Role of Confusion Matrix in evaluating Cybersecurity Analytics Systems

In the present world, cybercrime offences are happening at an alarming rate and will continue to increase as long as we’ve been interconnecting computers across the internet because we’re putting more and more important information, valuable information, resources that have actual monetary work on IT systems. So as the system gets more complex, the threat space and the size of the target that we place on these systems also increases. The alerts that people are attacking and doing certain techniques using different types of attack vectors also continues to change and increase. To deal with the scenario, market experts and SIEM application developers are trying to implement artificial intelligence and machine learning algorithms for cybersecurity solutions like event and log management, behavioral analysis, and real-time monitoring of databases and applications as one of the ways to withstand modern cyber-attacks.

In the case of cybersecurity, advanced analytics is used to find deeper correlations, make predictions, and provide recommendations by automating data processing with the help of deep learning algorithms. It can process large volumes of data, or what we now call Big Data using neural networks that simulate the activity of the human brain.

There are two categories of advanced analytics: predictive and prescriptive.

Using sophisticated statistics and machine learning techniques, predictive analytics analyses historical and current data to predict what will happen in the future, and what you should expect. It makes security solution more effective at detecting attacks before data leakage or outside intrusion happens. However, predictive analytics does not provide a plan of action when the network is under attack.

Prescriptive analytics not only predicts future events and help you detect an attack before it happens but also analyzes possible outcomes and suggests what actions you should take given a particular expected outcome.

Whatever model we choose, it can never be give hundred percent accurate predictions. That’s where the confusion matrix comes in play to help in monitoring and mitigating cyber attacks.

What is Confusion Matrix?

A confusion matrix is used to evaluate the classification model performance in machine learning where output can be two or more classes. It is a 2 x 2square matrix that contains different combinations of predicted and actual values. These combinations help in classifying the right and the wrong cases in machine learning models. Confusion matrix contains information on actual and predicted classifications done by a classifier. The performance of a cyber-attack detection system is commonly evaluated using the data in a matrix. The matrix below is the representation of a confusion matrix.

True Positive (TP): Predicted value is positive and the actual value is also positive.

False Positive (FP): Predicted value is positive but the actual value is negative. It represents the Type I error.

False Negative (FN): Predicted value is negative but the actual value is not negative. It represents the Type II error.

True Negative (TN): Predicted value is negative and the actual value is also negative.

Role of Confusion Matrix in Cybersecurity

In the case of the binary classifier models, four outcomes are possible which we can analyze using the confusion matrix. The four instances (TP, TN, FP, and FN) are the outcome of comparing two actual classes with the two predicted classes. Different performance metrics are defined in terms of the confusion matrix variables are useful for the comparison of the systems. It can be used to ensure the security of any physical equipment, identity, database or network.

Let us take a real world use case of performance analysis of security systems using confusion matrix.

How Confusion matrix is used in evaluating an IDS?

An Intrusion Detection System (IDS) is a network security technology built for detecting network vulnerability exploits by monitoring network traffic for suspicious activity and issues alerts when a harmful activity or any policy breach is discovered. The system monitors the activity within a network of connected computers in order to analyze the activity for intrusive patterns. The confusion matrix is the best way to represent the classification results of the IDS and evaluate its ability to make accurate predictions of threats and attacks. Then, a SIEM system integrates outputs from multiple sources and uses alarm filtering techniques to differentiate malicious activity from false alarms and take appropriate actions.

True positive: Intrusions that are successfully detected by the IDS which means the system is secure and preventive measures can be correctly executed in case of any breach.

False Positive (Type I error): Intrusions that are missed by the IDS, and classified as non-intrusive. In this case, our model predicts the system is secure but our system is actually under attack. This is the most critical because we are not able to get alert at the right time and haven’t taken any immediate action. The longer it takes to respond, the more data that gets leaked, the more damage that’s done. If you don’t respond quickly enough and notify all the people that need to be notified of a breach, it will cost your company significant money as well in terms of fines.

False Negative (Type II error): Non-intrusive behavior that is wrongly classified as intrusive by the IDS meaning that our model detects that the system is insecure and preventive actions will be carried out even if there is no threat. However, this does not lead to any actual harm to the system.

True Negative: Non-intrusive behavior that is successfully labelled as non-intrusive by the IDS means the system is still secure.

Therefore, from the above discussion, we could conclude that the Type I error is more dangerous than the Type II error. SIEM system can use confusion matrix to monitor for Type I errors and reduce the chances of attacks as much as possible.


A confusion matrix is a remarkable approach for evaluating a classification model in cybersecurity systems. It provides accurate insight into how correctly the model has classified the activities depending upon the data fed or how they are misclassified. It gives the comparison summary of the predicted results and the actual results in any classification problem use case in cybersecurity. The comparison summary is extremely necessary to determine the performance of the model after it is trained with some training data so as to ensure the complete security of our systems at all times against any threat and vulnerabilities. So this is how the confusion matrix help in cyber attack monitoring. The team checks the matrix and evaluates everything, and even tries to reduce the type 1 error as much as possible.