A Hybrid feature extraction method with machine learning for detecting the presence of network attacks

Network traffic is vulnerable to attacks by hackers and threat actors. There are varieties of attacks which can be both passive and aggressive which used to threaten network security and privacy. For identifying such attacks fast and precisely, a strong Intrusion Detection System is required. It detects attacks by widely inspecting each packet in the network in real-time. Hence network traffic analysis is important for enhancing network security which requires the use of machine learning techniques for diagnosis at machine speed. To seize complicated patterns in data, machine learning needs a large amount of data. Thus this article focuses on the NSL-KDD datasets, which eliminate certain redundant and more frequent records from the 1999 KDD Cup dataset that can still be used in machine learning techniques that are the primary tools for network traffic analysis and anomaly detection. Initially, features to be extracted from network traffic are pre-processed, which often involves the use of numerous mathematical techniques like removing unnecessary or undesired features to assemble the data for a machine learning model. Then the selected features are used for training the proposed models and a binary classification technique is used for prediction of normal or attack type. Eventually, the overall performance accuracy and error rate of our model is evaluated. Thus network traffic analysis will be used to expose invasions and forbid network attacks.