4 Ways Machine Learning Prevents Phishing Cyberattacks

When it comes to phishing, it’s important to point ML efforts towards mobile environments: the number 1 target of phishing attacks.
October 1, 2020
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Machine Learning and Phishing

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As if the thousands of challenges the COVID-19 pandemic brought with it weren’t enough, the average business now also has to deal with increasing cybersecurity concerns. For most companies, putting their focus on digital acceleration hasn’t exactly meant doing everything “by the book”—some improvisation always takes place when surviving is the priority. 

Driven by uncertainty, the global business context has become the ideal ground for phishing cyberattacks. Not only are organizations all around the world going through lots of tech transitions, but many are also doing so with one hand tied to their backs. Phishing attempts have surged by 37% since the beginning of the pandemic. A concerning figure, to say the least. 

Luckily, reducing and nullifying phishing cyberattacks doesn’t have to be too complicated. Not with the right technology and talent, at least. That’s why, today, businesses all over the world are outsourcing cybersecurity services powered by machine learning. The accessibility, cost-efficiency, and impactful results of this practice are too valuable to ignore right now. 

Let’s dive deeper into it. 


Know your Enemy: What is Phishing?

“Phishing is a cybercrime in which a target or targets are contacted by email, telephone or text message by someone posing as a legitimate institution to lure individuals into providing sensitive data such as personally identifiable information, banking, and credit card details, and passwords.”

Learn more at phishing.org


What Is Machine Learning?

“Machine learning (ML) is a collective term that describes the various processes that enable a machine to learn from data without following explicitly programmed rules. In other words, it learns from working with whatever data is available. Today, it is most commonly used to reveal hidden relationships in input data and pull insights from them.”

What’s really interesting about ML algorithms is that they improve themselves over time by accessing more data. Basically, the more data you feed them, the better results they will produce—just as long as they don’t become overfitted. Businesses often use machine learning to solve three types of problems: Classification (identifying objects, pictures, audios, etc), Prediction (forecasting outcome probabilities), and Generation (creating objects, text, images, audio, etc).

If you want to learn more about ML in a business context, you’ll first need to take a look into Artificial intelligence. We published an extensive guide on how to maximize the business value of AI which can get you started in everything you need to know. 


How Machine Learning Deals with Phishing

Machine Learning fulfills a critical role in cybersecurity in general: analyzing and interpreting data in real-time to identify malicious behavior. When it comes to phishing, it’s important to point ML efforts towards mobile environments. Why? Because mobile devices are the number 1 target of phishing attacks. 

You see, a mobile device is designed to deliver quick responses when working with minimal contextual information. This is surely helpful in our day-to-day use, but it’s also an exploitable opportunity for hackers. In its 2020 Data Breach Investigations Report, Verizon found that email and mobile messaging are the top targets for linked-based attacks that collect passwords and other private information.

In a business context, companies can use supervised ML algorithms to monitor several variables related to phishing attempts. The data generated from monitoring device detection, behavioral patterns, and connection location can anticipate most phishing attacks today. Paired with cloud computing tech, most businesses should be able to use ML and stop worrying about phishing altogether. 


4 Ways Machine Learning Prevents Phishing Cyberattacks

The lack of adoption is the greatest advantage phishing has against machine learning. These 5 on-device measures could be the best solution to close this gap. 


1. Real-time Threat Prevention Mobile App

If you’ve ever fallen victim to a phishing attack before, you know how easily its effects can expand to the rest of an organization. Today, it isn’t uncommon for companies to develop mobile apps designed to constantly run supervised ML algorithms that detect, remediate, and provide constant visibility of malicious threats. This is by far the most effective measure since it provides detailed analyses of phishing patterns. 


2. Data Mining for Insight Generation

There’s an immense amount of data available today which companies can use to reveal actionable insights that lead to smarter strategies. Modern machine learning algorithms are powerful enough to analyze multiple and interconnected endpoints of data. This strategy is also not independent of cybersecurity concerns. Analyzing data can lead to all types of knowledge discovery that can be applied company-wide. 


3. Next-level Security

Mobile devices have always had friction with security because of their inherent mobility. The fact that 81% percent of breaches are caused by stolen passwords via mobile devices strongly rests this case. The smartest way to prevent this is via Zero Trust initiatives, which use ML to safely provide users with what they need when they need it—as long as they are able to prove their identity. This hardened endpoint security is what separates strong from weak cybersecurity strategies. 


4. Predictive Modeling

You can’t talk about machine learning without talking about predictive models. And if you do, then you shouldn’t. Proper cybersecurity isn’t only about constant, non-invasive monitoring of activities. What truly brings effectiveness to this science is looking ahead by building predictive models from previous data captured at the endpoints. The real-time data provided by tools like URL lookup, Zero Sign-On, and a Zero Trust approach is necessary to sustain cybersecurity strategies that start at the device level. 


The Way Forward

Modern machine learning is so advanced that blocking phishing attempts gets easier every day. Taking a look at the tech giants, Google is now using ML on Gmail to block 18M daily malware and phishing emails last April, just like Microsoft is applying heuristics, detonation, and machine learning to prevent billions of phishing attempts on Office365.

That, however, doesn’t put us in the clear. There’s still a lot of work to be done regarding the effectiveness, compliance, privacy, and adaptability of the risk scores generated by machine learning. What’s more, we will likely run into new and more complex threats as technology continues its course. Having the proper IT infrastructure and know-how will always be the key to staying one step ahead of phishing. 

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