Seeing the signature green padlock and “https” in the browser bar means one thing for most internet users: safety. However, is this sense of security justified? The short answer is a loud, resounding, no! To start, let’s define what “https” really means: that the website being accessed is encrypted, and all information sent through the site is protected by... Continue Reading →
DeepPhish: Simulating malicious AI
Recently we presented a research paper on the malicious usage of AI by cyber attackers. Here the abstract, slides a link to the paper. Machine Learning and Artificial Intelligence have become essential to any effective cyber security and defense strategy against unknown attacks. In the battle against cybercriminals, AI-enhanced detection systems are markedly more accurate... Continue Reading →
AI vs AI – Can Predictive Models Stop the Tide of Hacker AI?
Long ago, the introduction of the internet moved crime from physical to digital locations, where anti-fraud actors play a high-stakes game of detection and prevention, always working to stay one step ahead of fraudsters. The battles of modern-day cybercrime follow the same pattern, with one major difference – cybercriminals are far more sophisticated than they... Continue Reading →
Building AI Applications Using Deep Learning
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 →
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... Continue Reading →