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  • On the Feasibility of Deep Learning in Sensor Network Intrusion Detection

    • Authors: Otoum, S.;  Advisor: -;  Participants: Kantarci, B.; Mouftah, H. T. (2019)

  • In this letter, we present a comprehensive analysis of the use of machine and deep learning (DL) solutions for IDS systems in wireless sensor networks (WSNs). To accomplish this, we introduce restricted Boltzmann machine-based clustered IDS (RBC-IDS), a potential DL-based IDS methodology for monitoring critical infrastructures by WSNs. We study the performance of RBC-IDS, and compare it to the previously proposed adaptive machine learning-based IDS: the adaptively supervised and clustered hybrid IDS (ASCH-IDS). Numerical results show that RBC-IDS and ASCH-IDS achieve the same detection and accuracy rates, though the detection time of RBC-IDS is approximately twice that of ASCH-IDS. Index Terms—Wireless sensor network, cybersecurity,
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  • Machine-Learning Detection Algorithms for Large Barkhausen Jumps in Cluttered Environment

    • Authors: Alimi, R.;  Advisor: -;  Participants: Ivry, A.; Fisher, E.; Weiss, E. (2019)

  • Modern magnetic sensor arrays conventionally use state-of-the-art low-power magnetometers such as parallel and orthogonal fluxgates. Low-power fluxgates tend to have large Barkhausen jumps that appear as a dc jump in the fluxgate output. This phenomenon deteriorates the signal fidelity and effectively increases the internal sensor noise. Even if sensors that are more prone to dc jumps can be screened out during production, the conventional noise measurement does not always catch the dc jumps because of their sparsity. Moreover, dc jumps persist in almost all the sensor cores although at a slower but still intolerable rate. Even if dc jumps could be easily setected in a shielded environment, when deployed in the presence of natural noise and clutter, it can be hard to positively detect ...
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  • Enhanced Machine Learning Techniques for Early HARQ Feedback Prediction in 5G

    • Authors: Strodthoff, N.;  Advisor: -;  Participants: Göktepe,B.; Schierl, T.; Hellge, C.; Samek, W. (2019)

  • We investigate Early Hybrid Automatic Repeat reQuest (E-HARQ) feedback schemes enhanced by machine learning techniques as a path towards ultra-reliable and lowlatency communication (URLLC). To this end, we propose machine learning methods to predict the outcome of the decoding process ahead of the end of the transmission. We discuss different input features and classification algorithms ranging from traditional methods to newly developed supervised autoencoders. These methods are evaluated based on their prospects of complying with the URLLC requirements of effective block error rates below 10 at small latency overheads. We provide realistic performance estimates in a system model incorporating scheduling effects to demonstrate the feasibility of E-HARQ across different signal-to-n...
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