Machine Learning in Cybersecurity: A Primer for Beginners

Machine learning (ML) is a branch of artificial intelligence (AI) that enables computers to learn from data and make predictions without being explicitly programmed. Today, ML has many applications across industries, such as healthcare, finance, education, and entertainment. But what about machine learning in cybersecurity? How can ML help protect our data and systems from cyberattacks?

In this post, we’ll explain basic concepts and types of machine learning and how they can be used to make various cybersecurity tasks more efficient and effective. Whether you’re a cybersecurity professional, business leader, or curious reader, here’s an overview of the benefits and challenges of using machine learning in cybersecurity and what developments in ML could mean for the future of cybersecurity.

• What is machine learning?
• Types of machine learning
• How does machine learning work in cybersecurity?
  • Cyber threat intelligence
  • Anomaly detection
  • Cyber risk quantification
  • Vulnerability management
  • Intrusion detection systems
  • Spam detection
  • Malware detection
  • Endpoint security
• Benefits and challenges
• Machine learning as a powerful tool for strong cybersecurity

Introduction to machine learning

ML can seem daunting to understand. Let’s break down what ML means, including different types and related use cases to know.

What is machine learning?

Machine learning focuses on the use of data models and statistical algorithms to imitate the way a human brain learns to gradually improve its accuracy over time.

When you talk about machine learning, you’re talking about a subset of artificial intelligence on which other types of AI are built. For example, large language models (LLMs) are a recent development in ML that employs deep learning techniques to analyze extremely large data sets faster and more efficiently than manual analysis.

ML algorithms also include different AI technologies, such as neural networks and natural language processing (NLP), which learn from data and can perform a wide array of tasks with and without the need for explicit instructions. Let’s explore some common types of ML.

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Types of machine learning

There are several types of machine learning, which mainly differ in the training data provided and the level of human involvement required during ML model training.

• Supervised ML: Supervised ML involves using labeled datasets that train models to predict outcomes based on the provided training. The main goal of supervised learning is to map an input variable with an output variable. Supervised ML also has two main categories:
  1. Classification: Algorithms address classification problems and the output variable is categorical
  2. Regression: Input and output variables have a linear relationship
• Unsupervised ML: With unsupervised learning, the ML model is trained using an unlabeled dataset to predict outcomes without supervision. The model learns from data without help from humans. Unlike supervised learning, unsupervised ML models can discover insights and patterns without any guidance.

While many refer to unsupervised learning as self-learning since both models don’t require labeled datasets, some argue self-learning is a subset of unsupervised learning since it uses supervisory signals as feedback during training.

• Semi-supervised learning: Semi-supervised machine learning falls in between supervised and unsupervised ML, as it uses a small amount of labeled data and a large amount of unlabeled data to train models.
• Reinforcement learning: This is an ML training method based on rewarding desired behaviors and punishing undesired ones. The model being trained can perceive and interpret its environment and then act, learning through trial and error. Unlike supervised ML, reinforcement learning lacks labeled data.

But what does machine learning have to do with helping combat today’s cybersecurity challenges? Let’s review why ML is quickly becoming an essential part of the growing field of AI in cybersecurity.

How does machine learning work in cybersecurity?

With cybersecurity attacks constantly on the rise, these threats are also getting increasingly sophisticated as bad actors seek new vulnerabilities and use different approaches to infiltrate defenses. This means organizations must look for different ways to minimize their enterprise attack surface while also hardening their growing IT infrastructure. And they often need to do this with limited resources.

How can CISOs manage this perfect storm scenario? One answer is to improve their cybersecurity practices by integrating machine learning capabilities. Machine learning in cybersecurity can support these efforts in several ways, including:

• Quicker data analysis with greater accuracy: ML models can analyze large amounts of data rapidly and with less human error.
• Faster threat detection and response times: ML and AI systems can quickly detect potential threats, identify suspicious activity, and automate actions to isolate and address threats before they do damage.
• Forecasting future threats: ML models can be trained to predict future threats and proactively remediate potential breach risks by identifying when typical system or user behavior patterns fall outside normal ranges.

Many capabilities of ML show promising results in redefining and modernizing traditional methods of cyber security, including threat intelligence, anomaly detection, cyber risk quantification, and vulnerability management. Additionally, the deployment of ML can enhance and improve existing security solutions like intrusion detection, spam detection, malware detection, and endpoint management to provide organizations with the comprehensive approaches needed to defend against today’s cyber threats.

[Read also: What is the MITRE ATT&CK framework? Insights about getting started, including real-world examples and mapping templates]

Role of ML on cyber threat intelligence

Cybersecurity threat intelligence is knowledge about the occurrence and assessment of the latest cybersecurity threats and threat actors intended to help organizations better mitigate possible attacks. Threat intelligence information can come from social media, open-source intelligence, device log files, forensically acquired data, or data from network traffic.

Threat intelligence is an essential component of effective cybersecurity strategies because it can enable companies to be more proactive in determining the vulnerabilities and security efforts to prioritize based on active threats that represent the greatest risks to their business operations.

ML can offer a more dynamic approach to ingesting a wide array of threat intelligence by centralizing and identifying patterns across various sources of intelligence information, even as they receive real-time updates, removing the need for continuous, time-consuming, manual triage no matter the origin of the threat intelligence.

ML can also help automate the process of sharing the most recent threat intelligence insights among operations, IT teams, and other key stakeholders within the business, allowing organizations to benefit from the latest threat information related to possible business risks as soon as it’s available.

Machine learning for anomaly detection

ML and AI can discern complex patterns and behaviors that might indicate cyber threats. By analyzing historical data and current trends, algorithms in ML-driven systems can identify potential vulnerabilities and attack vectors to provide insights that become increasingly more effective at identifying and countering cybersecurity threats.

These AI-powered systems, equipped with advanced algorithms, can also quickly scan massive amounts of data to identify anomalies and potential security breach risks much more efficiently than human-driven detection methods. The ability to rapidly process and analyze vast amounts of data is vital given the growing volume and sophistication of threats.

AI-driven threat detection can also go beyond identifying known threats by leveraging pattern recognition to uncover subtle indicators of evolving threats. This proactive detection is crucial to reducing vulnerability and allowing organizations to respond before significant damage can occur.

Cyber risk quantification and machine learning

Organizations are facing cyber risks from a multitude of sources. For example, the growing number of endpoint devices connecting to an enterprise network, including remote and Internet of Things (IoT) devices, means cybercriminals have more potential entry points and a widening attack surface that continues to expand.

The rise of AI and increases in data collection are also giving rise to advanced modeling techniques for bringing cyber risk to the surface, according to consulting firm Deloitte.

Among other things, quantifying your cyber risk score can enable CISOs and CIOs to alert senior management and boards to the level of risk their organizations are facing at a given time. By clearly communicating the risks, these leaders have a better chance of acquiring the resources they need to mitigate them.

[Read also: How CISOs can build trust with board members]

Automating the process of cyber risk quantification (CRQ) using AI and ML can not only help create efficiencies and repeatable, improved risk insights but also allow organizations to distribute these insights at speeds that can potentially outpace threats.

Use of machine learning for vulnerability management

Vulnerability management has become a major priority for organizations. As a proactive approach to cybersecurity, vulnerability management leverages threat detection and remediation capabilities to help organizations prevent and resolve vulnerabilities in their infrastructure, code, and devices.

Using ML and AI with vulnerability management can add substantial benefits, including automation to reduce manual processes and address potential issues at scale, which can help organizations more easily keep up with the latest threats.

Machine learning in intrusion detection systems

ML models can be used with intrusion detection systems (IDS), devices, or services that monitor network security and system behavior for suspicious activity or security policy violations to improve their ability to detect cyberattacks.

Integrating machine learning models, including deep learning, into IDS can help enhance the accuracy of new data, reducing false positives, increasing detection rates, and allowing real-time monitoring for anomaly detection on networks.

Machine learning in spam detection

ML can also play a role in helping detect spam. A model can be trained using large datasets that include both spam and non-spam emails. The model is provided with examples of each type and labels indicating whether each message is spam or legitimate mail. Based on the examples, the model learns to recognize common spam characteristics, such as particular keywords or phrases, which teaches the ML model to recognize certain data patterns and features that distinguish spam emails from non-spam emails.

[Read also: What is business email compromise (BEC)?
And learn why BEC attacks are so costly]

Machine learning in malware detection

ML models can be trained to better detect malware compared to traditional antivirus software solutions. Via large training datasets consisting of both clean and malicious files, the models can discern features that distinguish between clean software and infected code.

Since models can be retrained and continue to learn, they can be especially effective at identifying new types of malware, like phishing emails, as they evolve.

Machine learning for endpoint security

ML can be used for improved endpoint security. Organizations must continually track the growing number of external and internal devices across changing environments. By using ML models that can learn from real-time data, organizations can enhance their visibility, detection, and incident response capabilities and better inform endpoint management.

ML can also help automate repetitive tasks, such as patching, updating, or configuring endpoints, freeing human resources for more important tasks like focusing on strategic activities.

Benefits and challenges of machine learning in cybersecurity

ML can deliver a variety of benefits, including the ability to quickly find and respond to threats and better leverage insights from data analytics. However, it can also present challenges, including complexity and the need for clean data.

Major benefits of machine learning in cybersecurity

To summarize, here are some of the advantages of ML use in cybersecurity:

• Finding and responding to threats: ML can help organizations detect cyber threats and mitigate them before they become a problem or quickly remediate when they’re found. This is especially valuable as cybersecurity threats continue to rise while security budgets and skills are stretched.
• Analyzing data: ML can predict future risks of data breaches, cyberattacks, and more by effectively analyzing massive sets of data coming in from various tools and other sources.
• Adding automation: By automating processes such as data analysis and other rule-based actions, ML can quickly find, isolate, and mitigate threats without the need for manual threat hunting or remediation actions.
• Safeguarding sensitive information: ML can monitor data patterns and flag any anomalous or suspicious behavior that might indicate a breach risk or unauthorized access, reducing the risk of data loss and any resulting financial losses organizations can experience due to successful cyberattacks.

[Read also: The not-so-hidden cost of unpatched devices]

Potential challenges and limitations of machine learning in cybersecurity

ML implementations for cybersecurity can come with challenges. Among the three most common challenges of ML in cybersecurity are:

• Poor quality or lack of data: One of the more crucial tasks in the ML model creation process is to use training data to achieve a desired output. Using unclean or having too little training data can result in negative outcomes, such as algorithms making inaccurate predictions or biased data.
• Complexity coupled with the need for related skills: Using ML is not a simple process, and the field is still relatively new and changing at a rapid pace.
• Rising sophistication of threats: ML can also be used by malicious actors to launch more advanced and targeted cyberattacks.

[Read also: 3 of the biggest GenAI threats to know about – and how to prevent them]

Despite these and other challenges, machine learning has given cybersecurity professionals, IT teams, and organizations across industries an effective tool for strengthening organizational defenses. And since ML is designed to grow and improve over time, future developments of ML and other AI technologies are sure to come that will unlock the full potential of ML for organizations seeking to improve their security operations and defend against cybercrime.

Machine learning as a powerful tool for strong cybersecurity

It’s clear machine learning can be applied to numerous aspects of cybersecurity, including cyber threat detection, threat intelligence, cyber risk quantification, data security, vulnerability management, intrusion detection, spam detection, malware detection, and endpoint security.

Tanium’s vision for Autonomous Endpoint Management (AEM) is a prime example of how ML and AI insights can be incorporated together to provide intelligent automation and decision-making capabilities for more effectively managing IT endpoints. AEM will take advantage of composite AI, which combines multiple AI strategies, such as ML, natural language processing, and other smart automation techniques for cybersecurity alongside other essential tasks, to create a powerful tool capable of solving real-world problems and completing additional tasks to the level of autonomy IT operations and security teams prefer and trust.

Through autonomous and AI-assisted capabilities, AEM at Tanium will leverage real-time data and insights from the Tanium Converged Endpoint Management (XEM) platform to make recommendations and automate actions based on peer success rates and customer risk thresholds, allowing organizations to make more informed decisions, decrease manual tasks, and ultimately improve security levels.

Learn more about Tanium’s approach to autonomous endpoint management and our goal of helping organizations enhance their security operations, systems, processes, and defenses against potential cybersecurity threats.