HARSHITA KUMARI

Confusion matrix

What is confusion Matrix ?

A confusion matrix is a table that is often used to describe the performance of a classification model (or “classifier”) on a set of test data for which the true values are known. The confusion matrix itself is relatively simple to understand, but the related terminology can be confusing.

There are two possible predicted classes: “yes” and “no”. If we were predicting the presence of a disease, for example, “yes” would mean they have the disease, and “no” would mean they don’t have the disease.

Let’s now define the most basic terms, which are whole numbers (not rates):

• true positives (TP): These are cases in which we predicted yes (they have the disease), and they do have the disease.
• true negatives (TN): We predicted no, and they don’t have the disease.
• false positives (FP): We predicted yes, but they don’t actually have the disease. (Also known as a “Type I error.”)
• false negatives (FN): We predicted no, but they actually do have the disease. (Also known as a “Type II error.”)

Cyber Crime investigation using confusion matrix:

One of the most important topic in Machine Learning is “CONFUSION MATRIX”

Cyber-attacks have become one of the biggest problems of the world. They cause serious financial damages to countries and people every day. The increase in cyber-attacks also brings along cyber-crime.

The key factors in the fight against crime and criminals are identifying the perpetrators of cyber-crime and understanding the methods of attack. Detecting and avoiding cyber-attacks are difficult tasks.

There are three main objectives in our study. The first is to use actual cyber-crime data as input to predict a cyber-crime method and compare the accuracy results. The second is to measure whether cyber-crime perpetrators can be predicted based on the available data. The third objective is to understand the effect of victim profiles on cyber-attacks.

The number of crimes, damages, attacks and methods of attack in the dataset.

However, researchers have recently been solving these problems by developing security models and making predictions through artificial intelligence and Machine learning methods. A high number of methods of crime prediction are available in the literature. On the other hand, they suffer from a deficiency in predicting cyber-crime and cyber-attack methods.

This problem can be tackled by identifying an attack and the perpetrator of such attack, using actual data. The data include the type of crime, gender of perpetrator, damage and methods of attack. The data can be acquired from the applications of the persons who were exposed to cyber-attacks to the forensic units.

Here, we analyze cyber-crimes in two different models with machine-learning methods and predict the effect of the defined features on the detection of the cyber-attack method and the perpetrator. We used some machine-learning methods in our approach and concluded that their accuracy ratios were close.

The Logistic Regression was the leading method in detecting attackers with an accuracy rate of 65.42%.

In other method, we predicted whether the perpetrators could be identified by comparing their characteristics.

Where 0 is “Perpetrator Known”, 1 is “Perpetrator Unknown”.

Thanks for reading this Article……☺☺

By Harshita Kumari….!