Analyze textual data: deep neural network for adversarial inversion attack in wireless networks

Abstract

Deep neural networks (DNN) are highly effective in a number of tasks related to machine learning across different domains. It is quite challenging to apply the information gained to textual data because of its graph representation structure. This article applies innovative graph structures and protection techniques to secure wireless systems and mobile computing applications. We develop an Intrusion Detection System (IDS) with DNN and Support Vector Machine (SVM) to identify adversarial inversion attacks in the network system. It employs both normal and abnormal adversaries. It constantly generates signatures, creates attack signatures, and refreshes the IDS signature repository. In conclusion, the assessment indicators, including latency rates and throughput, are used to evaluate the effectiveness and efficiency of the recommended framework with Random Forest. The results of the proposed model (SVM with DNN) based on adversarial inversion attacks were better and more efficient than traditional models, with a detection rate of 93.67% and 95.34% concerning latency rate and throughput. This article also compares the proposed model (SVM with DNN) accuracy with other classifiers and the accuracy comparison for feature datasets of 90.3% and 90%, respectively.