Detection of Cyber Crime Based on Facial Pattern Enhancement Using Machine Learning and Image Processing Techniques

Introduction

Using computers or other communication technologies to terrorise or harm others or damage, hurt, or destroy property is what we mean when we say “cybercrime.” Cybercrimes that are computer-assisted as well as computer-focused can be divided into two groups. Cyber stalking and child pornography are instances of computer-assisted crimes; phishing and hacking are examples of computer-focused offences. For a variety of factors, such as the culture in which the crime was committed, its severity, and occurrences that were not reported because of ignorance or social restraints, it is difficult to get accurate and official statistics on cybercrime. The involvement of law enforcement in circumstances like this is critical because it regulates the level of detail given. In the 1960s, the first cybercrime involving the duplication of computer programming occurred (Al-Khater, 2020). There were numerous fraud and forgery charges brought against Union Dime Savings Bank in New York in 1970 after a bank clerk embezzled $1.5 million from client accounts. Imperial Chemical Industries (ICI) employees stole hundreds of computers and their backups in the early 1970s and sought 275,000 pounds sterling in ransom. A computer worm was created in 1988 by Robert T. Morris at MIT in Cambridge, Massachusetts (MIT).

A phishing attempt was made for the first time in 1995The Electronic Disturbance Theatre was established in 1997 with the mission of creating electronic counterparts to the site-in tools protesters employ. The president of Mexico's website was attacked with a denial-of-service attack in 1998 using a tool called FloodNet (Sivakumar, 2021).

This was done in January 1998, when a coal-fired power station's emergency mode was activated and the SCADA system software was uninstalled. In 2005, a hacker attack on the bank's air conditioning systems resulted in the bank's computer systems being shut down because of the rising temperature in its computer room. The Russian Business Network (RB) was founded in 2006 and is based in New York City (Kester et al., 2021). This unlawful company has committed numerous cybercrimes and provided a wide range of Trojans, spam, and phishing-related tools and services. Its main business is the resale of stolen personal information. A webpage on how to construct bombs was replaced with a page on how to make cupcakes by British security agencies in 2011.

Several sorts of studies were examined in the review of the literature for this paper in order to establish approaches for the identification and prevention of cybercrime. It is through the use of statistical methods that we can better understand the nature of cybercrime and develop effective methods for identifying it. Approaches that focus on predicting outputs from data input include machine learning techniques. Cybercrime detection technologies now in use have been subjected to several reviews and surveys (Rawat et al.,2021). Existing review studies, on the other hand, only concentrated on analysing the detection methods that are limited to one or a few cybercrimes, such as cyber bullying or botnets.

A machine learning technique allows computers to learn and even improve themselves without being explicitly programmed, according to Arthur Samuel. It is possible for software systems to improve their accuracy at predicting outcomes without explicitly programming them through the use of machine learning (ML). When constructing an algorithm for machine learning, the primary concept is to employ statistical analysis to anticipate an output while also changing results in response to new data being available. The research of making computers capable of self-learning is known as machine learning. In my opinion, ML is one of the most intriguing technologies I've ever encountered (Nicholls et al., 2021). To put it plainly, the term reveals that it offers the computer the power to learn. It's possible that machine learning is now being applied in more places than you think.