Intrusion Detection in Cyber Space Using Machine Learning Based Algorithm

Abstract

Now a day, the fast growth of Internet access and the adoption of smart digital technology has resulted in new cybercrime strategies targeting regular people and businesses. The Web and social activities take precedence in most aspects of their lives, but also poses significant social risks. Static and dynamic analysis are inefficient in detecting unknown malware in standard threat detection approaches. Virus makers create new malware by modifying current malware using polymorphic and evasion tactics in order to fool. Furthermore, by utilizing selection of features techniques to identify more important features and minimizing amount of the data, these Machine Learning models' accuracy can be increased, resulting in fewer calculations. In the previous study traditional machine learning approaches were used to detect Malware. We employed Cuckoo sandbox, a malware detection and analysis system for detection and categorization, in this study we provide a Machine Learning based Intrusion analysis system to calculate exact and on spot Intrusion classification. We integrated feature extraction and component selection from the file, as well as selecting the much higher quality, resulting in exceptional accuracy and cheaper computing costs. For reliable identification and fine-grained categorization, we use a variety of machine learning algorithms. Our experimental results show that we achieved good, classified accuracy when compared to state-of-the-art approaches. We employed machine learning techniques such as K-Nearest Neighbor, Random Forest, Support Vector Machine, and Decision Tree. Using the Random Forest classifier on 108 features, we attained the greatest accuracy of 99.37 percent. We also discovered that Random Forest outscored all other classic machine learning techniques during the procedure. These findings can aid in the exact and accurate identification of Malware families.