A technical review and comparative analysis of machine learning techniques for intrusion detection systems in MANET

Abstract

Machine learning techniques are being widely used to develop an intrusion detection system (IDS) for detecting and classifying cyber attacks at the network-level and the host-level in a timely and automatic manner. However, Traditional Intrusion Detection Systems (IDS), based on traditional machine learning methods, lacks reliability and accuracy. Instead of the traditional machine learning used in previous researches, we think deep learning has the potential to perform better in extracting features of massive data considering the massive cyber traffic in real life. Generally Mobile Ad Hoc Networks have given the low physical security for mobile devices, 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 techniques 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. An IDS in MANET is a sensoring mechanism that monitors nodes and network activities in order to detect malicious actions and malicious attempt performed by Intruders. Recently, multiple deep learning approaches have been proposed to enhance the performance of intrusion detection system. In this paper, we made a systematic comparison of three models, Inceprtion architecture convolutional neural network Inception-CNN, Bidirectional long short-term memory (BLSTM) and deep belief network (DBN) on the deep learning-based intrusion detection systems, using the NSL-KDD dataset containing information about intrusion and regular network connections, the goal is to provide basic guidance on the choice of deep learning methods in MANET.

An improved performance routing protocol based on delay for MANETs in smart cities

“…Our algorithm defines and uses the concept of the average delay of a path (𝐷𝑒𝑙𝑎𝑦 𝑖 ). The average delay of a path is the summary delay of all links on that path, determined as in (2):…”

“…Moreover, mobile devices are equipped with the Machine to machine (M2M) modules, which can communicate directly between devices without based on pre-installed infrastructure (the principle of mobile ad-hoc networks (MANETs) formation) [1]. In reality, MANETs was shaped in the 1980s as an organization of mobile radio devices which is self-established and self-configuring parameters to communicate in-network more conveniently [2]. In recent years, MANETs have series of applications for humanity in many areas such as smart agriculture, smart retail, intelligent transportation system, green energy and IoT ecosystems, indicated in [3]- [16].…”

Machine Learning Methods for Intrusive Detection of Wormhole Attack in Mobile Ad-Hoc Network (MANET)

“…Based on intelligence, these various activities are structured to construct a paradigm that generally classifies and discriminates between "normal" and "malicious" samples, such as attacks and standard packets. With the exponential growth in the use of Deep Learning Models [41], the sophistication of attack strategy tools is enhanced.…”

An approximation of balanced score in neutrosophic graphs with weak edge weights

“…The MST is principally used to solve a problem of a lowest cost(weight) to cover all nodes in an undirected graph. Certain applications include routing protocol evaluation in computer/communication networks [14], cluster analysis [15] and civil network strategy planning [16]. In weighted graphs, the MST reflects a spanning tree graph which develops minimum cost/weight than other trees on the equivalent graph.…”