A confusion matrix is a table that is often used to describe the performance of a classification model (or "classifier") on a set of test data for which the true values are known. The confusion matrix itself is relatively simple to understand, but the related terminology can be confusing.
I wanted to create a "quick reference guide" for confusion matrix terminology because I couldn't find an existing resource that suited my requirements: compact in presentation, using numbers instead of arbitrary variables, and explained both in terms of formulas and sentences.
Let's start with an example confusion matrix for a binary classifier (though it can easily be extended to the case of more than two classes):
What can we learn from this matrix?
There are two possible predicted classes: "yes" and "no". If we were predicting the presence of a disease, for example, "yes" would mean they have the disease, and "no" would mean they don't have the disease.
The classifier made a total of 165 predictions (e.g., 165 patients were being tested for the presence of that disease).
Out of those 165 cases, the classifier predicted "yes" 110 times, and "no" 55 times.
In reality, 105 patients in the sample have the disease, and 60
patients do not.
cybercrime:
Cybercrime is any criminal activity that involves a computer, networked device or a network. While most cybercrimes are carried out in order to generate profit for the cybercriminals, some cybercrimes are carried out against computers or devices directly to damage or disable them, while others use computers or networks to spread malware, illegal information, images or other materials. Some cybercrimes do both -- i.e., target computers to infect them with a computer virus, which is then spread to other machines and, sometimes, entire networks.
A primary effect of cybercrime is financial; cybercrime can include many different types of profit-driven criminal activity, including ransomware attacks, email and internet fraud, and identity fraud, as well as attempts to steal financial account, credit card or other payment card information. Cybercriminals may also target an individual's private information, as well as corporate data for theft and resale. As many workers settle into remote work routines due to the pandemic, cybercrimes are expected to grow in frequency in 2021, making it especially important to protect backup data.
CYBER-SECURITY WITH CONFUSION MATRIX
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 for the detection and classification of 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 in order to improve the efficiency of the classifiers. 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 improve (accuracy for Normal class as well as DOS class is almost 100%) and comparable to false alarm rate and training, testing times.
Thanks for reading👍
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