A MANET is an entirely devoid-of-infrastructure network. This network is made up of nodes that randomly move around. Since MANET has no central supervision, it can be formed anywhere using randomly moving nodes. This network faces numerous security issues as a result of MANET's vulnerable behaviour. There are numerous security threats to MANET that do not have a solution. It is also difficult to detect these issues. Some security threats are extremely serious. These threats have the potential to bring the network to its knees. Researchers are attempting to determine how to respond to these threats. The NID system is an important tool for protecting MANETs from vulnerabilities and malicious activities. A slew of new techniques have recently been demonstrated; however, due to the continuous launch of the various threats that existing systems are unable to detect, these techniques face significant challenges. The authors have proposed two stage deep learning (TSDL) model in this publication. For efficient NID, a stacked auto-encoder (SAE) with a softmax classifier (SMC) is used. There are two decisive phases in the model: A first phase in the system traffic classification process that uses a possibility score value to determine whether system movement is regular or irregular. This is then used as a bonus feature during the last stage of the decision-making process. Both the normal state and various types of attacks are to be detected, the suggested framework can automatically and efficiently gain knowledge and categories of beneficial feature representations from large amounts of unlabelled data.

Network coding is a progressive enhancement in natural network routing that increases throughput and reliability for unicast, multicast, and even broadcast transmissions. P2P networks are ideal for implementing network coding algorithms for two reasons: I. A P2P network's topology isn't predetermined. As a result, designing a topology that is compatible with the network coding algorithm is much easier. II. Every peer is an end host in this network. As a result, instead of saving and sending the message, complex network coding operations, such as encoding and decoding, are now easier to perform. The objective of this work is to use the best features of network coding algorithms and properly apply them to P2P networks to create an efficient and reliable data transmission service. The goal of the network coding algorithm is to make better use of network resources and thus increase P2P network capacity. An encoding algorithm that enables an intermediate peer to produce output messages by encoding (that is, computing a function of the data it receives. The decoder's role is to obtain enough encoded packets so that the original information can be recovered. This research work has measured an amount of hypothetical and applied consequences in which the network coding procedure or a variation of it is used to improve performance parameters such as throughput and reliability in P2P network data transmission based on network coding. The comparison of data transmission between network routing and network coding algorithms was the main focus of this paper. According to our simulations, the new network coding systems can reach 15% to 20% upper throughput than supplementary P2P network routing systems.

The use of smart devices is driving the Internet of Things (IoT) trend today. Day by day IoT helps to support more services like car services, healthcare services, home automation, and security services, weather prediction services, etc, to ease user’s life. Integration of heterogeneous IoT devices and social resources sometimes creates many problems like the privacy of data. To avoid privacy issues, an appropriate access control mechanism is required to check authorized and trusted devices, so that only valid devices can access the data which is only required. In the sequel, this paper presents implementation of distributed and dynamic trust based access control mechanism (DDTAC) for secure machine to machine communication or distributed IoT environment. Novelty of this mechanism is that, it uses trust calculation and device classification for dynamic access control. The proposed scheme is implemented, tested and deployed on Node MCU and same mechanism is also simulated on NS-2 for large number of nodes. This access control model support Scalability, Heterogeneity, Privacy, Trust, Selective disclosure, Principle of least privileges, and lightweight calculation features. Results of this models proves that it gives good performance as compared to existing scheme in terms of scalability, throughput and delay. As number of devices increase it does not degrade performance. This mechanism is also protected against the Man-in-the-Middle attack, Sniffing attack, Session Hijacking attacks and Injection attacks. It required less time to detect and resist those attacks.