My personal blog on machine learning, cybersecurity and AI
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 →
First, let’s quickly review the clusters we built to understand phishing attacks. Using data we collected over the course of a year spent tracking and taking down phishing cases for a major U.S. financial institution, we extracted features from four categories: similarity analysis, structure analysis, phishing visitors tracking and domain registration. Then, using the expectation-maximization... Continue Reading →
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 into account, companies should use a more business-oriented measure such as “Cost,” which allows companies to make decisions that are better aligned... Continue Reading →
In a recent report we showed how we are able to gain better understanding of phishing attacks and attackers by using cluster analysis. This post lays out in greater detail how to create those clusters by examining the features and methods used.For the study, we used the data collected over the course of more than a year... Continue Reading →
A credit card fraud detection algorithm consists in identifying those transactions with a high probability of being fraudulent based on historical fraud patterns. The use of predictive modeling/machine learning in fraud detection has been a topic of interest in recent years. Different detection systems based on machine learning techniques have been successfully used for this problem,... Continue Reading →
As cybercriminals are constantly updating their strategies to avoid being detected, traditional fraud detection tools, such as expert rules, are less effective as they do not incorporate recent fraud patterns as fast as the fraudsters are changing their behavior. To incorporate the fraudulent behavior fast, it is important to use advanced machine learning algorithms, such as... Continue Reading →
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, including mobile and online payments, creating new challenges as innovative fraud patterns emerge. One technique organizations use to detect and prevent... Continue Reading →
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 Service ... [Read more]
