What is a Cyber-crime ?
Cyber-crime is one type of crime, committed on the internet using Computer or mobile device, etc.
Cyber-crime is defined as a crime that is committed using a network-connected device such as a computer or a mobile phone. Those who commit cyber-crime are known as cyber criminals or cyber crooks.
Types of cyber-crime :
4: identity theft
5: spamming to name, etc…
Role of Confusion Matrix :
A confusion matrix is a performance measurement technique for Machine learning classification problems. It’s a simple table which helps us to know the performance of the classification model on test data for the true values.
It’s a concept that is used to find the accuracy of the model that we create in Machine learning.
Microsoft uses its own cyber-security platform, Windows Defender Advanced Threat Protection (ATP), for preventative protection, breach detection, automated investigation and response. Windows Defender ATP IS built into Windows 10 devices, automatically updates and employs cloud AI and multiple levels of machine learning algorithms to spot threats.
Lets come to Confusion matrix :
- True Positive [ TP ]: In TP, the Machine Learning model predicted right and it was actually right.
- True Negative [ TN ]: In TN, the Machine Learning model predicted right but actually it was the wrong prediction, also called False alarm.
- False Positive [ FP ]: In FP, the model predicts the wrong but actually it was right
- False Negative [FN ]: In FN, the model predicted wrong and actually it as wrong.
Example: when a model predicts a positive reaction like cyber theft in server with 70% data accuracy out of 100% but actual data shows 50% then model tell the right prediction True positive and 20% model predicts the wrong prediction means False positive.
this type of error also known as Type-1 Error.
and it’s also most dangerous error as the machine predicted false but it was not false it was true.
Example: when a model predicts a negative reaction like cyber theft in server 60% data negative out of 100% but actual data shows 55% then model tell the right prediction means True Negative but 5% predictions wrong by model according to the actual data False Negative.
this type of error also known as Type-2 Error.
In both the scenario, model gives some error this shows the critical nature that might vary from case to case. it impossible to achieve the 100% accuracy which also an ideal accuracy, but we can try to go as close as to ideal accuracy by providing required data.
A confusion matrix is a tabular summary of the number of correct and incorrect predictions made by a classifier. It is used to measure the performance of a classification model. It can be used to evaluate the performance of a classification model through the calculation of performance metrics like accuracy, precision, recall.
Need for Confusion Matrix in Machine learning:
It evaluates the performance of the classification models, when they make predictions on test data, and tells how good our classification model is.
It not only tells the error made by the classifiers but also the type of errors such as it is either type-I or type-II error.
With the help of the confusion matrix, we can calculate the different parameters for the model, such as accuracy, precision, recall, F1-score.
Accuracy is used when the [TP] & [TN] are more important. Accuracy is a better metric for Balanced Data.
Whenever [FP] is much more important use Precision.
Whenever [FN] is much more important use Recall.
F1-Score is used when the [FN] & [FP] are important, it is also a better metric for Imbalanced Data.
From the overall concept we can say that prediction might vary from case to case, to achieve best result provide the required data to model, so we can train the model.
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So this would give an idea of what the four boxes in the confusion matrix are representing.