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Malware detection using opcodes statistical features

机译:使用操作码统计功能进行恶意软件检测

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In recent years, malicious software has affected and overshadowed personal computer and computer network securities. For this reason, searching for innovative solutions to detect malware has become increasingly important. In this paper, we develop a malware detection method using similarity measurement algorithms. The purpose of the proposed method is to improve the malware detection rate and detection speed. This method, compared to other static detection techniques, has many advantages, such as a much higher speed due to the direct use of opcodes and better detection results due to being uninfluenced by obfuscation and disassembly techniques. We also evaluate these malware detection algorithms by using the most up-to-date antivirus software. Experimental results show that the proposed method has a detection rate of 90%, while the most up-to-date antivirus software has an average detection rate of about 40%. It is found that the proposed method increases the speed of detection program by 30% compared to the existing techniques.
机译:近年来,恶意软件已经影响并掩盖了个人计算机和计算机网络的安全性。因此,寻找创新的解决方案来检测恶意软件变得越来越重要。在本文中,我们开发了一种使用相似性度量算法的恶意软件检测方法。提出的方法的目的是提高恶意软件的检测率和检测速度。与其他静态检测技术相比,此方法具有许多优点,例如,由于直接使用操作码而导致的速度要高得多,并且由于不受混淆和反汇编技术的影响,因此具有更好的检测结果。我们还将通过使用最新的防病毒软件来评估这些恶意软件检测算法。实验结果表明,该方法具有90%的检测率,而最新的防病毒软件的平均检测率约为40%。发现与现有技术相比,所提出的方法将检测程序的速度提高了30%。

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