Two conventional approaches, supervised and unsupervised learning, form the very basics of machine learning models. While both methods are essential in extracting insights from the data, they differ significantly in their methodology and applications.
The cybersecurity landscape has been facing rapid changes, and machine learning is turning out to be a strong tool that helps detect, prevent, and respond to cyber threats effectively. Both supervised and unsupervised learning techniques form the basis for enhancing digital defenses. In this article, various use cases of these two machine learning approaches will be discussed in relation to cybersecurity.
Supervised Learning: Learning from Labeled Data
In supervised learning, the algorithm is fed with labelled datasets, which means input data is provided with their desired output. That’s roughly how one learns from examples in order to generalize new, unseen data. This is a model that gets better and better with time, as each prediction is contrasted with the known correct answer.
Supervised learning can be subdivided into two categories:
Classification: In this type, a model predicts discrete class labels, such as classifying emails as “spam” or “not spam”. Common classification algorithms include linear classifiers, SVMs, decision trees, and random forests.
Regression: In this type of problem, the model predicts continuous values, such as prices or probabilities. This includes linear regression and logistic regression.
Applications of supervised learning in cybersecurity
1. Malware Detection and Classification
Supervised learning algorithms can be trained on large datasets of known malware samples to identify and classify new, previously unseen malicious software. This enables:
- Rapid identification of malware families.
- Detection of polymorphic malware, which changes its code to evade detection.
- Classification of malware regarding either its behaviour or its intended purpose.
2. Phishing Detection
Using features such as the structure of the URL, the content, and the metadata from known phishing attempts, supervised models can:
- Identify potential phishing emails or websites.
- Provide a score on the likelihood that a link might be malicious.
- Flag suspicious content for further investigation.
3. Intrusion Detection Systems (IDS)
Supervised learning enhances IDS by adding value in:
- Analyzing network traffic patterns and identifying anomalous behaviour.
- Detecting known attack signatures in network traffic in real-time
- Reducing false positives in alerting systems
4. User Behaviour Analytics
By training from historical user behaviour data, the following can be enabled by a supervised model:
- Detect compromised accounts or insider threats.
- Detect anomalous access or attempted data exfiltration.
- Raise alerts on potential security policy violations.
Unsupervised Learning: Uncovering Hidden Patterns
Unsupervised learning deals with unlabeled data. Such algorithms themselves generalize by discovering hidden structures or patterns in the data. Generally speaking, unsupervised learning is used to accomplish three tasks:
Clustering: This is the process of gathering similar data points together. For instance, in a business, it may be done in customer segmentation based on age, location, or spending pattern.
Association: These algorithms find the association between different variables of the dataset. Market basket analysis by a business to know which items are usually bought together is one practical implementation.
Dimensionality Reduction: A technique for data reduction that includes reducing the number of variables in the dataset but strives to retain as much information as possible. It is usually used while preparing the data for further processing, such as filtering and enhancement of images by reducing noise.
Applications of unsupervised learning in cybersecurity
1. Anomaly Detection
Unsupervised algorithms are very good at finding unusual patterns that might indicate security threats, such as:
- Detection of zero-day attacks that do not match any known signature
- Anomalous network traffic or system behaviour detection.
2. Clustering for Threat Intelligence
By clustering similar security events or indicators, unsupervised learning helps to:
- Discover new patterns or techniques of attack.
- Group similar security incidents for their effective analysis.
- Find the correlation among apparently uncorrelated events.
3. Network Behavior Analysis
Unsupervised models can analyze the network traffic to:
- Establish a baseline of normal network behaviour.
- Identify deviations that may indicate that systems have been compromised or are part of DDoS attacks.
- Identify potential command and control (C2) communications.
4. Fraud Detection
In financial cybersecurity, unsupervised learning serves the following purposes:
- Identifying unusual patterns of transactions.
- The detection of new fraud schemes that it is impossible to match up with any known pattern.
- Grouping similar fraudulent activities for investigation.
Key Differences and Considerations
The general performance of a supervised learning model is better than that of an unsupervised model. However, they require quite a substantial amount of human intervention to label data in advance.
On the other hand, unsupervised learning models work autonomously to discover underlying structures in unlabelled data. They can group like items together but can’t make predictions like supervised models. For example, unsupervised images would be classified by what’s contained within them, such as people, animals, or buildings, without any knowledge that these classes existed previously.
Making the Right Choice
While supervised learning is generally applied because of its accuracy and efficiency, unsupervised learning boasts a few advantages. It can deal with unlabelled data, which is all too common when real-world data sets come into the equation, and sometimes uncovers hidden patterns that may sometimes elude a supervised model.
With large volumes of big data, supervised learning has proved effective in classification tasks, churning out very accurate and reliable results. Unsupervised learning may also process volumes of data in real-time but often lacks transparency in the clustering process, adding a higher risk for inaccurate results.
Semi-Supervised Learning: A Hybrid Approach
When purely supervised or unsupervised learning methods cannot be appropriately used, semi-supervised learning offers a middle path. Both labelled and unlabelled data form part of the training dataset in semi-supervised learning. It is especially applicable to the analysis of such volumes of data, where feature extraction by hand would be burdensome.
Many modern cybersecurity solutions combine both supervised and unsupervised learning techniques, including:
1. Advanced Persistent Threats (APT) Detection
Supervised models identify known APT indicators. Unsupervised models detect subtle, long-term anomalies characteristic of APTs.
2. Security Information and Event Management (SIEM)
Supervised learning categorizes and prioritizes security events based on type and criticality, while unsupervised learning finds hidden patterns in log data.
3. Automated Threat Hunting
Supervised models guide initial threat hunting using known indicators. Unsupervised learning will find new patterns that may be malicious and should be subject to investigation.
The choice between supervised and unsupervised learning depends upon the nature of your data and goals. Both are robust approaches that can be very powerful in extracting great insight from data. With the continuous evolution of machine learning, these models play an increasingly significant role in improving many aspects of cybersecurity.
