WHAT IS CONFUSION MATRIX ?
First of all, confusion matrix is an N x N matrix used for evaluating the performance of a classification model , where N is the number of target classes. The matrix basically compares the actual target values with those predicted by the machine learning model created by us. Basically , this gives a holistic view of how well our classification model is performing and what kinds of error it is making in a model…..
TWO TYPES OF ERRORS OF CONFUSION MATRIX ARE:
- Type1 Error
- Type2 Error
TYPE 1 ERROR:
Type 1 error, also known as a “false positive” : the error of rejecting a null hypothesis when it is actually true. In other words this is the error of accepting an alternative hypothesis(the real hypothesis of interest) when the results can be attributed to chance. Normally, it occurs when we are observing a difference when in truth there is none or more specifically.so the probability of making a type I error in a test with rejection region R is 0 PR H( is true).
TYPE2 ERROR
Type II error, also known as a “false negative”: the error of not rejecting a null hypothesis when the alternative hypothesis is the true state of nature. In other words ,this is the error of failing to accept an alternative hypothesis when you don’t have adequate power. plainly speaking, it occurs when we are failing to observe a difference when in truth there is one. so the probability of making a type II error in a test with rejection region R is 1(| is true). The power of test can be True.
CYBERCRIMES
Cybercrime, also called computer crime, the use of computer as an instrument to further illegal ends ,such as committing fraud , trafficking in child pornography and intellectual property, stealing identities and privacy. cybercrime specially through the internet , has grown in importance as the computer has become central to commerce , entertainment and government.
Cybercrime is vastly growing in the world of tech today. criminals of the world wide web exploit internet users’ personal information for their own profit. they dive deep into the dark web to buy and sell illegal products and services. They even gain access to classified government information.
Cybercrime basically will more than triple the number of unfilled cybersecurity jobs by 2021.
CYBER ATTACK DETECTION AND CLASSIFICATION USING PARALLEL SUPPORT VECTOR MACHINE:
A number of cyber-attack detection and classification methods have been introduced with different levels of success that are used as a counter measure to preserve data integrity and system availibilty from attacks. we proposed a parallel support vector machine(PSVM) algorithm for the detection and classification of cyber attack datasets basically , cyber attacks detection are a classifications problems in which we classify the normal pattern from the abnormal.
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 .
The proposed parallel support vector machine algorithm is evaluated using KDD1999 intrusion detection datasets. the first drawbacks is that SVM is very sensitive to attacks. The second, SVM designed for the two class- problems it has to be extended for the multiclass problem by choosing a suitable kernel function. decision tree based support vector machine which combines support vector machines and decision tree can be an effective way for solving multiclass problems.
Improved support vector machine(ISVM) algorithm for classification of cyber attacks datasets which gives 100% detection accuracy for normal and denial of service classes and comparable to false alarm rate , training and testing times.
DR(Detection rate) is computed as the ratio between the number of correctly detected attacks and the total number of attacks, while the false alarm( false positive) rate is computed as the ratio between the number of normal connections that is incorrectly misclassified as attacks and the total number of normal connections.
CONCLUSION
This research presents new cyber attacks detection and classification system to classify cyber attacks. in this , we developed the performance of IDS using a parallel support vector machine for distributed cyber attack detention and classifications. the new PSWM is shown more efficient for the detection and classification of different types of cyber attacks compared to SDF. the experimental results on the KDD99 benchmark dataset manifest that the proposed algorithm achieved a high detection rate on different types of network attacks.
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