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How Text Mining Improves Phishing Detection How Text Mining Improves Phishing Detection
Phishing is among the most persistent threats all businesses face. As simple as these attacks are, they can be challenging to... How Text Mining Improves Phishing Detection

Phishing is among the most persistent threats all businesses face. As simple as these attacks are, they can be challenging to spot — especially for mistake-prone human users. Text mining can help organizations overcome that obstacle.

Text mining is not a complete solution in and of itself. However, businesses can see significant improvements by integrating this practice into their anti-phishing measures.

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What Is Text Mining?

As its name suggests, text mining is data mining with a specific focus on textual data. It uses analytical techniques and AI applications like natural language processing (NLP) to find unstructured text data and compile it into a structured database. In some cases, it also covers analyzing this information — uncovering patterns and insights from databases after structuring them.

While data analytics gets most of the attention today, it’s important not to overlook the importance of this mining process. Between 80% and 90% of all data is unstructured, much of which is text. Consequently, analytical tools that can only work with structured datasets fail to get the full picture.

How to Improve Phishing Detection Through Text Mining

Text mining has many applications, but phishing detection is one of its most beneficial from a cybersecurity perspective. Organizations can use it to improve their phishing recognition processes in several ways.

Expand Phishing Filters

The most obvious application of text mining in phishing prevention is to use it as the basis for spam filters. Many businesses already use phishing detection models, but 24% of AI adopters still cite a lack of clean data as a significant obstacle. Another 17% say they struggle to find the right information. These challenges limit filters’ efficacy, but text mining provides a solution.

Text mining techniques can supplement training databases with once-unstructured data to provide a larger sample of the words and phrases phishing attacks often use. With bigger, more diverse samples on hand, AI models can detect phishing attempts more accurately.

One study used text mining to achieve 99.2% accuracy in a phishing detection model. That’s a substantial improvement over conventional methods, considering older attempts struggled to break the 97% accuracy mark.

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Reduce False Positives

Similarly, text mining can reduce false positives in phishing detection algorithms. While AI tends to be more accurate than humans — users misidentified 41% of legitimate accounts in one study — it still produces a high number of false positives. These errors increase alert fatigue on security teams, leading to further mistakes and burnout.

Text mining can address this issue by providing more context in training. Businesses can use it to offer more variety in legitimate messages, not just in phishing examples. In turn, detection models can get a more nuanced understanding of innocuous messages to avoid false alarms.

In one study, detection algorithm accuracy reached 97% by including examples from 20 legitimate sites alongside hundreds of real-world phishing examples. Mining more legitimate examples could lead to even higher accuracy with fewer false positives.

Update Anti-Phishing Training Materials

Organizations can also use text mining to improve employees’ ability to spot phishing attempts. This step is easy to overlook but important because no automated solution will ever be 100% effective, so users must be able to spot phishing emails, too.

The best ransomware protections rely on real-world experience to prepare the workforce for threats they may face. Phishing simulations are an excellent way to implement such protections, but they must be realistic and varied to be effective. Text mining can provide trends from real phishing incidents to craft convincing simulations to better judge employees’ ability to spot attacks.

Over time, text mining will reveal new phishing methods and trends. Updating phishing simulations with these emerging patterns will ensure the workforce stays ready in an evolving risk landscape.

Other Necessary Anti-Phishing Best Practices

These text mining applications can improve any phishing prevention strategy. Still, they’re not a complete solution. While detection algorithms and employee training are crucial parts of anti-phishing measures, organizations must also embrace other protections.

Phishing attempts can still slip through the cracks, even with better-trained employees and more accurate AI. As generative AI fuels more convincing phishing messages — something nearly half of cybersecurity leaders have already witnessed — prevention will become more difficult. Consequently, businesses need measures to stop attacks once they’ve already penetrated the first line of defense.

Implementing the principle of least privilege is a good start. Tighter access controls will make a breached account less dangerous. Organizations can also employ user behavior analytics to spot potential hacked accounts. Text mining may improve these algorithms, too.

Best practices like keeping encrypted backups of all sensitive data and deploying automated detection and response still apply. Phishing emails have increased by 1,265% since ChatGPT’s launch, so these foundational practices are as pressing as ever. 

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Text Mining Makes Phishing Prevention Possible

It’s difficult to spot a phishing attempt today. Text mining makes it easier for both humans and automated filters.

Like all technologies, text mining is imperfect and requires complementary solutions to reach its full potential. However, organizations not using it are missing out on significant potential for improvement. Capitalizing on this practice is key to more reliable phishing prevention.

Zac Amos

Zac is the Features Editor at ReHack, where he covers data science, cybersecurity, and machine learning. Follow him on Twitter or LinkedIn for more of his work.

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