An analysis on Keylogger Attack and Detection based on Machine Learning

Abstract

A keylogger attack is a type of cyberattack that involves the use of a software program to record keystrokes on a target device. Attacks of this kind can be used to steal sensitive data, including credit card numbers and login credentials. Keylogger attacks are often targeted at specific individuals or organizations, and the attackers may have prior knowledge of the target's systems and configuration. There are a variety of ways to carry out a keylogger attack, and the attacker's choice of method will depend on the type of information they are trying to steal. For example, an attacker may install a hardware keylogger on the target's computer in order to record every keystroke made. As an alternative, the attacker might develop malicious software that captures keystrokes and transmits them to a distant server. Keylogger attacks are difficult to detect, as the keylogger software can be disguised as a legitimate program or run in the background without the user's knowledge. However, there are some signs that a keylogger attack may be taking place, such as unexpected activity on the computer or unusual network traffic. The best way to protect against keylogger attacks is to use a reputable antivirus program and to keep all software up to date. Furthermore, users need to be cautious while opening attachments or clicking on hyperlinks that come from unknown sources. This paper focuses on the evolution of technology over time, as well as the implementation and observation of keylogger attacks.