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Wireless networks are the foundation of modern communication. This also leads to network data hijacking and network attacks. Despite the fact that different databases and methods exist to combat this, the network intrusion problem is still on the rise. To address this, the proposed research work introduces a faster and better Intrusion Detection System trained on the AWID data-set with faster learning and better accuracy. A unique supervised studying device has been evolved to categorise the community wi-fi as malicious or benign. To discover the wi-fi version by considering the detection wi-fi rate, a combination of supervised gaining knowledge of a set of rules and feature choice technique are used. Through this it is observed that, Artificial Neural Network (ANN) is primarily based on the machine studying with wrapper characteristic selection that outperform support vector machine (SVM) technique even as classifying community traffic. To assess the performance, NSL-KDD dataset with feature wrapping is used to categorise community traffic data with the use of SVM and ANN supervised machine gaining knowledge technique. Comparative studies suggests that, the proposed model is faster and more efficient than different existing fashions to recognize the intrusion detection rate. This paper aims to categorize the threats to Institute of electrical and Electronics Engineers (IEEE) 802.11n well known protocol and classify the contemporary answers and countermeasures to them. In addition, this paper proposes a more accurate, and faster widespread intrusion detection system as a proposed solution for IEEE 802.11 threats.