Time series are recorded values of an interesting phenomenon such as stock prices, household incomes, or patient heart rates over a period of time. Time series data mining focuses on discovering interesting patterns in such data. This article introduces a wavelet-based time series data analysis to interested readers. It provides a systematic survey of various analysis techniques that use discrete wavelet transformation (DWT) in time series data mining, and outlines the benefits of this approach demonstrated by previous studies performed on diverse application domains, including image classification, multimedia retrieval, and computer network anomaly detection.

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The URL components of web addresses are frequently used in creating phishing detection techniques. Typically, machine learning techniques are widely used to identify anomalous patterns in URLs as signs of possible phishing. However, adversaries may have enough knowledge and motivation to bypass URL classification algorithms by creating examples that evade classification algorithms. This paper proposes an approach that generates URL-based phishing examples using Generative Adversarial Networks. The created examples can fool Blackbox phishing detectors even when those detectors are created using sophisticated approaches such as those relying on intra-URL similarities. These created instances are used to deceive Blackbox machine learning-based phishing detection models. We tested our approach using actual phishing datasets. The results show that GAN networks are very effective in creating adversarial phishing examples that can fool both simple and sophisticated machine learning phishing detection models.