My personal blog on machine learning, cybersecurity and AI
Previously in this series, we have looked at decision trees and random forests, two types of supervised learning algorithms. Supervised algorithms are trained with data that provides input vectors as well as their corresponding target vectors, or the output that is expected after the data is processed. Unsupervised models, on the other hand, are trained using data... Continue Reading →
Random Forests are supervised ensemble-learning models used for classification and regression. Ensemble learning models aggregate multiple machine learning models, allowing for overall better performance. The logic behind this is that each of the models used is weak when employed on its own, but strong when put together in an ensemble. In the case of Random... Continue Reading →
Recently, we published a research paper showing how it is possible to detect fraudulent credit card transactions with a high level of accuracy and a low number of false positives. By using ensembles of cost-sensitive decision trees, we can save up to 73 percent of losses stemming from fraud. Here’s how. Classification, in the context... Continue Reading →
A Decision Tree is a supervised predictive model that can learn to predict discrete or continuous outputs by answering a set of simple questions based on the values of the input features it receives. To get a better understanding of how DT works, we will use a real-world dataset to better illustrate the concept. This... Continue Reading →
Online, or real-time, transactional fraud detection systems have recently created quite the buzz in the info security industry. They are an appealing concept: Because we know that fraud patterns change over time, the ability to use machine-learning algorithms to automatically learn new patterns instantly allows us to have a stronger defense system. We often find... Continue Reading →
Recently, we have seen a huge boom around the field of deep learning; it is currently being implemented in a wide variety of fields, from driverless cars to product recommendation. In their most primitive form, deep learning algorithms originated in the 1960s. If the concept has been around for decades, why is it that widespread... Continue Reading →
In a recent research paper, we showed how we are able to detect with a high level of accuracy if a website is a phish just by looking at the URL. This post lays out in greater detail how, by using a deep recurrent neural network, we’re able to accurately classify more than 98 percent... Continue Reading →
Machine learning models are often dismissed on the grounds of lack of interpretability. There is a popular story about modern algorithms that goes as follows: Simple linear statistical models such as logistic regression yield to interpretable models. On the other hand, advanced models such as random forest or deep neural networks are black boxes, meaning... Continue Reading →
TDWI: 5 Minutes with a Data Scientist: Alejandro Correa Bahnsen of Easy Solutions Lead data scientist Alejandro Correa Bahnsen develops machine learning algorithms for fraud detection. He described for Upside the basic skills and personality traits he believes are necessary to succeed in data science. [Read More]
One of the newest techniques to detect anomalies is called Isolation Forests. The algorithm is based on the fact that anomalies are data points that are few and different. As a result of these properties, anomalies are susceptible to a mechanism called isolation. This method is highly useful and is fundamentally different from all existing... Continue Reading →
Phishing, by definition, is the act of defrauding an online user and tricking them into clicking on a malicious link in order to obtain personal information by posing as a trustworthy institution or entity. That is why users have a hard time differentiating between a legitimate and a malicious site. Although one might think the... Continue Reading →
Eighty thousand Kindle users. Sixty-five million Tumblr users. What do they have in common? Both groups had their login credentials breached, courtesy of hackers. While these attacks didn’t directly target financial accounts,the information contained in these breaches is likely being sold on the Dark Web and being used to build a larger profile that will... Continue Reading →
