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The i.i.d assumption is the corner stone of most conventional machine learning algorithms. However, reducing the bias and variance of the learning model on the i.i.d dataset may not help the model to prevent from their failure on the adversarial samples, which are intentionally generated by either the malicious users or its rival programs. This paper gives a brief introduction of machine learning...
With the arrival of big data era, data mining techniques have been widely used to build models for cyber security applications such as spam filtering, malware or virus detection, and intrusion detection. This project proposes a novel approach that uses randomness to improve robustness of data mining models used in cyber security applications against attacks that try to evade detection by adapting...
The technique of distributed storage coding has been widely used in wireless sensor networks for increasing the robustness of data storage and efficiency of data retrieval. Existing works mainly focus on scenarios in which each storage node stores a linear combination of a subset of K data packets generated by different source nodes. By solving the linear equations, a collector can recover all the...
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