Blog

2017


Classifying Phishing URLs Using Recurrent Neural Networks
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 of URLs... [Read more]
Machine Learning Explained
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 it is nearly impossible to understand how a model is making a prediction... [Read more]

2016


Benefits of Anomaly Detection Using Isolation Forests
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 ... [Read more]
The Technical Side of Phishing and How to Prevent It
Phishing, by definition, is the act of defrauding an online user and tricking them into clicking on a malicious link ... [Read more]
Applying Data Science to Fraud Prevention
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 ... [Read more]
Fraud Detection That Accounts for Misclassification Using Cost-Sensitive Logistic Regression
Fraud detection is a cost-sensitive problem, in the sense that falsely flagging a transaction as fraudulent carriesa significantly different financial cost than missing an actual fraudulent transaction. In order to take these costs ... [Read more]
Phishing Attack Analysis: Estimating Key Cluster Features and Why It’s Important
A recent report showed how we can gain a better understanding of phishing attacksand attackers by using cluster analysis.Subsequently, in a recent post we showed how ... [Read more]
Clustering of Phishing Attacks
Easy Solutions data scientists, including the author of this article, will present extensive research on phishing patterns and correlations between attacks. ... [Read more]
Evaluating a Fraud Detection Using Cost-Sensitive Predictive Analytics
A credit card fraud detection algorithm consists in identifying those transactions with a high probability of being fraudulent based on historical fraud patterns. ... [Read more]

2015


Feature Engineering for Fraud Detection Models
As cybercriminals are constantly updating their strategies to avoid being detected, traditional fraud detection tools, such as expert rules, are less effective as ... [Read more]
Fraud Detection with Advanced Outlier Detection Algorithms
Online fraud costs the global economy more than $400 billion, with more than 800 million personal records stolen in 2013 alone. Increasingly, fraud has diversified to different digital channels ... [Read more]
Hello! Let Me Introduce Myself
As one of the newest employees at Easy Solutions, I’d like to take this opportunity to introduce myself. I am joining the Company as a Data Scientist. Before becoming part of Easy Solutions, I spent my time working at SIX Financial Services ... [Read more]