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The Internet of Things (IoT) ushers in a new era of communication that depends on a broad range of things and many types of communication technologies to share information. This new age of communication will be characterised by the following characteristics: Because all of the IoT's objects are connected to one another and because they function in environments that are not protected, it poses a significantly greater number of issues, constraints, and challenges than do traditional computing systems. This is due to the fact that traditional computing systems do not have as many interconnected components. Because of this, it is imperative that security be prioritised in a new approach, which is not something that is currently present in conventional computer systems. The Wireless Sensor Network, often known as WSN, and the Mobile Ad hoc Network are two technologies that play significant roles in the process of building an Internet of Things system. These technologies are used in a wide variety of activities, including sensing, environmental monitoring, data collecting, heterogeneous communication techniques, and data processing, amongst others. Because it incorporates characteristics of both MANET and WSN, IoT is susceptible to the same kinds of security issues that affect those other networks. An assault known as a Delegate Entity Attack (DEA) is a subclass of an attack known as a Denial of Service (DoS). The attacker sends an unacceptable number of control packets that have the appearance of being authentic. DoS assaults may take many different forms, and one of those kinds is an SD attack. Because of this, it is far more difficult to recognise this form of attack than a simple one that depletes the battery's capacity. One of the other key challenges that arise in a network during an SD attack is that there is the need to enhance energy management and prolong the lifespan of IoT nodes. This is one of the other significant issues that arise in a network when an SD attack is occurs. It is recommended that you make use of a Random Number Generator with Hierarchical Intrusion Detection System, abbreviated as RNGHID for short. The ecosystem of the Internet of Things is likely to be segmented into a great number of separate sectors and clusters. The HIPS system has been partitioned into two entities, which are referred to as the Delegate Entity (DE) and the Pivotal Entity, in order to identify any nodes in the network that are behaving in an abnormal manner. These entities are known, respectively, as the Delegate Entity and the Pivotal Entity (PE). Once the anomalies have been identified, it will be possible to pinpoint the area of the SD attack torture and the damaging activities that have been taken place. A warning message, generated by the Malicious Node Alert System (MNAS), is broadcast across the network in order to inform the other nodes that the network is under attack. This message classifies the various sorts of attacks based on the results of an algorithm that employs machine learning. The proposed protocol displays various desired properties, such as the capacity to conduct indivisible authentication, rapid authentication, and minimum overhead in both transmission and storage. These are only a few of the desirable attributes. [ABSTRACT FROM AUTHOR]

Request route flooding attack is one of the main challenges in the security of Mobile Ad Hoc Networks (MANETs) as it is easy to initiate and difficult to prevent. A malicious node can launch an attack simply by sending an excessively high number of route request (RREQ) packets or useless data packets to nonexistent destinations. As a result, the network is rendered useless as all its resources are used up to serve this storm of RREQ packets and hence unable to perform its normal routing duty. Most existing research efforts on detecting such a flooding attack use the number of RREQs originated by a node per unit time as the threshold to classify an attacker. These algorithms work to some extent; however, they suffer high misdetection rate and reduce network performance. This paper proposes a new flooding attacks detection algorithm (FADA) for MANETs based on a machine learning approach. The algorithm relies on the route discovery history information of each node to capture similar characteristics and behaviors of nodes belonging to the same class to decide if a node is malicious. The paper also proposes a new flooding attacks prevention routing protocol (FAPRP) by extending the original AODV protocol and integrating FADA algorithm. The performance of the proposed solution is evaluated in terms of successful attack detection ratio, packet delivery ratio, and routing load both in normal and under RREQ attack scenarios using NS2 simulation. The simulation results show that the proposed FAPRP can detect over 99% of RREQ flooding attacks for all scenarios using route discovery frequency vector of sizes larger than 35 and performs better in terms of packet delivery ratio and routing load compared to existing solutions for RREQ flooding attacks. [ABSTRACT FROM AUTHOR]