Analyzing credit card transactions using machine learning techniques – 3

Introduction In a previous article, we explored how PCA can be used to plot credit card transactions into a 2D space, and we proceeded to visually analyse the results. In this article, we take this process one step further and use hierarchical clustering to automate parts of our analysis, making it even easier for our … Continue reading Analyzing credit card transactions using machine learning techniques – 3

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Analyzing credit card transactions using machine learning techniques – 2

Principal Component Analysis - Introduction and Data Preperation Principal Component Analysis [PCA] is an unsupervised algorithm which reduces dimensionality and is widely used. A good visual explanation can be found here: http://setosa.io/ev/principal-component-analysis/ As mentioned in our previous article, Correspondence Analysis  works exclusively on categorical data. In contrast, PCA accepts only numerical data. This means our data … Continue reading Analyzing credit card transactions using machine learning techniques – 2

Analyzing credit card transactions using machine learning techniques

Introduction In this 3-part series we'll explore how three machine learning algorithms can help a hypothetical financial analyst explore a real data set of credit card transactions to quickly and easily infer relationships, anomalies and extract useful data. Data Set The data set we'll use in this hypothetical scenario is a real data set released … Continue reading Analyzing credit card transactions using machine learning techniques

What do Smartphone Predictive Text and Cybersecurity have in common?

Maybe the link between your smartphone keyboard and current machine learning research in cybersecurity is not apparent at first glance, but the technology behind both is extremely similar: both leverage deep learning architectures called Recurrent Neural Networks [RNNs], specifically a type of RNN called Long Short Term Memory [LSTM]. One of the main advantages of … Continue reading What do Smartphone Predictive Text and Cybersecurity have in common?

The importance of data mining in the field of cybersecurity

In a very interesting article on TechCrunch, Michael Schiebel writes about the various ways in which security analysts can learn from data scientists. He makes a couple of points that are worth highlighting. Today, hacking is a much more complex art than it used to be: It no longer only involves just scanning and penetrating … Continue reading The importance of data mining in the field of cybersecurity

Data mining firewall logs : Principal Component Analysis

In this article we'll explore how Principal Component Analysis [PCA] [1] - a popular data reduction technique - can help a busy security or network administrator. Any such administrator has often been faced with a daunting problem... going through reams of firewall or router connection logs trying to figure out if any of the thousands … Continue reading Data mining firewall logs : Principal Component Analysis

First Steps in applying machine learning to InfoSec – WEKA

The intersection between machine learning [ML] and information security [InfoSec] is currently quite a hot topic. The allure of this intersection is easy to see, security analysts are drowning in alerts and data which need to be painstakingly investigated and if necessary acted upon. This is no easy processes and as was seen in the … Continue reading First Steps in applying machine learning to InfoSec – WEKA