Use of confusion matrix in detecting cyber crime

Summer 2021 Task 05 ???????

Particularly in the last decade, Internet usage has been growing rapidly. However, as the Internet becomes a part of the day to day activities, cybercrime is also on the rise.

Here is a way to predict cyber crimes using machine learning techniques ->

At present, there is no generalized framework is available to categorize cybercrime offenses by feature extraction of the cases. In the present work, data analysis and machine learning are incorporated to build a cybercrime detection and analytics system

For feature extraction the TFIDF vector process is used This developed methodology is based on 4 phases that are applied to the data, which are reconnaissance, preprocessing, data clustering and classification and prediction analysis.

In this phase only the feature extraction process takes place. It converts the high dimensional data to low dimensional data. This preprocessed data are helpful for data visualization because a composite data can organize well when that complex data are converted as a less number of dimensions

Here, na?ve Bayes is used for classification and k-means are used for clustering .The cybercrime offenses are clustered based on the TFIDF weighted vectors obtained from the features. The data has considered by using a 70:30 thumb rule. Where 70% of data were utilized for training and 30% of the data

In the prediction analysis step, the cybercrime data were analyzed and used to predict which crime is occurring more in a particular year at a particular location

Precision: It is the measure of truly predicted positive samples to the total number of positively predicted samples. If the precision score is more then it represents that our model is pretty good to classify the samples. 

Recall: It is the measure of truly predicted positive samples of all the samples present in the actual class as yes. It is also termed as the sensitivity of the model

CONFUSION MATRIX

depicts the confusion matrix for our model when the training size was 0.8 and the test size was 0.2. By this, we know how many cases are classified correctly and how many are classified incorrectly. It means we can find out the true negatives and true positives and false negatives and false positives classified by using the model.