What Is Machine Learning for Cybersecurity?

Cyberattacks are becoming increasingly more complex and frequent, and the rise of artificial intelligence (AI) has resulted in more sophisticated attacks that use machine learning (ML) and automation to maximize impact. In a CompTIA survey on the state of cybersecurity, 47 percent of surveyed organizations cited the rise of generative AI as a reason for cybersecurity action, and 78 percent said that cybersecurity was a top priority for their organization [1]. This is where ML, a subset of AI, emerges as a significant cybersecurity tool.

ML’s capability to rapidly analyze large data sets, recognize patterns, predict threats, and automate responses allows for the mitigation of cybersecurity threats with speed and accuracy. In fact, in a MixMode survey, 70 percent of surveyed organizations recognized the effectiveness of AI, which includes ML, in detecting cyber threats that would have previously gone unnoticed [2]. This highlights the transformative role of ML in the cybersecurity landscape.

Explore the applications of machine learning for cybersecurity, including the advantages it brings, real-life use cases, and potential cybersecurity jobs that benefit from ML skills. 

What is machine learning?

Machine learning (ML) is a subfield of AI that focuses on teaching machines to imitate human intelligence processes so they can learn autonomously, ultimately helping to automate tasks, solve problems, and make predictions with accuracy. 

The first step in ML involves collecting and preparing vast amounts of data from different sources for model training. The next step requires selecting a suitable model or algorithm based on the specific task, training it by feeding it the collected data, and allowing the model to learn by making patterns and predictions. At this stage, programmers can also change the internal parameters to drive more accurate results. Once you have trained the ML model, you must further test it on a separate data set to evaluate its performance on unseen data. 

According to Statista, the global ML market is expected to increase at a compound annual growth rate (CAGR) of 32.41 percent from 2025 to 2031 [3]. Due to its powerful prediction capabilities, ML is increasingly being applied to several fields, such as health care, finance, and more. 

One such field is cybersecurity. You can start exploring the applications of ML in cybersecurity below.

Is machine learning used in cybersecurity?

Yes, ML is used in various aspects of cybersecurity, such as threat prediction, malware detection, and endpoint security. Using ML offers a proactive approach to cybersecurity, allowing you to identify threats before they can do damage. ML’s ability to analyze large data sets to identify anomalous behaviors, automate time-consuming tasks, and continuously learn and adapt to changing threats makes it particularly suitable for use in cybersecurity. 

Threat detection

The detection of cyber threats can follow two approaches: 

• Misuse-based: This approach, also known as signature or rule-based methods, defines a pattern for a specific threat and assumes that future threats will follow the same pattern, which makes it precise but limited to detecting only known threats.

• Anomaly-based: This approach sets a baseline for normality and assumes that deviations from normality correspond to a threat, which makes it suitable for detecting novel attacks but generates more false positives. 

Applying ML models to automate the threat detection process involves supplementing these traditional methods with a generalized approach that can differentiate between threats and non-threats and quickly detect new threats. ML models analyze large volumes of network and user data, as well as past security incidents, to accomplish the following:

• Identify patterns

• Predict potential threats 

• Distinguish between benign and malicious acts

• Offer actionable insights to cybersecurity teams 

All of this allows for quick responses and reduces the vulnerability window. 

Anomaly detection

ML models analyze vast amounts of network data against a predefined baseline for regular activity and highlight irregularities, or anomalies, that could indicate potential security threats. This can help discover threats like insider attacks or policy violations. Additionally, through constant learning, the ML model can become increasingly more effective at identifying anomalies by recognizing even subtle indicators of threats, allowing faster response and more efficient detection.

Malware detection 

Conventional signature-based methods often fail against new and evolving malware strains. Instead of learning from signatures, ML models analyze file behaviors and attributes and adapt to changing malware codes, allowing them to detect new or unknown malware, detect malware in encrypted traffic, and predict malware behavior, allowing cybersecurity systems to proactively block malware code.

Intrusion detection

ML models can enhance the accuracy of intrusion detection systems (IDSs) that monitor network or system behaviors for suspicious activity by distinguishing between malicious and benign traffic. In this way, ML-improved IDSs reduce false positives, increase detection rates, and protect against unauthorized access. ML models constantly learn from intrusion attempts, which allows IDSs to respond adaptively to evolving cyber threats.

Endpoint security

You can use ML models to improve your endpoint security strategies at the device level. By processing real-time data from endpoints, ML models can continuously monitor and detect irregularities and threats across mobile and other devices, preventing unauthorized access and improving endpoint management.

Threat intelligence

ML models can analyze and identify patterns across vast amounts of security information, including attack trends and security logs, offering actionable intelligence insights and automating intelligence sharing. This reduces the need for time-consuming manual analysis of information, even when real-time updates are coming in. 

Threat response

You can program ML systems to classify and actively respond to cyber threats. You can use ML models to automate the blocking of suspicious IP addresses and the separation of compromised devices, reducing response times and providing real-time protection. 

Fraud detection

ML algorithms can learn from your spending habits and analyze transaction data in real-time to detect unusual spending, suspicious log-in attempts, and fraudulent card transactions.

What is the primary benefit of using machine learning in cybersecurity?

The main benefit of using ML in cybersecurity is the rapid analysis of large volumes of data. A common challenge cybersecurity teams face is the need to quickly analyze intelligence insights across attack areas, which are usually generated much faster than they can manually handle. ML algorithms can easily process vast data sets, allowing organizations to detect and respond to threats in real-time. 

Other benefits of using ML in cybersecurity include the following:

• Task automation: You can apply ML models to automate repetitive or time-consuming tasks, such as data analysis or continuous monitoring of devices or networks, freeing analysts to focus on more urgent matters. 

• Predictive analytics: By analyzing large amounts of data to identify patterns, ML models can identify subtle anomalies and anticipate future attacks, allowing you to strengthen your cybersecurity defenses beforehand.

• Improved threat detection: ML algorithms can automate the threat-hunting process to process data rapidly, continuously monitor network and user behavior, and detect threats more precisely, allowing cyber teams to mitigate threats before they cause damage.

• Exposing vulnerabilities: ML models can allow businesses to constantly monitor their systems and networks for anomalies or potential weak spots in their defenses, reducing the risk of data loss or financial loss.

• Real-time monitoring: ML models analyze data instantly, alerting organizations of any breaches as they happen, ultimately reducing response time and potential damage.

• Incident response: As ML models continuously learn and improve, you can program them to swiftly identify and respond to threats in real-time by isolating breached systems, ultimately reducing recovery time and ensuring scalability.

Machine learning for cybersecurity: Real-life examples

To combat the threat of cyberattacks, many organizations are leveraging AI and ML technologies to bolster their cybersecurity defenses. For example, the 2024 MixMode survey found that 62 percent of organizations could identify areas in their cybersecurity framework where ML would be beneficial [2]. Explore how some organizations are using ML in cybersecurity below.

PayPal

PayPal employs ML for payment fraud detection and protection. By analyzing data from a large volume of monthly transactions and a global customer base, PayPal’s ML model learns from each transaction to detect anomalous activity. Using ML, PayPal optimizes its fraud analysis, ensuring the safety of its products for businesses and individuals alike.

Amazon AWS GuardDuty

Amazon’s GuardDuty service uses ML to detect anomalies and provide insights into security risks, analyzing large amounts of data from multiple AWS sources to more effectively mitigate threats. Its ML approach establishes a normal behavior baseline to track suspicious activities like unusual user logins, allowing more accurate identification of abnormal activities based on known attack tactics.

Siemens

Siemens offers an intrusion detection solution powered by an unsupervised ML algorithm trained on data for normal network communications, allowing the passive monitoring of the entire network and the detection of anomalies or threats in real-time. Additionally, Siemens Energy leverages an AI and ML-based Managed Detection and Response platform called Eos.ii to gather real-time energy asset data and provide actionable intelligence to its cybersecurity teams. This allows them to detect attacks before they happen and implement suitable defense strategies.

Machine learning cybersecurity jobs

Organizations are increasingly recognizing the role of ML in combating cyber threats, and as a result, the demand for cybersecurity professionals with AI and ML skills is growing. Discover some cybersecurity jobs that can benefit from AI/ML skills below:

AI/ML security engineer

Average annual salary in the US (Glassdoor): $135,756 [4]

AI and ML security engineers design, build, and implement AI systems, making sure that they are secure against attacks. AI/ML security engineers assess new and established AI systems and design and implement ML models for cybersecurity purposes. 

Cybersecurity analyst

Average annual salary in the US (Glassdoor): $101,970 [5]

Specializing in network and IT infrastructure security, cybersecurity analysts work to protect from malware, cyber attacks, and the behavior of cyber criminals. Cybersecurity analysts with AI/ML skills leverage AI/ML tools to detect threats and areas of weakness by analyzing security data.

Threat intelligence analyst

Average annual salary in the US (Glassdoor): $117,417 [6]

As a threat intelligence analyst, your role is to prevent illegal access to personal data. Threat intelligence analysts can use ML to analyze threat data and anticipate future cyberattacks.

Explore machine learning and cybersecurity on Coursera

As cyberattacks become increasingly frequent, machine learning emerges as a powerful tool for enhancing cybersecurity strategies. From threat detection to incident response, machine learning can streamline cybersecurity processes by analyzing vast amounts of security data and predicting future attacks.

To build or add to your machine learning or cybersecurity skills, explore a range of courses and Professional Certificates on Coursera. For example, the IBM Machine Learning Professional Certificate gives you the opportunity to develop a foundational understanding of machine learning models. If you want to focus on cybersecurity, you can consider the Google Cybersecurity Professional Certificate, which will demonstrate the importance of cybersecurity for businesses and how to identify and defend against cyber threats.