Description

Internet of Things (IoT) is promising technology that brings tremendous benefits if used optimally. At the same time, it has resulted in an increase in cyber security risks due to the lack of security for IoT devices. Enhancing Network Intrusion Detection Systems (NIDS) with supervised Machine Learning (ML) is tough. MLNIDS must be trained and evaluated, operations requiring data where benign and malicious samples are clearly labelled. We demonstrate the design, implementation, and evaluation of Citrus: a network intrusion detection framework which is adept at tackling emerging threats through the collection and labelling of live attack data by utilizing diverse Internet vantage points in order to detect and classify malicious and benign attacks. Finally, we can detect the cyber-attacks such as intrusion attacks or not (normal) in the web application (FLASK) user interface. We are implementing machine learning algorithm (SVM) our proposed random forest algorithm give high accuracy and prediction.