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Authors: Ji, Y.; Advisor: -; Participants: Wang, Q.; Li, X.; Liu, J. (2019) - This survey gives a comprehensive overview of tensor techniques and applications in machine learning. Tensor represents higher order statistics. Nowadays, many applications based on machine learning algorithms require a large amount of structured high-dimensional input data. As the set of data increases, the complexity of these algorithms increases exponentially with the increase of vector size. Some scientists found that using tensors instead of the original input vectors can effectively solve these high-dimensional problems. This survey introduces the basic knowledge of tensor, including tensor operations, tensor decomposition, some tensor-based algorithms, and some applications of tensor in machine learning and deep learning for those who are interested in learning tensors. The t...
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Authors: Liang, F.; Advisor: -; Participants: Hatcher, W. G.; Liao, W.; Gao, W.; Yu, W. (2019) - The advancement of the Internet of Things (IoT) has allowed for unprecedented data collection, automation, and remote sensing and actuation, transforming autonomous systems and bringing smart command and control into numerous cyber physical systems (CPS) that our daily lives depend on. Simultaneously, dramatic improvements in machine learning and deep neural network architectures have enabled unprecedented analytical capabilities, which we see in increasingly common applications and production technologies, such as self-driving vehicles and intelligent mobile applications. Predictably, these technologies have seen rapid adoption, which has left many implementations vulnerable to threats unforeseen or undefended against. Moreover, such technologies can be used by malicious actors, an...
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Authors: Tonello, A. M.; Advisor: -; Participants: Letizia, N. A.; Righini, D.; Marcuzzi, F. (2019) - A great deal of attention has been recently given to Machine Learning (ML) techniques in many different application elds. This paper provides a vision of what ML can do in Power Line Communications (PLC). We rst and brie y describe classical formulations of the ML, and distinguish deterministic from statistical learning models with relevance to communications. We then discuss ML applications in PLC for each layer, namely, for characterization and modeling, for the development of physical layer algorithms, for media access control and networking. Finally, other applications of the PLC that can bene t from the usage of ML, as grid diagnostics, are analyzed. Illustrative numerical examples are reported to serve the purpose of validating the ideas and motivate future research endeavor...
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Authors: Fang, H.; Advisor: -; Participants: Wang, X.; Tomasin,S. (2019) - The 5G and beyond wireless networks are critical to support diverse vertical applications by
connecting heterogeneous devices and machines, which directly increase vulnerability for various
spoofing attacks. Conventional cryptographic and physical layer authentication techniques are facing
some challenges in complex dynamic wireless environments, including significant security
overhead, low reliability, as well as difficulties in pre-designing a precise authentication model, providing
continuous protection, and learning time-varying attributes. In this article, we envision new authentication
approaches based on machine learning techniques by opportunistically leveraging physical layer attributes,
and introduce intelligence to authentication for more efficient security provis...
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Authors: Cremer, J. L.; Advisor: -; Participants: Konstantelos, I. (2019) - Various supervised machine learning approaches have been used in the past to assess the power system security (also known as reliability). This is typically done by training a classifier on a large number of operating points whose postfault status (stable or unstable) has been determined via time-domain simulations.
The output of this training process can be expressed as a security rule that is used online to classify an operating point. A critical, and little-studied aspect of these approaches is the interpretability of the rules produced. The lack of interpretability is a well-known issue of some machine learning approaches, especially when dealing with difficult classification problems. In the case of the security assessment of the power system, which is a complex
mission-critical...
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Authors: Mishra, P.; Advisor: -; Participants: Varadharajan, V.; Tupakula, U.; Pilli, E. S. (2019) - Intrusion detection is one of the important security problems in todays cyber world. A significant number of techniques have been developed which are based on machine learning approaches. However, they are not very successful in identifying all types of intrusions. In this paper, a detailed investigation and analysis of various machine learning techniques have been carried out for finding the cause of problems associated with various
machine learning techniques in detecting intrusive activities. Attack classification and mapping of the attack features is provided corresponding to each attack. Issues which are related to detecting low-frequency attacks using network attack dataset are also discussed and viable methods are suggested for improvement.
Machine learning techniques have bee...
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Authors: Nawaz, S. J.; Advisor: -; Participants: Sharma, S. K.; Wyne, S.; Patwary, M. N.; Asaduzzaman, M. (2019) - The upcoming fth generation (5G) of wireless networks is expected to lay a foundation of intelligent networks with the provision of some isolated arti cial intelligence (AI) operations. However, fully intelligent network orchestration and management for providing innovative services will only be realized in Beyond 5G (B5G) networks. To this end, we envisage that the sixth generation (6G) of wireless networks will be driven by on-demand self-recon guration to ensure a many-fold increase in the network performance and service types. The increasingly stringent performance requirements of emerging networks may nally trigger the deployment of some interesting new technologies, such as large intelligent surfaces, electromagnetic orbital angular momentum, visible light communications, a...
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Authors: Fleming, S. W.; Advisor: -; Participants: Goodbody, A. G. (2019) - Hydroelectric power generation, water supplies for municipal, agricultural, manufacturing, and service industry uses including technology-sector requirements, dam safety, ood control, recreational uses, and ecological and legal constraints, all place simultaneous, competing demands on the heavily stressed water management infrastructure of the mostly arid American West. Optimally managing these resources depends on predicting water availability. We built a probabilistic nonlinear regression water supply forecast (WSF) technique for the US Department of Agriculture, which runs the largest stand-alone WSF system in the US West. Design criteria included improved accuracy over the existing system; uncertainty estimates that seamlessly handle complex (heteroscedastic, non-Gaussian) pred...
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Authors: Sartzetakis, I.; Advisor: -; Participants: Christodoulopoulos, K. K.; Varvarigos, E. M. (2019) - In optical transport networks the quality of transmission (QoT) is estimated before provisioning new
connections or upgrading existing ones. Traditionally, a physical layer model (PLM) is used for QoT estimation
coupled with high margins to account for the model inaccuracy and the uncertainty in the evolving physical layer
conditions. Reducing the margins increases network efficiency but requires accurate QoT estimation. We present
two machine learning (ML) approaches to formulate such an accurate QoT estimator. We gather physical layer feedback, by monitoring the QoT of existing connections, to understand the actual physical conditions of the network. These data are used to train either the input parameters of a PLM or a machine learning model (ML-M). The proposed ML methods ac...
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