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Over the first few years, a wireless sensor network has a very important role over the networks. The primary features of WSN include satellite communication, broadcast channel, hostile environment, medical system and data gathering. There are a lot of attacks available in WSN. Wormhole attack is one of the severe attacks, which is smoothly resolved...
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From the last decade, a wireless sensor network (WSN) has a very important role over the networks. The primary features of WSN include satellite communication, broadcast channel, hostile environment, medical system and data gathering. There are a lot of attacks available in WSN.In wormhole attack scenario is brutal from other attacks, which is smoo...
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... The Sybil threat and selective forwarding assault are combined to create a wormhole attack. The data packet received Node D from Node A, as illustrated in Figure 3, and conversely [19]. ...
Wireless sensor networks (WMSNs) are becoming increasingly popular in many fields, from academia to transportation, environmental monitoring, wildlife preservation, and military espionage. Therefore, examining potential threats, power consumption, vulnerability recognition, and systemic vulnerability characteristics is essential to develop a reliable information security approach for WSNs. As a result, it is becoming increasingly crucial for the technical community to conduct intrusion recognition method evaluations. Since this is the case, using deep learning techniques in creating intrusion identification and mitigation systems for wireless multimedia sensor networks is essential. This article examines how well different machine learning and deep learning algorithms perform in attack identification systems. Testing the efficacy of different methods on the WMSN-DS database through experimentation is essential. In this work, we combine the power of a Convolutional Neural Network classifier with a Random forest. In order to accomplish this, a Convolutional Neural Network with a Random Forest Classifier is used. The intrusion detection system (IDS) is a crucial technique proposed in this study for WMSN. To address this issue, the current study proposal uses deep Learning with a Random Forest classifier to detect and prevent attacks and to promote efficient forwarding in WMSNs. Multiple WMSN assaults have been investigated, and the results of these investigations have been critically evaluated.
... The observation of such a type of attack is too difficult. Therefore, authors have majorly focused only on the observation so that by applying different techniques and methods they will simply apply their observation for the detection of wormhole attacks [4]. ...
Over the last few years there has been explosive growth in the use of the Wireless communications from top to bottom i.e. Satellite transmission to home Wireless personal area networks. The primary advantage of a wireless network is the ability of wireless node to communicate with the universe during mobility. Two basic system models of a wireless system are fixed and Mobile Adhoc Network has been developed for the wireless network paradigm. The first model consists of multiple numbers of mobile workstations and relatively fewer but more powerful, having fixed routers. The second model has been proposed to set up a network on-demand basis. When a collection of wireless mobile nodes are capable of communicating with each other without the use of network infrastructure, centralized administration, or centralized control like mobile switching centers or base stations, that set up is called Mobile Adhoc Network. Here each mobile node operates not only as a host but also as a router, forwards packets to other mobile nodes in the network that may not be within direct wireless transmission range of each other. Each node participates in an Adhoc routing protocol (Yadav et al. in Comput Netw 118:15–23, 2017) that allows it to discover multiple paths through the network to any other node. Institute of Electrical and Electronics Engineers 802.11 (IEEE 802.11) [Kaur et al. in Int J Res Advent Technol 5(8), 2017] is a vital part of pervasive networks, which is a special kind of network where users can join and communicate anytime or anywhere on the fly. It is a popular kind of network because its applications cover a variety of areas. A unique communication paradigm is used which is able to run without fixed infrastructure and relies on wireless terminals for routing and transporting services. A number of security and scalability issues arise due to its wireless transmissions and unpredictable topology changes. Open standard, dynamic topology, scattered arrangements, and multi-hop routing are crucial features of IEEE 802.11 networks that make them vulnerable to various kinds of attacks. A Wormhole attack is one of the serious kinds of attack. Thus, security is the most important concern in IEEE 802.11 networks. This paper presents the refurbishment of the routing structure of Ad hoc On-Demand Distance Vectors (AODV) protocol (Ning et al. in Proceedings of the IEEE systems, man and cybernetics society information assurance workshop (IAW), West Point, New York, USA, pp 60–67, 2003). It will be helpful to safeguard IEEE 802.11 networks from Wormhole attacks by preventing Wormhole Attacks. The DAPS technique had been presented in Saini et al. (Int J Res Advent Technol 6(4), 2018) to detect Wormhole Attacks. Further to DAPS technique, a new Wormhole prevention technique has been introduced in this paper called Proactive Prevention Key Solution (PPKS).
... Various wormhole attack detection methods exist in ad-hoc wireless networks [21]. The method of providing a directional antenna to a node uses the node's antenna area to establish a connection between nodes [22]. ...
Wireless sensor networks (WMSNs) are becoming increasingly popular in many fields, from academia to transportation, environmental monitoring, wildlife preservation, and military espionage. Therefore, examining potential threats, power consumption, vulnerability recognition, and systemic vulnerability characteristics is essential to develop a reliable information security approach for WSNs. As a result, it is becoming increasingly crucial for the technical community to conduct intrusion recognition method evaluations. Since this is the case, using deep learning techniques in creating intrusion identification and mitigation systems for wireless multimedia sensor networks is essential. This article examines how well different machine learning and deep learning algorithms perform in attack identification systems. Testing the efficacy of different methods on the WMSN-DS database through experimentation is essential. In this work, we combine the power of a Convolutional Neural Network classifier with a Random Forest. To accomplish this, a Convolutional Neural Network with a Random Forest Classifier is used. The intrusion detection system (IDS) is a crucial technique proposed in this study for WMSN. To address this issue, the current study proposal uses deep Learning with a Random Forest classifier to detect and prevent attacks and to promote efficient forwarding in WMSNs. Multiple WMSN assaults have been investigated, and the results of these investigations have been critically evaluated.
