Since the start of the COVID-19 pandemic, work conditions have changed substantially. In particular, white-collar workers are almost exclusively working from home. More significantly, though, these workers have become the targets of phishing attacks, as various bad actors recognize the lower security threshold of home computer systems. Such attacks could seriously harm businesses, going beyond the damage caused by the pandemic. As a result, companies are seeking out more advanced security tools – and they’re looking to big data for help.
Big Data’s Detection Power
Phishing attacks have been around for a long time, but older iterations of these attacks were quite rudimentary; users had to be fairly gullible and had to manually interact with suspicious content in order for such attacks to succeed. At this juncture, though, the attacks can be quite savvy. They involve detailed mockups of real websites and email styles, fake web addresses, and complex interfaces. And this is why today’s attacks are so successful – they’re convincing so people trust them.
The good news about these attacks is that, while they may be hard for humans to detect, they’re also often caught by big data-driven platforms. For example, Gmail’s security tools rely heavily on big data to protect users, capturing emails that could contain viruses, sorting our spam, and otherwise alerting to potentially concerning messages.
Of course, Gmail’s tools are just scratching the surface when it comes to what big data and AI are capable of when it comes to security. Rather, other self-learning email tools can actually look much more deeply into potential threats, parsing data on cybersecurity risks and substantially benefiting businesses.
Smarter Anti-Phishing Solutions
What types of technology exists beyond the sort of basic tools Gmail uses? One iteration is targeted anti-phishing software like Check Point’s enterprise platforms that use smart filters to identify common language patterns used in phishing schemes. Such filters can also learn to detect slight variations or indicators like links that are missing their SQL certificate or that otherwise lack the markers of a secure, authorized platform. Recognizing the scope of this problem, many other companies are also raising funds to advance AI-driven anti-phishing technology.
In addition to using natural language processing to identify likely attacks – phishing attacks often contain errors you wouldn’t find in legitimate messages, these systems also use other linguistic systems that are best evaluated by computer systems. These include a hybridized form of machine learning and natural language processing to perform stylometry, or the analysis of linguistic style, as well as deep learning systems that can perform content summarization and name-entity identification. These systems are quite advanced, and they can see deeply into emails and flag suspicious content that isn’t visible to the naked eye.
Perhaps the most powerful way big data detects phishing attacks is also the simplest: it learns what users do day in and day out. This is often the way human recipients detect such emails, too. We receive an email and it’s just slightly off compared to emails we’re used to receiving – though humans often pick up on different cues.
In the case of the computer system, machine learning protocols use data from past interactions, these programs may flag an email because the email address isn’t quite right (it’s missing a letter or uses a different domain), or because the message is from an otherwise suspicious sender. The system learns the user’s interaction patterns and spots deviations. It can do the same with web addresses that seek to steal information by mimicking a sign-in screen or other interface.
All of the above tools rely on past patterns to identify a new threat, but such an approach has obvious limitations. What happens when a phishing attack takes a more dramatic leap forward?
This happens all the time, and that’s why these attacks are so successful at getting past both humans and computers. But machine learning that employs predictive analytics to extrapolate potential future attack iterations can catch these treats. Though the system still uses past information to draw its conclusions about the likelihood a suspicious message is a phishing attack, it does so with a greater degree of extrapolation than it might otherwise. Predictive analytics play a key role in securing modern systems and will only become more precise going forward. This is the power of big data – past threats inform current analysis and identify future incursions.
Analysis, Detection, Prediction – The Big Data Trifecta
Big data tools are central to the functioning of most advanced computer systems today. They help companies identify future client needs, predict supply and demand to stock their shelves, and to make a variety of other future-oriented decisions. When used to prevent phishing attacks, though, machine learning platforms primarily serve to ensure business security long enough to benefit from all of those other decisions. Phishing attacks threaten the very foundation of a business by stealing sensitive information. Stopping them is a matter of professional survival, and every business needs to prepare.