Fuzzy inference based feature selection and optimized deep learning for Advanced Persistent Threat attack detection

Abstract

SummaryOne of the attacks that have rapidly happen is Advanced Persistent Threat (APT). APT attacks contain different sophisticated approaches and methods of attacking targets for stealing confidential as well as sensitive information. This research introduced novel and effective APT attack detection techniques, namely Smart Flower Cosine Algorithm‐driven Deep Convolutional Neural Network (SFCA‐DeepCNN). Here, the APT attack detection is done by the DeepCNN, wherein the weight of DeepCNN is updated by the proposed SFCA. The SFCA is modeled by unifying the Smart Flower Optimization Algorithm (SFOA) and Sine Cosine Algorithm (SCA). Additionally, the pre‐processing process is done by Quantile normalization, and the features are chosen based on the fuzzy‐based distance measures. Moreover, data augmentation is done to increase the size of data by performing the oversampling that avoids the overfitting problems. Furthermore, the proposed optimized deep learning scheme detects the accurate APT detection outcome. The performance improvement of the proposed method for testing accuracy is 9.417%, 10.47%, 4.232%, and 3.068% higher than the existing methods, such as, Deep Learning, Support Vector Machine (SVM), Bidirectional Long Short‐Term Memory (Bi‐LSTM), and Hidden Markov Model (HMM).