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The Cyber Security Challenges: A Survey of Chief Information Security Officer in Indian Context
Published in Durgesh Kumar Mishra, Nilanjan Dey, Bharat Singh Deora, Amit Joshi, ICT for Competitive Strategies, 2020
Kumar Rahul, Rohitash Kumar Banyal, Nikhil Raghav Bhatt
Cyber security technologies are used in artificial intelligence (AI) and deep learning (DL), behavioral analytics, embedded hardware authentication, block chain cyber security and zero trust models. Apart from these technologies, context-aware behavioral analytics, next generation breach detection, virtual dispersive networking (VPN), smart grid technologies, SAML (security assertion markup language) and the cloud, active defense measures, and early warning systems technologies are available for cyber security. Emerging technologies included as hardware authentication, user- behavioral analytics, and data loss prevention etc. The features of cyber security technologies include major coverage of threat vectors such as mail and web, vital supervision across all products and services, threat prevention, cloud-based workload, cloud-based back-end services, open API, tightly coupled between product and services, offered many deployment options etc.
Population-Specific and Personalized (PSP) Models of Human Behavior for Leveraging Smart and Connected Data
Published in Kuan-Ching Li, Beniamino DiMartino, Laurence T. Yang, Qingchen Zhang, Smart Data, 2019
Theodora Chaspari, Adela C. Timmons, Gayla Margolin
Recent computational and algorithmic advances can fully leverage the vast amount of information obtained on a 24/7 basis from smart-sensing devices for conventional applications, such as computer vision and speech processing. This is not always the case in affective computing, behavioral analytics, and human-related applications, where the high diversity of people and behaviors renders such data hard to manage and process [1, 2]. The ideal view of a system that can be generalized to fully capture the behavioral and clinical characteristics of a large group of people requires longitudinal multimodal information from extensive and systematic data collection procedures that are usually hard to implement for a large number of participants. Furthermore, even if the acquisition of such data was feasible, the long processing times and computational power required from the corresponding machine learning algorithms render such applications almost prohibitive for the average user. These challenges have created the need to develop computationally efficient models that can address the inherent data sparsity issues and high variability present in human-related applications.
Potential Disruptive Innovators and Scenarios
Published in Nawal K. Taneja, Airline Industry, 2016
In October 2015, Emirates announced an arrangement with Oxford University in the UK to establish a data science laboratory to develop ways to understand customer preferences on one side and the airline’s processes on the other side with the purpose of designing and creating more customer-centric products, services, and operations. Emirates can provide the data and the university can provide the multi-disciplinary staff from such departments as Mathematics, Engineering Science, Computer Science, and Statistics, and use the internet to conduct behavioral analytics to analyze the customer behavioral aspects of the data. The airline hopes to transform its business initiatives from the customer’s perspective, leveraging technology-based innovations. A month later, in November 2015, Emirates announced a similar arrangement with Carnegie Mellon University in the USA, to set up an Innovation Laboratory to help Emirates re-invent its business practices enabled by smart technology, big data, and real-time analytics. As with the groups at Oxford University, the groups at Carnegie Mellon University will also represent multi-disciplinary teams. The Emirates team will work with Carnegie Mellon’s Integrated Innovation Institute that, in turn, is a joint enterprise between the university’s College of Engineering, College of Fine Arts, and the School of Business.
Customer Mobile Behavioral Segmentation and Analysis in Telecom Using Machine Learning
Published in Applied Artificial Intelligence, 2022
Eman Hussein Sharaf Addin, Novia Admodisastro, Siti Nur Syahirah Mohd Ashri, Azrina Kamaruddin, Yew Chew Chong
Tabb (2019) stated that mobile behavioral analytics reveals new insights into the behavior of users on mobile phones. Behavioral analytics utilizes the massive volumes of users’ event data captured during sessions in which users use an application, game, or website. One important area in analyzing mobile user behaviors is advancing with more meaningful and effective customer segments for businesses. Ezenkwu, Ozuomba, and Kalu (2015) defined that the segmentation process refers to the division of market or customers into groups called segments, where each customer segment shares similar traits.