BLOG ON CYBER CRIME CASES WHERE THEY USE CONFUSION MATRIX

These days, when everyone is working for home, there’s a lot less opportunity to just casually ask your office colleagues if they’ve received a suspicious email like the one you just got. And attackers know it, driving a 300% increase in cybercrime since the start of the pandemic. It’s always good practice to check every link before you click on it, but of course, busy employees can get careless.

Confusion matrix is a fairly common term when it comes to machine learning. Today I would be trying to relate the importance of confusion matrix when considering the cyber crimes.

This all gives us an idea that there is something more to confusion matrix than just being another classification metric.

What Is Confusion Matrix ?

It is a performance measurement for machine learning classification problem where output can be two or more classes. It is a table with 4 different combinations of predicted and actual values.

  • For the 2 prediction classes of classifiers, the matrix is of 2*2 table, for 3 classes, it is 3*3 table, and so on.
  • The matrix is divided into two dimensions, that are predicted values and actual values along with the total number of predictions.
  • Predicted values are those values, which are predicted by the model, and actual values are the true values for the given observations.

The above table has the following cases:

  • True Negative: Model has given prediction No, and the real or actual value was also No.
  • True Positive: The model has predicted yes, and the actual value was also true.
  • False Negative: The model has predicted no, but the actual value was Yes, it is also called as Type-II error.
  • False Positive: The model has predicted Yes, but the actual value was No. It is also called a Type-I error.

High accuracy is always the Better be it machine learning we can say its good to have less error in our model and train our model to predict better out comes as their will be high accuracy it comes in better result.

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, etc.

we can use both type of error and can create better one for us .

We Can Create Phishing URL Detection With Python And ML

Cybercrime can be anything like:

  • Stealing of personal data
  • phishing
  • Leak Your private photos
  • Hack emails for gaining information.

This shows the critical nature that might vary from use case to use case where we want a tradeoff between the two types of error.

“Close to half of security analyst teams battle false positive rates of 50% or higher from their security tooling. Meantime, another report from the Ponemon Institute shows that as much as 25% of a security analyst’s time is spent chasing false positives — sifting through erroneous security alerts or false indicators of confidence — before being able to tackle real findings.

That means that every hour an analyst spends on the job, they’re wasting 15 minutes on false positives. On average, the typical organization wastes anywhere between 424 hours and 286 hours per week on false positives.

How can you combat false positives and negatives?

There are several approaches to consider when it comes to reducing the number of security threats, including network analysis, enacting policies that reduce the opportunity for cyber attacks, beefing up your overall security measures, and taking a look at how modern AI technology could help.

Analyze Network Traffic
Look through information in your network logs to spot unfamiliar usernames, odd connection details, and suspicious trends in the duration and frequency of communication to uncover security threats the old-fashioned way. You may be able to detect more false negatives than if you hadn’t looked, but this process is prone to suffer from human error and can become quite time-consuming.

Limit Network Access on IoT Devices
As a matter of protocol, consider implementing a policy that limits the network access of IoT devices. These devices have become common targets for cybercriminals looking for a way in, and they typically don’t require much access to function properly. When IoT devices are given restricted network access, your security software is more likely to recognize unusual behavior and should issue more accurate alerts.

Use Web Application Firewalls
A large percentage of data breaches are targeted at web applications vulnerabilities. While the commonly-deployed Web Application Firewall can reduce these instances, this type of firewall can hog network resources when used to detect false negatives and positives. The related slowdown can reduce the firewall’s effectiveness in quickly alerting staff to authentic threats or slow network traffic to an unacceptable level.

No Security Program is Perfect

While it would be impossible to fend off every false positive and negative security threat, modern solutions utilizing modern tech can greatly reduce the number of threats that need further investigation.

Thank you…

learing always make man perfect