… the network operation and detect the attack. In current digital information world, with the help
of machine learning algorithms the … In this study six machine learning modelled IDSs were …

… to apply machine learning to problems in ad hoc networking. First, Section II gives short
descriptions of suitable machine learning approaches for use in wireless ad-hoc networks, …

… -organization and wireless communication channels, ad-hoc network is … model to Ad-hoc
network for DoS and privacy attacks detection. Aiming at the characteristics of Ad-hoc network, …

… Then, by adopting a bottom-up approach, we examine existing work on machine learning for
the IoT at the physical, data-link and network layer of the protocol … Wireless ad hoc network …

… algorithm in heterogeneous networks is a big … machine learning and deep learning in
wireless networks. Due to the dynamic behaviour of network scenarios in several adhoc networks (…

… studied and simulation used in Ad-hoc network which enables the users to … model of
machine learning. This paper presents the futuristic anatomy of different machine learning models …

… This section further describes the machine learning approaches applied to the trust
enhancement of MANETs. Since mobile ad hoc networks are fully distributed systems when …

… node communicates with other nodes in a wireless ad-hoc network [12]. Figure 10 illustrates
… FANET still uses conventional routing protocols used in mobile ad-hoc network (MANET) an …

… machine learning techniques. In this paper we try to reach this goal by applying Multi-Agent
Reinforcement Learning (… Although model-free Reinforcement Learning (RL) schemes are …

… Ad-hoc Network) is a developing technology, which is a combination of cellular technology,
ad-hoc network & wireless LAN … In this paper machine learning method is used to detect the …