A deep learning-based intrusion detection approach for mobile Ad-hoc network

The goal of the paper is to present a Stacked autoencoder approach for enhancing Intrusion Detection Systems (IDSs) in Mobile Ad-Hoc Networks (MANETs). The paper proposes a Stacked autoencoder-based approach for MANET (Stacked AE-IDS) to reduce correlation and model relevant features with high-level representation. This method reproduces the input with a reduced correlation, and the output of the autoencoder is used as the input of the Deep Neural Network (DNN) classifier (DNN-IDS). The proposed Deep Learning-based IDS focuses on Denial of Services (DoS) attacks within labeled datasets, which are available for intrusion detection, and employs the most potential attacks that impact routing services in Mobile Networks. The proposed Stacked AE-IDS method enhances the effectiveness of IDSs in detecting attacks in MANETs by reducing the correlation and modeling high-level representations of relevant features. The focus on DoS attacks and their impact on routing services in Mobile Networks makes the proposed approach particularly relevant for MANET security. The proposed Stacked AE-IDS approach has potential applications in enhancing the security of MANETs by improving the effectiveness of IDSs. This approach can be used to detect different types of attacks, particularly DoS attacks, and their impact on routing services in Mobile Networks.

Automated summary

The paper presents a Stacked autoencoder approach (Stacked AE-IDS) to enhance Intrusion Detection Systems (IDSs) in Mobile Ad-Hoc Networks (MANETs). The proposed approach reduces correlation and models relevant features with high-level representation to improve the effectiveness of IDSs in detecting attacks in MANETs. It focuses on Denial of Services (DoS) attacks within labeled datasets and their impact on routing services in Mobile Networks, making it particularly relevant for MANET security.