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A modified framework to detect keyloggers using machine learning algorithm

机译:一种使用机器学习算法检测键盘记录程序的改进框架

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摘要

Keyloggers are very dangerous programs that does the monitoring of all the activities carried in our PC. Some of the activities include capturing screenshots of all the activities performed in PC screen, recording the activity performed by browser and generating the different keystrokes of the various activities performed in our PC. These activities are difficult to be traced by any detectable softwares. Hence there is an urgent need to detect the presence of keyloggers in our system and nullify all the existing keyloggers present in PC. They do all the spying and steal all the sensitive, confidential and important information. This information could be used for harmful purposes and endanger the life of the person associated with it. This is really a grave threat to the society. We have tried to find a solution to this grave problem. We proposed a new detection technique that will help in detecting all the keyloggers present in our PC using Machine learning algorithm. The different keyloggers which are available or being installed are detected using Support Vector Machine learning algorithm. After various analysis the result has been generated and it is counter verified with some of the already available anti-keylogger tools.
机译:键盘记录器是非常危险的程序,它监视着我们PC中进行的所有活动。其中一些活动包括捕获PC屏幕上执行的所有活动的屏幕快照,记录浏览器执行的活动以及生成在PC中执行的各种活动的不同按键。这些活动很难被任何可检测的软件跟踪。因此,迫切需要检测我们系统中键盘记录程序的存在,并使PC中存在的所有现有键盘记录程序无效。他们进行所有间谍活动并窃取所有敏感,机密和重要信息。此信息可能用于有害目的,并危及与之关联的人的生命。这确实是对社会的严重威胁。我们试图找到解决这个严重问题的方法。我们提出了一种新的检测技术,该技术将使用机器学习算法来检测PC中存在的所有按键记录程序。使用支持向量机学习算法检测可用或正在安装的不同按键记录程序。经过各种分析后,将生成结果,并使用一些可用的反键盘记录器工具对其进行反验证。

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