.2% and false negative rates of 3.1%.
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Introduction on how unique identifier systems are managed and coordinated - R...
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R&D - ?ng d?ng Smart OTT
- 1. VTC Mobile - OTT Project ? NG? VI?NH HO?A ¨C PM ? ?INH THA? NH ?A?T ¨C MOBILE TECH LEADER ? NGUY?? N DUY ?A?T ¨C MOBILE DEVELOPER ? HOA? NG MINH TH?? NG ¨C SERVER DEVELOPER ? HA? HUY QU?N ¨C DNS LOAD BALANCING SYSTEM Build a service, make it smart
- 2. CONTENTS 1. Recap: Goals & Objects 2. Customize Open Fire & Scale 3. To be smatter with BOT Framework 4. Demo 5. Q&A: Evaluation and the next step?
- 3. Goals and Objectives Business Goal/Objective Description XMPP & OpenFire N??m v??ng c?ng ngh¨º?XMPP tr¨ºn n¨º?n ta?ng OpenFire Xamarin MobileTeam X?y d??ng ???i ngu? pha?t tri¨º?n ??ng du?ng b??ng Xamarin Sip Server/ Web CRT Hi¨º?u ro? c? ch¨º? k¨º?t n??i v??i h¨º?th??ngVoice IP &Video Call Wozza Streaming Tri¨º?n khai tha?nh c?ng Wozza SDK cho Client k¨º?t n??i v??i Wozza Server phu?c vu? live streaming t?? camera phone cu?a ng???i du?ng
- 4. Customize Open Fire: Plugin
- 5. Customize Open Fire: Clustering
- 6. DNS SRV LOAD BALANCING
- 7. To be smatter with BOT Framework https://node.xmpp.scoin.vn https://luis.ai
- 8. To be smatter with BOT Framework
- 9. DEMO
- 10. Q&A: Evaluation and the next step?