An Improvised Machine Learning Model KNN for Malware Detection and Classification

The problem of network security has arisen as a key source of worry in today’s linked society. Sabotage and information extortion are among the most significant risks toan organization’s security. They include a broad spectrum of dangers such as huge intellectual property theft and software attacks, as well as sabotage and information extortion. Inappropriate usage of protocols on a network may potentially represent a security risk to a system or network. In network security, the use of data mining tools to gather, alter, and analyze enormous amounts of data is vital. With the help of a range of data mining technologies that are now accessible, it is possible to conduct analysis and forecasting of data and threats across computer networks. This study makes an effort to forecast network security dangers by using a variety of categorization methodologies. The classification techniques used in this study include the Naive Bayes Classifier, the Decision Tree Classifier and the K Nearest Neighbors Classifier. It assesses the efficacy of the categorization approaches described above in order to discover potential risks and vulnerabilities. In order to analyze a dataset and extract information, Simple Machine Learning techniques are utilized, such as in Distributed Denial of Service (DDoS) attacks (R2L), U2R attacks, and Probe Attacks (U2R). The Naive Bayes Classifier, the Decision Tree Classifier, and the K Nearest neighbours method are all examples of classification algorithms discussed in this section. For the purpose of comparing the performance of the proposed machine learning algorithm technique with other approaches, the entropy calculation and accuracy, recall, F1Score, and entropy may be employed.