Cyber crime cases and confusion matrix

What is a confusion matrix?

a confusion matrix is a metric that is often used to measure the performance of a classification algorithm. let us consider a spam classifier, our classifier needs to determine whether the mail is spam or not. The predicted classes are represented in the columns of the matrix, whereas the actual classes are in the rows of the matrix. We then have four cases. True positives (TP): the cases for which the classifier predicted ‘spam’ and the emails were actually spam.

True negatives (TN): the cases for which the classifier predicted ‘not spam’ and the emails were actually real.

False positives (FP): the cases for which the classifier predicted ‘spam’ but the emails were actually real.

False negatives (FN): the cases for which the classifier predicted ‘not spam’ but the emails were actually spam.

CYBER CRIME

According to Wikipedia, Cybercrime, or computer crime, is a crime that involves a computer and a network. The computer may have been used in the commission of a crime, or it may be the target. Cybercrime may harm someone's security and financial health.

CYBER CRIMES AND CONFUSION MATRIX

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. Cybercrime will cost nearly $6 trillion per annum by 2021 as per the cybersecurity ventures report in 2020. For illegal activities, cybercriminals utilize any network computing devices as a primary means of communication with a victims' devices, so attackers get profit in terms of finance, publicity and others by exploiting the vulnerabilities over the system. Cybercrimes are steadily increasing daily. Evaluating cybercrime attacks and providing protective measures by manual methods using existing technical approaches and also investigations has often failed to control cybercrime attacks. Existing literature in the area of cybercrime offenses suffers from a lack of a computation methods to predict cybercrime, especially on unstructured data. Therefore, this study proposes a flexible computational tool using machine learning techniques to analyze cybercrimes rate at a state wise in a country that helps to classify cybercrimes. Security analytics with the association of data analytic approaches help us for analyzing and classifying offenses from India-based integrated data that may be either structured or unstructured. The main strength of this work is testing analysis reports, which classify the offenses accurately with 99 percent accuracy.

Examples, different types of cyber crime:

• Email and internet fraud.
• Identity fraud.
• Theft of financial or card payment data.
• Theft and sale of corporate data.
• Cyberextortion (demanding money to prevent a threatened attack).
• Ransomware attacks (a type of cyberextortion).
• Cryptojacking (where hackers mine cryptocurrency using resources they do not own).
• Cyberespionage (where hackers access government or company data).