Cyber Crime Security Based on Confusion Matrix:-

By the Use of confusion matrix detect about the threat. Any threat is Coming or not. And we can say if anyone wants to attack my network or anything they notify me that someone attack your system. In this Process the confusion matrix gives us the Probability or Accuracy that how much sure about the attack. We use the great function that is confusion matrix.

We research about that how they notify or how it works. So this blog is totally based on how confusion matrix works on Cyber crime security. let’s Continue…..

In recent years, botnets have become one of the major threats to information security because they have been constantly evolving in both size and sophistication. A number of botnet detection measures, such as honeynet-based and Intrusion Detection System (IDS)-based, have been proposed. However, IDS-based solutions that use signatures seem to be ineffective because recent botnets are equipped with sophisticated code update and evasion techniques. A number of studies have shown that abnormal botnet detection methods are more effective than signature-based methods because anomaly-based botnet detection methods do not require pre-built botnet signatures and hence they have the capability to detect new or unknown botnets. In this direction, this paper proposes a botnet detection model based on machine learning using Domain Name Service query data and evaluates its effectiveness using popular machine learning techniques. Experimental results show that machine learning algorithms can be used effectively in botnet detection and the random forest algorithm produces the best overall detection accuracy of over 90%.

we evaluate each of the three constructed models regarding different aspects.

Confusion Matrix Algorithm is our first model that was used to extract frequent crime patterns.

we choose it as the ideal model

for crime prediction in their study. Reports Of the confusion matrix of applying this model on

Denver testing set. A report of the main classification metrics obtained by this model.

Here two types of error showing one is Type1 Error and second one is Type2 Error. Type1 error is too dangerous or too important for alarming and Type2 error is not much important because of they shows false positive (FP) but Type1 error shows True positive (TP). By this idea we conclude about the threat.

This is the brief about how Confusion Matrix uses in cyber Crime Security. Hope you like it

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