A deep learning methods for intrusion detection systems based machine learning in MANET

Abstract

Deep learning is a subset of machine learning where algorithms are created and function similar to those in machine learning, but there are numerous layers of these algorithms each providing a different interpretation to the data it feeds on. Mobile Ad-Hoc Network (MANET) is picking up huge popularity due to their potential of providing low-cost solutions to real-world communication problems. MANETs are more susceptible to the security attacks because of the properties such as node mobility, lack of centralized management and limited bandwidth. To tackle these security issues, traditional cryptography schemes can-not completely safeguard MANETs in terms of novel threats and vulnerabilities, thus by applying deep learning methods in IDS are capable of adapting the dynamic environments of MANETs and enables the system to make decisions on intrusion while continuing to learn about their mobile environment. IDS represent the second line of defense against malevolent behavior to MANETs since they monitor network activities in order to detect any malicious attempt performed by Intruders. Recently, more and more researchers applied deep neural networks (DNNs) to solve intrusion detection problems. Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), the two main types of DNN architectures, are widely explored to enhance the performance of intrusion detection system. In this paper, we present the most well-known deep learning models CNN, Inception-CNN, Bi-LSTM and GRU and we made a systematic comparison of CNN and RNN on the deep learning-based intrusion detection systems, aiming to give basic guidance for DNN selection in MANET