Cyber Crime and Confusion Matrix
Globally, cybercrime damages are expected to reach US $6 trillion by 2021 and reaching $10.5 trillion USD annually by 2025
:: Cybersecurity Ventures Report
What is Cyber Crime ?
Cybercrime is criminal activity that either targets or uses a computer, a computer network or a networked device.
Most, but not all, cybercrime is committed by cybercriminals or hackers who want to make money. Cybercrime is carried out by individuals or organizations.
Some cybercriminals are organized, use advanced techniques and are highly technically skilled. Others are novice hackers.
Rarely, cybercrime aims to damage computers for reasons other than profit. These could be political or personal.
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.
However, researchers have recently been solving these problems by developing security models and making predictions through artificial intelligence 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
Accuracy (Acc) score is a method used to evaluate the performance of the model made by comparing the predictions made after running the algorithm with the test data. A value between 0 and 1 is produced according to the ratio of the entire predicted value for a prediction to match with the real values. To determine the accuracy of the forecast we use confusion matrix
Confusion Matrix :
Confusion Matrix is a useful machine learning method which allows you to measure Recall, Precision, Accuracy, and AUC-ROC curve. Below given is an example to know the terms True Positive(TP), True Negative(TN), False Negative(FN), and True Negative(TN)
- TP = Prediction is positive(normal) and actual is positive(normal).
- FP = Prediction is positive(normal) and actual is negative(abnormal).
- FN = Prediction is negative(abnormal) and actual is positive(normal).
- TN = Prediction is negative(abnormal) and actual is negative(abnormal)
You projected positive and its turn out to be true. For example, you had predicted that France would win the world cup, and it won.
When you predicted negative, and it’s true. You had predicted that England would not win and it lost.
Your prediction is positive, and it is false.
You had predicted that England would win, but it lost.
Your prediction is negative, and result it is also false.
You had predicted that France would not win, but it won.
You should remember that we describe predicted values as either True or False or Positive and Negative.
For future works; crime, criminal, victim profiling and cyber-attacks can be predicted using deep learning algorithms and the results can be compared. Based on the talks with other authorized units having crime databases, cyber-crime data of other provinces may also be obtained to use for comparison with this study. The data of other provinces can be compared to similar studies. Intelligent criminal-victim detection systems that can be useful to law enforcement agencies in the fight against crime and criminals can be created to reduce crime rates.