Can machine learning be used to detect phishing emails?

Yes, machine learning can detect phishing emails.

What are phishing emails?

Phishing emails impersonate trusted entities to get sensitive information from individuals and organizations. “Scammers use email or text messages to try to steal your passwords, account numbers, or Social Security numbers. If they get that information, they could get access to your email, bank, or other accounts,” the FTC’s article on How To Recognize and Avoid Phishing Scams explains.

The nature of phishing attacks

Phishing is based on deception and social engineering. It manipulates users into clicking on malicious links or sharing confidential information. As evidenced in a Critical Review on Phishing Email Detection Using Machine Learning, “Phishing is a scam tactic that uses email correspondence to get private information by impersonating reliable sources.” These attacks “involve deceiving victims into divulging personal information such as bank account details, passwords, and account IDs.”

The scale of phishing activity has increased over time, “reaching 128,926 in Q3 2020 as opposed to 44,497 in Q2 and 44,008 in Q1,” according to the Anti-Phishing Working Group (APWG).

Typical detection approaches and their limitations

Historically, phishing detection has relied on rule-based systems, signature detection, and blacklists. While these methods remain in use, they present several limitations. More specifically, “Blacklist systems depend on the labor-and time-intensive process of user identification and reporting.”

Consequently, new phishing campaigns can evade detection until they are identified and added to a blacklist. Additionally, rule-based systems struggle to adapt to new attack patterns, especially when attackers modify language, formatting, or sender details.

Furthermore, the sophistication of phishing techniques has increased, making it a major cybersecurity issue. As a result, researchers are exploring automated detection methods, particularly machine learning (ML), as an adaptive solution.

Using ML for phishing detection

Machine learning learns patterns from previously labeled data, making it a data-driven method for identifying phishing emails, i.e., ML models classify emails based on extracted features.

These systems usually analyze the structural and textual components of emails, including:

• Sender information and routing paths
• Subject lines and message content
• Embedded URLs
• Metadata such as timestamps and IP addresses

In addition, natural language processing (NLP) is frequently used alongside ML to interpret textual content and detect linguistic patterns associated with phishing.

Common ML algorithms in phishing detection

Support Vector Machine (SVM)

“SVM is a supervised machine learning method… aiming to find an ideal hyperplane to separate data points from different classes.”

For example, an SVM model converts emails into numerical features (such as suspicious links or urgent language) and plots them in a multi-dimensional space. It then finds the optimal boundary to separate phishing and legitimate emails and classifies new emails based on which side they fall on.

Naïve Bayes (NB)

A Naïve Bayes model calculates the probability that an email is phishing based on features such as keywords, links, and sender information. “The NB classifier… gauges the likelihood of an occurrence falling into a given course based on watched highlights.” It, therefore, classifies the email by selecting the category (phishing or legitimate) with the highest probability.

Long short-term memory (LSTM)

“LSTM networks… offer a significant advantage… in their ability to learn and retain information over extended sequences.” An LSTM model reads an email word by word, remembering earlier phrases like “your account” and connecting them with later urgency cues like “verify immediately” to understand the context. It then uses this sequence of information to classify the email as phishing if the combined pattern matches known attack behaviors.

Other algorithms

Additional methods include Random Forest, Logistic Regression, Decision Trees, K-Nearest Neighbors (KNN), and Convolutional Neural Networks (CNN). Each offers different trade-offs in terms of interpretability, computational cost, and performance.

In terms of performance, the critical review proves that some models consistently outperform others in phishing detection tasks. “The best classifiers for identifying email phishing assaults are SVM, NB, and LSTM, with accuracy rates of 99.62%, 97%, and 98%, respectively.” So, while many algorithms can be effective, certain models are more accurate when applied to well-prepared datasets.

Feature extraction and NLP

Machine learning models work best when they are given the right kind of information from emails. Feature extraction pulls out clues, like suspicious links, unusual sender details, or certain words, from an email so the model can analyze them.

For example, the system might:

• Check if a link looks fake (URL analysis)
• Look at who sent the email and how it was routed (email headers)
• Analyze the message text for risky phrases (NLP)
• Notice unusual sending patterns (behavioral clues)

NLP (Natural Language Processing) helps the model understand the meaning and tone of the email, since the system can detect subtle warning signs that may be less obvious.

ML for phishing detection in HIPAA compliant email systems

The law dictates that when emails contain protected health information (PHI), organizations comply with HIPAA. In other words, organizations must implement safeguards to protect patient data from unauthorized access or disclosure. Machine learning (ML) can help improve email security in this context, as it reduces the risk of phishing-related breaches.

Learn more: HIPAA Compliant Email: The Definitive Guide (2026 Update)

How ML improves HIPAA email security

Machine learning analyzes patterns in email data and identifies anomalies that may indicate malicious intent, improving phishing detection. ML models also adapt over time as new phishing techniques arise.

In healthcare organizations, ML systems can evaluate the content and structure of emails containing PHI to detect suspicious behavior before the message reaches healthcare staff.

Practical application in healthcare email systems

A machine learning-based HIPAA email security system typically scans incoming and outgoing messages. It evaluates features like:

• Sender identity and domain authenticity
• Email routing paths and IP addresses
• Content of messages containing PHI
• Embedded links and attachments
• Behavioral patterns of email traffic

For example, if an email claims to come from a hospital’s internal billing department but originates from an external or unfamiliar domain, the system may flag it as suspicious. Similarly, if an email requests immediate access to patient records or urges staff to bypass normal verification procedures, the model may classify it as a potential phishing attempt.

ML models used in HIPAA email protection

Support Vector Machine (SVM)

In HIPAA environments, SVM can analyze structured email data to detect subtle differences between genuine internal communications and spoofed messages. For instance, an email pretending to be from a hospital IT department may be flagged if its metadata does not match known internal communication patterns.

Naïve Bayes (NB)

Naïve Bayes is widely used in email filtering due to its efficiency and probabilistic approach. In healthcare, NB can flag emails containing suspicious phrases like “urgent patient update required” or “confidential access needed immediately,” especially when combined with unknown sender domains.

Long short-term memory (LSTM)

LSTM models are particularly useful in HIPAA compliant email systems because they understand context over sequences of text, since legitimate healthcare emails may also contain medical urgency.

For example, an LSTM model can distinguish between a legitimate message like “patient results are ready for review,” and a phishing attempt like “patient records must be verified immediately to avoid account suspension,” based on contextual patterns rather than isolated words.

Feature extraction in HIPAA email security

Feature extraction is required for training ML models to detect phishing in healthcare settings. It involves identifying relevant signals from email data that may indicate risk.

In HIPAA compliant systems, feature extraction typically includes:

• Email header analysis: Verifying sender domains, routing paths, and authentication results.
• URL inspection: Detecting fake or suspicious links that mimic healthcare portals.
• Content analysis: Identifying language patterns associated with urgency or coercion.
• Behavioral monitoring: Tracking unusual sending patterns within hospital networks.

For example, if a user who typically sends internal administrative updates suddenly sends an email with an external attachment requesting PHI access, the system may flag it for review.

NLP in healthcare email protection

NLP enhances ML models, allowing them to interpret the meaning and intent behind email content, where language can be complex and medical jargon may be used.

NLP allows systems to:

• Detect urgency-based manipulation (“immediate action required”)
• Identify impersonation attempts (“IT support team requesting login credentials”)
• Understand contextual inconsistencies in medical communication
• Differentiate between normal clinical language and phishing attempts

For example, an email stating, “Your hospital account will be deactivated unless you confirm patient access credentials now,” may trigger a phishing alert because NLP detects coercive language combined with authentication requests.

Accuracy in HIPAA compliance

In healthcare, we need accuracy because false positives and false negatives have consequences. False positives may block legitimate clinical communication, delaying patient care, and false negatives may allow phishing emails to reach staff, risking PHI exposure.

High accuracy is also important in HIPAA contexts because a successful phishing attack can lead to regulatory violations, financial penalties, and reputational damage.

Go deeper: The complete guide to HIPAA violations

HIPAA compliance benefits of ML

1. Prevention of unauthorized PHI access

ML systems reduce the likelihood of attackers gaining access to patient records through phishing attacks.

2. Continuous monitoring

ML models operate continuously and can detect threats immediately.

3. Adaptive security

ML systems change with new phishing techniques, improving long-term protection.

4. Reduced human error

Since phishing exploits human behavior, ML acts as an automated safeguard that supports healthcare staff. As evidenced in the critical review, “Social engineering assaults… manipulate human psychology and behaviour rather than relying solely on advanced technological knowledge.”

Therefore, ML filters suspicious emails before they reach users, and ML reduces the risk of accidental data exposure.

The future of ML in HIPAA email security

Future improvements are likely to focus on enhancing contextual understanding and system integration. In the future, we may see more systems combining ML with encryption-based verification systems, improved detection times, and better contextual understanding of clinical communication.

FAQs

How can machine learning improve HIPAA compliance?

Machine learning analyzes large amounts of healthcare data to detect risky patterns that may threaten protected health information (PHI). It can automatically flag suspicious emails, unusual access behavior, and potential phishing attempts before they become security incidents. It also helps organizations proactively strengthen security controls and address vulnerabilities, reducing the risk of HIPAA violations.

Can machine learning stop all phishing attacks?

No. While ML significantly reduces risk, it cannot stop all attacks because phishing tactics constantly evolve and often rely on human error.

What happens if a phishing email is not detected?

If undetected, it may lead to unauthorized access to healthcare systems, exposing sensitive patient data and potentially resulting in HIPAA violations.