The last few years have witnessed a tremendous growth of the demand for wireless services and a significant increase of the number of mobile subscribers. A recent data traffic forecast from Cisco reported that the global mobile data traffic reached 1.2 zettabytes per year in 2016, and the global IP traffic will increase nearly threefold over the next 5 years. Based on these predictions, a 127-fold increase of the IP traffic is expected from 2005 to 2021. It is also anticipated that the mobile data traffic will reach 3.3 zettabytes per year by 2021, and that the number of mobile-connected devices will reach 3.5 per capita.
With such demands for higher data rates and for better quality of service (QoS), fifth generation (5G) standardization initiatives, whose initial phase was specified in June 2018 under the umbrella of Long Term Evolution (LTE) Release 15, have been under vibrant investigation. In particular, the International Telecommunication Union (ITU) has identified three usage scenarios (service categories) for 5G wireless networks: (i) enhanced mobile broadband (eMBB), (ii) ultra-reliable and low latency communications (uRLLC), and (iii) massive machine type communications (mMTC). The vast variety of applications for beyond 5G wireless networks has motivated the necessity of novel and more flexible physical layer (PHY) technologies, which are capable of providing higher spectral and energy efficiencies, as well as reduced transceiver implementations.
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Upcoming Webinar! 21 October 2019
Webinar Topic: "Fast Detection of Transformed Data Leaks"
Presented by Dr. Xiaokui Shu and based on an IEEE Xplore article
published in IEEE Transactions on Information Forensics and Security
|Presenter: Dr. Xiaokui Shu
|Date: 21 October 2019
Time: 11:00 am EDT (New York time)
Duration: Approximately 1 hour
Register: Attendee Registration
About this Topic:
This webinar, “Fast Detection of Transformed Data Leaks,” will discuss how the leak of sensitive data across secure network boundaries is becoming one of the most critical concerns for many industries in the move towards digitalization, building worldwide information portals, and the cloud. Due to the complex causes of data leaks, developers and researchers have created an umbrella of methods to identify, mitigate, and prevent leaks in different scenarios. This data leak detection webinar begins with a few distinct data leak cases and real-world countermeasures to inspire the audience to discover research problems. Then we pick up one set of detection problems regarding data comparison and dive into a string of detection approaches from simple to complex. Different design requirements regarding the deployment needs will be discussed, followed by existing solutions in the literature or commercial products. The last part of the webinar will generalize sensitive data into knowledge, connect data leak detection with other rapidly growing fields, and discuss potential research directions in the near future.
About the Presenter:
Dr. Xiaokui Shu is a Research Staff Member in the Cognitive Cybersecurity Intelligence Group at the IBM T. J. Watson Research Center. He received his Ph.D. degree in computer science from Virginia Tech and was the recipient of the Outstanding Ph.D. Student Award. He received his Bachelor’s degree from the University of Science and Technology of China (USTC) through the Guo Moruo Scholarship. He succeeded at his first penetration test (a type of ethical hacking) at USTC, and was awarded first prize in the Virginia Tech Inaugural Cybersecurity Summit Competition. His research interests are in system and network security, and he has published more than a dozen papers in the past few years on cyber-reasoning, program anomaly detection, data leak detection, mobile security, and user behavior analytics. The ACM featured his anomaly detection research in 2016 and highlighted his threat intelligence computing methodology in 2018. His data leak detection research opens doors to new business models, and many of his innovations have been patented by Yahoo and IBM.
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