Confusion Matrix and Cyber crime

What is Confusion Matrix ?

A 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 compares the actual target values with those predicted by the machine learning model. This gives us a holistic view of how well our classification model is performing and what kinds of errors it is making.

FOUR OUTCOMES OF CONFUSION MATRIX

The confusion matrix visualizes the accuracy of a classifier by comparing the actual and predicted classes. The binary confusion matrix is composed of squares:

• TP: True Positive: Predicted values correctly predicted as actual positive
• FP: Predicted values incorrectly predicted an actual positive. i.e., Negative values predicted as positive. Also known as the Type 1 error
• FN: False Negative: Positive values predicted as negative. Also known as the Type 2 error
• TN: True Negative: Predicted values correctly predicted as an actual negative

Why Do We Need a Confusion Matrix?

Let’s say you want to predict how many people are infected with a contagious virus in times before they show the symptoms, and isolate them from the healthy population (ringing any bells, yet?

). The two values for our target variable would be: Sick and Not Sick.

Now, you must be wondering — why do we need a confusion matrix when we have our all-weather friend — Accuracy? Well, let’s see where accuracy falters.

Let’s see how our model performed:

The total outcome values are:

TP = 30, TN = 930, FP = 30, FN = 10

So, the accuracy for our model turns out to be:

96%! Not bad!

But it is giving the wrong idea about the result. Think about it.

Our model is saying “I can predict sick people 96% of the time”. However, it is doing the opposite. It is predicting the people who will not get sick with 96% accuracy while the sick are spreading the virus!

CYBER CRIME CASES

Though the word Crime carries its general meaning as “a legal wrong that can be followed by criminal proceedings which may result into punishment” whereas Cyber Crime may be “unlawful acts wherein the computer is either a tool or target or both”. The world 1st computer specific law was enacted in the year 1970 by the German State of Hesse in the form of ‘Data Protection Act, 1970’ with the advancement of cyber technology. With the emergence of technology the misuse of technology has also expanded to its optimum level and then there arises a need of strict statutory laws to regulate the criminal activities in the cyber world and to protect technological advancement system. It is under these circumstances Indian parliament passed its “INFORMATION TECHNOLOGY ACT, 2000” on 17th oct to have its exhaustive law to deal with the technology in the field of e-commerce, e-governance, e-banking as well as penalties and punishments in the field of cyber crimes.

In the present world, cybercrime offenses are happening at an alarming rate. As the use of the Internet is increasing many offenders, make use of this as a means of communication in order to commit a crime. The framework developed in our work is essential to the creation of a model that can support analytics regarding the identification, detection, and classification of integrated cybercrime offenses (structured and unstructured). The main focus of our work is to find the attacks that take advantage of the security vulnerabilities and analyze these attacks by making use of machine learning techniques.