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

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

Producer mobility support scheme for indirection-based mobility approach in named data networking

Named Data Networking (NDN) is a clean-slate future Internet architecture proposed to support content mobility by using hierarchical naming instead of IP addresses for routing. The hierarchical naming structure of NDN offers more benefits in supporting consumer mobility. However, the movements of producer inflict changes in routing name prefix hierarchy, which makes the entire network unaware of the new location of the producer. Thus, it causes some significant challenges, such as unnecessary Interest packet losses, high handoff latency, high signaling overhead cost, poor utilization of bandwidth, and path stretching. The aim of this research is to propose a Producer Mobility Support Scheme (PMSS) in order to minimize the handoff latency, signaling cost, improve data packets delivery via optimal path once a content producer relocated. The proposed PMSS model includes the formulated Mobility Weighted Function to incorporate movement behavior of the mobile producer. Also, Mobility Interest packet was designed to convey binding information and Broadcasting Strategy to facilitate handoff processes by updating the intermediate routers. Therefore, modeling and simulation methodologies were used in the design and performance evaluation of PMSS for rigorous investigation. The analytical result of PMSS scheme outperforms Optimal Producer Mobility for Larger-scale scheme with 50% lower handoff latency and signaling cost. Moreover, it minimizes 46% handoff signaling cost and improves 32% data path optimization as compared to the Kite scheme. The simulation results show that the proposed PMSS scheme minimizes 40% handoff latency, 28% packets delay, 28% unnecessary Interest packets loss, and improves 20% throughput. This study contributes to the development of the movement behavior model and mobility update packets. The findings have significant implication to support seamless mobility and the integration of NDN with other networks without additional mechanism