Malware Detection Using Machine Learning

Prabhat Singh, Sakshi Kaur, Shivani Sharma, Gitika Sharma, Swati Vashisht, Vinay Kumar
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Abstract

Considering all the researches done, it appears that over last decade, malware has been growing exponentially and also has been causing significant financial losses to different organizations. Thus, it becomes important to detect if a file contains any malware or not. The malwares can cause a lot of damage to the system such as slowing down the system and also stealing sensitive information from the system. In the current times, one of the most important assets of the people is their data and information which needs to be protected. Hence, in order to protect the data and information, there is a need for software which could perform this task and help in ensuring the integrity of our system. Our method for malware detection uses different machine learning algorithms such as decision tree, random forest etc. The algorithm which has the maximum accuracy gets selected which provides a great detection ratio for the system. Furthermore, the performance of the system is detected by calculating the false positive and false negative rates using the confusion matrix.
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利用机器学习进行恶意软件检测
考虑到所做的所有研究,似乎在过去的十年中,恶意软件呈指数级增长,也给不同的组织造成了重大的经济损失。因此,检测文件是否包含任何恶意软件变得非常重要。这些恶意软件会对系统造成很大的损害,比如使系统变慢,还会从系统中窃取敏感信息。在当今时代,人们最重要的资产之一是他们的数据和信息,需要保护。因此,为了保护数据和信息,需要软件来执行这项任务,并帮助确保我们系统的完整性。我们的恶意软件检测方法使用了不同的机器学习算法,如决策树、随机森林等。选择精度最高的算法,为系统提供较大的检测率。此外,通过使用混淆矩阵计算假阳性和假阴性率来检测系统的性能。
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