As 2017 draws to a close, we here at Easy Solutions have been reflecting on the biggest fraud events of the year. These seven events changed the fraud landscape and left lasting effects on organizations and their outlook on security for years to come.
In our series, Machine Learning Algorithms Explained, our goal is to give you a good sense of how the algorithms behind machine learning work, as well as the strengths and weaknesses of different methods.
Uber has had a difficult year. It took another hit last week with the news that it had covered up a data breach that occurred more than a year ago.
Predictions 2018: Human Manipulation Is the Standard, Obsessing Over End User Authentication Becomes the Norm
As 2017 comes to a close, people start to take stock of the past year and what’s in store for the coming one.Not ones to be left out, we’ve compiled our own list of what we can expect to see on the cyber threat landscape in 2018.
The US Department of Homeland Security (DHS) recently announced a new policy requiring that all federal agencies implement a DMARC policy to protect their email domains.
Seventy percent of the damage from a phishing attack occurs within the first hour of its launch. When it comes to phishing, speedy detection and takedown are crucial in helping organizations avoid hefty losses.
Deploying DMARC can seem confusing and overwhelming at first. In our previous blog post on the topic, we talked about basic tips to keep in mind when deploying a DMARC record.
Phishing is one of the oldest forms of digital fraud, and it shows no signs of going away anytime soon.
In our series, Machine Learning Algorithms Explained, our goal is to give you a good sense of how the algorithms behind machine learning work
Easy Solutions data scientists will present their research on the use of ensembles of cost-sensitive decision trees to detect fraudulent transactions at the Data Science for Cyber Security Conference in London this week. The article below details some of the findings.