In this article, We are going to discuss confusion matrix use cases and it’s role in cybersecurity.
What is the confusion matrix?
A confusion matrix is a table that is often used to describe the performance of a classification model on a set of test data for which the true values are known.
Let’s now define the most basic terms used in the confusion matrix
- True Positive(TP): Predicted class is positive and actual class is positive then it is a True Positive(TP)
- True Negative(TN): Predicted class is negative and actual class is negative then it is a True Negative
- False Positive(FP): Predicted class is positive and the actual class is negative. also known as (Type 1 error)
- False Negative(FN): Predicted class is negative and the actual class is positive. also known as (Type 2 error)
Support Vector Machines (SVM) are the classifiers that were originally designed for binary c1assification. The c1assificatioin applications can solve multi-class problems. The result shows that pSVM gives more detection accuracy for classes and comparable to the false alarm rate.
Cyberattack detection is a classification problem, in which we classify the normal pattern from the abnormal pattern (attack) of the system.
The SDF is a very powerful and popular data mining algorithm for decision-making and classification problems. It has been using in many real-life applications like medical diagnosis, radar signal classification, weather prediction, credit approval, and fraud detection, etc.
A parallel Support Vector Machine (PSVM) algorithm was proposed to detect and classify cyber-attack datasets.
The performance of the support vector machine is greatly dependent on the kernel function used by SVM. Therefore, we modified the Gaussian kernel function in a data-dependent way to improve the classifiers' efficiency. The relative results of both the classifiers are also obtained to ascertain the theoretical aspects. The analysis is also taken up to show that PSVM performs better than SDF.
The classification accuracy of PSVM remarkably improves (accuracy for Normal class and DOS class is almost 100%) and comparable to false alarm rate and training, testing times.
That's all for this article.
Thank you for reading…