Enhancing Network Security For Kr00k Attack Detection Using Binary Grasshopper Optimization (BGO)

The Internet of Things (IoT) is a technology that enables devices to communicate with one another and also with the cloud. Wi-Fi networks, which serve as the foundation of IoT connectivity, are susceptible to various security threats. One such recent attack is the kr00k attack, a security vulnerability within WPA2 encrypted Wi-Fi traffic that allows for decryption. Exploiting this vulnerability can result in an open Wi-Fi network, enabling intruders to eavesdrop on sensitive information. For detection of kr00k attack, an existing solution employs a CNN-LSTM model in which CNN is used for feature extraction and LSTM is used for classification of attacks. The limitations of the existing method are: it leads to overfitting of the model and some of the important features are missed during feature extraction. To address this limitation, feature selection is done instead of feature extraction. The relevant features are selected using BGO algorithm and the selected features are given to the LSTM network for classification of attacks. The AWID3 Dataset, encompassing a large volume of Wi-Fi network traffic is used in this work. The proposed work yielded an accuracy of 97%, precision of 0.97, recall of 0.95 and F-1 score of 0.93.