Highlights:

  • Scammers are now using artificial intelligence (AI) to target and attack users by taking and eating every piece of breached data they find on the internet.
  • Reports from the third quarter of 2021 show that business email compromise (BEC) phishing led to transaction requests worth USD 85,000, almost double what was reported before.

One might have heard a lot about phishing attacks. Or maybe not? Actually, a large fraction of it remains unseen as spam filters have gotten good enough to combat a majority of phishing attacks. Just visit your junk mail folder and you will find them in numbers. All thanks to email services today that have advanced so much that most of such emails end up in the spam filter.

Yet, phishing and ransomware traps are becoming a concern, especially in light of the fact that attackers are looking for a smaller target audience, like a specific group or company, to target. Because they are less spammy, chances are high that they will pass all filters and get a bite from the people. Scammers have learned that the more information they can get about you, the more likely they will be able to phish you. The threat is especially concerning for businesses.

According to the latest Zscaler ThreatLabz Phishing Report, phishing attacks rose 29% globally to a new record of 873.9M attacks last year. Furthermore, the Anti-Phishing Working Group’s Phishing Activity Trends report says that there were a record 1,025,968 phishing attacks in the first quarter of 2022. (APWG). But the situation is getting even worse.

Scammers are now taking and eating every piece of breached data they find on the internet and combining it with Artificial Intelligence (AI) to target and attack users. As phishing attempts become more sophisticated, some of the biggest companies in the world are more worried than ever about this practice. What’s scary? Even though the AI being used isn’t that smart, there are more successful phishing and ransomware payouts.

What should we do then? Can we improve our security by adding AI with something else? Yes!

This is where Machine Learning-based behavior analytics offer a solution. As it is known, Machine Learning is one of the foremost mechanisms that go hand-in-hand with AI. It is based on algorithms that try to understand and find patterns in vast amounts of data to put in place a system that can predict strange behavior and anomalies. It changes with time as it learns patterns of normal behaviour. All these features help identify phishing emails, spam, and malware.

Evolution of phishing attacks  

Social engineering is all about stirring one’s emotions to generate a response that ultimately compels one to share personal information like passwords, credit card information, and more. Unsophisticated phishing attacks can be easily identified if one knows what to look for. Examples include typos, grammatical errors, dubious links, duplicate logos, and much more. Such attacks are done in bulk so as to target millions of people at one time and see who would bite. Like technology, bad actors, too, evolved as also their tactics.

Hackers’ new tactics involved the usage of AI to intelligently target individuals. Typical examples include emails from the IT department that contains information about your profile or a customized and direct spear phishing attack, including your actual password, informing you that your account had been compromised.

Bad actors have not stopped here. They have evolved further.

The AI phishing revolution

Hackers love and pile data. The most valuable data for them is breached data — this isn’t limited to the information they’ve personally breached or ransomed. They actually love all kinds of data leaked on the dark web.

Data breaches can reveal almost every piece of information about you – be it your mother’s maiden name or your date of birth, your previous passwords and can even identify your personal interests. This may not be something not heard of, though what has changed is the way this information is being used by scammers.

Bad actors are combining this data with AI to launch sophisticated phishing attacks that are able to convince users more than ever. This is being done at a time when AI is still in its developing stage. As it is known, AI functions on a pre-programmed path, so there’s no need to worry about it thinking for itself. But as people become smarter, more sophisticated models can be developed and AI can be trained to run more complex scenarios. With an increase in sophistication, signs are that phishing will look much like targeted ads in the future.

Fighting Phishing Attacks: Working Together

Cyber phishing was easy to stop, but those days are long gone. Modern-day attackers use more sophisticated strategies, and their attacks are more realistic and tailored. Fake websites and landing pages look very much the same as the real ones. They make ads that look authentic, and their social media presence is cleverly engineered. Every day, new tools for cyber scamming that pose considerable risks to users and their security emerge. Humans alone are incapable to tackle such advanced attack scenarios. This is where Machine Learning that offers adequate anti-phishing solutions is important. Its countermeasures are more effective and take less time because it constantly learns and updates.

Mitigating Mobile Phishing Using ML

ML mitigation is needed for more than just stopping phishing on large systems. It’s also needed on systems that aren’t as big. People are using their phones and tablets more and more for all their digital transactions and communications. To prevent phishing on mobile phones and other similar devices, you must also be able to use Machine Language. Organizations need to start using ML tools to stop phishing. ML algorithms constantly learn from the data they receive to predict a phishing attack from tiny clues.

The need of the hour is for a Machine Learning engine that can analyze and interpret system data in real-time and identify any suspicious activity. What’s needed today is the use of supervised ML algorithms that take into account device detection, location, and user behavior patterns among others to forecast and circumvent phishing attacks.

It needs no mentioning that any ML engine and the platforms supporting it must be cloud-based and must be scalable enough to analyze millions of data points. At the same time, the cloud platform must be built on high-performing computing clusters and must also possess the capability to iterative machine learning models on the fly, in milliseconds, to learn about new patterns of potential phishing breaches.

Four Ways Machine Learning Can Help Stop Phishing Attacks

Machine Learning algorithms will fail only when they stop transforming. Since technology advances on a daily basis, it is helpful to keep in mind the following information to close the knowledge gap and prevent attacks.

  • One should improve cyber security protection in a way that looks ahead. Instead of looking at things after they’ve been done, it’s essential to look at the data from endpoint devices and predict the occurrence of any threat. This will stop any sophisticated attempts to break in.
  • The effectiveness of a cyber security strategy will be judged by how well an organization can predict, deal with, and stop threats.
  • Establish Machine Learning algorithms on every mobile phone so as to detect threats in real-time, even in offline devices.
  • These algorithms can help improve mobile phone security and can also be used as an employee ID card to eliminate the needless hassle of weak passwords.

In conclusion

While AI and breached data are helping hackers launch more targeted and sophisticated phishing campaigns with greater success, the same technologies can be used to thwart such attempts as well. The best thing about security enabled by AI and ML is that it can help uncover patterns and learn from unstructured data. Such tools can arm security professionals with the power to combat attacks. They can also gain insights into evolving threats and ways how they can defend against impending incidents.

For a Machine Learning model to be able to stop phishing, it must know what kind of email, text, social media post, or SMS is phishing. Today, even mobile devices are capable to stop such attacks. The need of the hour though is for a platform of data, risk ratings, and IT models that keeps growing. Machine Learning systems always learn from large amounts of data to detect anomalies. It can tell when the way a sender talks changes and stop messages from fake addresses. By interacting with the cloud, ML algorithms can work better because they can access vast data. This lets them learn from data breaches that have happened in the past, which helps them stop phishing better.