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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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... Distributed storage, routing techniques, and data aggregation may be seriously threatened by this destructive attack [32]. In wormhole attacks [33], the malicious node could be located close to legal nodes, and the malicious entity can use fictional connections that it truly controls to tunnel traffic between legitimate nodes. As a last resort, the rogue node may discard the tunneled packet or attack the routing protocols. ...
... A wormhole attack intercepts a message from one node to another and delivers it faster, that is, in fewer hops. As a result, the original, legitimate copy of the message will be discarded by the recipient node [23,25,26]. This attack can be mounted using two malicious nodes that can transmit messages to each other over a communication channel different from the channel the WSN uses. ...
This article discusses the modeling and detection of attacks in self-organizing decentralized wireless sensor networks (WSNs) that can be applied to various critical scenarios in practice. Security issues in this type of network have previously been studied to a rather poor extent. In particular, existing attack detection approaches and algorithms do not rely on the properties of self-organization and decentralization, which an attacker is able to exploit to compromise the network and its services. We propose, first, a model of a self-organizing decentralized wireless sensor network; second, a model of the attacks on such networks; third, algorithms for data collection and attack detection; and, finally, a technique for their application. The WSN model represents a formal specification of this type of network, defining the conditions and limitations of network self-organization and decentralization. The model is characterized by a proposed underlying role-based operation of network nodes and a set of their functional states. The proposed attack model covers the possible types of attacks that are relevant to a given type of WSN and are based on the exploitation of the self-organization and decentralization of the network. The developed algorithm for collecting data for attack detection presents specific types of data and their sources. The developed combined attack detection algorithm is formed of actions that detect relevant attacks on self-organizing decentralized WSNs using machine learning methods. The distinctive element of this algorithm is a set of highly specific features that are obtained by analyzing the data collected in the WSN and used to detect attacks. The proposed technique combines the constructed models and algorithms for the sake of tuning and deploying the attack detection module and the effective detection of attacks in practice. This technique specifies the main steps for the joint use of the models and algorithms and the assignment of data collection and detection parameters. The results of the experiments confirm the correctness of the constructed models, algorithms and technique due to the high values of the attack detection quality indicators. Therefore, the practical application of the proposed apparatus will facilitate improvements in the security of self-organizing decentralized WSNs. Experimental research has confirmed the practical applicability of our proposed solutions. In particular, it has shown that the proposed algorithms and the detection technique can detect both attacks implemented through the exploitation of the network’s properties of decentralization/self-organization and common variations in these attacks (i.e., without exploiting the decentralization property). In general, the experimental results expose a high quality of detection, with an f1-score equal to 0.99.
... These potential attacks demonstrate the critical need for Prime MotorX to address the identified vulnerabilities promptly and implement robust security measures to safeguard its IIoT infrastructure and prevent potential attacks from malicious actors.In this scenario, the exploited vulnerabilities encompass Weak Authentication and Misconfiguration. The launched attacks consist of the Wormhole attack [75], Blackhole [?], [76], the Sybil attack [?], the Sinkhole attack [77], the Routing Table Poisoning attack [78], DoS attack [79], [80], the DDoS attack [80], [81], with the target being the WSN Nodes of Prime MotorX. ...
We have witnessed significant technological advancement over the past few years, including the Internet of Things (IoT). The IoT’s ability to connect consumer appliances to the internet has changed the way we live. As a result of the significant benefits that IoT has brought to household usage, it has become a topic of discussion for research departments, leading to its expansion into industrial sectors, commonly known as the Industrial Internet of Things (IIoT). IIoT enables automation and the use of intelligent machines to improve product manufacturing processes and enhance our lives as customers. However, as IIoT-enabling technology and applications continue to grow, security issues and privacy protection challenges become harder to manage, which frequently results in data breaches and sensitive information disclosures. This paper first explains what the reader needs to know about the IIoT system architecture in Industry 4.0 to make it easier for them to understand. Second, a hacking scenario is utilized as a methodology to conduct an in-depth analysis of various security issues, as well as their impacts and countermeasures, for each level of the IIoT architecture. Additionally, our hacking scenarios present a variety of targets from which malicious actors can launch their assaults. Third, we provide a thorough review of the various blockchain solutions currently being employed to protect IIoT systems. Finally, this paper draws to a close by outlining certain gaps and potential solutions that could be investigated in subsequent studies to strengthen security and enhance privacy for IIoT systems.
... Therefore, three questions should be addressed (Adarsh et al., 2021): How to elect a CH for each community? (Ghugar and Pradhan, 2021)? How does CH detect the existence of a wormhole attack? ...
... Consider the following scenarios (Adarsh et al., 2021): A node moves so fast compared to its neighbors that its connections and communications with other nodes are very brief. In this case, this node is of little use and should not be assigned important responsibilities (Ghugar and Pradhan, 2021). If a node with relatively fast mobility compared with its neighbors is selected as the CH, the cluster managed by this node will collapse quickly. ...
Mobile ad hoc networks in smart grid become more and more popular and significant. However, the deployment scenarios, the functionality requirements and the limited capabilities make them vulnerable to a large group of attacks, e.g., wormhole attacks. In this paper, a novel cluster-based scheme is proposed for the purpose of preventing wormhole attacks. Firstly, a clustering algorithm is proposed that employs a powerful analytical hierarchy process methodology to elect clusterheads. Afterwards, the elected clusterheads are required to implement the wormhole attacks prevention scheme which includes two phases, i.e., detection phase and location phase. By detection phase, the existence of wormhole attacks can be detected. By location phase, the wormhole nodes are able to be detected. Simulation results indicate the scheme in our paper can be used to prevent wormhole attacks in ad hoc networks efficiently.
... When a malicious node receives an RREQ packet, it sends an RREP showing the shortest and fastest route to the intended destination. When a source node starts sending packets to a malicious node, it further sends to another distantly located wormhole node via a wormhole tunnel which may drop all the data packets and cause network interruption [6]- [8]. It can quickly be launched without having a knowledge network or compromising any legitimate nodes or cryptographic mechanism. ...
Wireless Body Area Network (WBAN) is a promising technology that is having a significant number of applications in healthcare. Despite having several benefits, WBAN is susceptible to several security attacks, particularly related to the node misbehavior attacks such as sinkholes, wormholes, blackholes, and gray hole attacks. In this research work, the performance of the AODV protocol against wormhole and sinkhole attacks has been analyzed. This comparison-based study has been achieved for the PDR (Packet Delivery Ratio), NRL (Normalized Routing Load), residual energy, packet loss, average throughput, and average end-to-end delay metrics for WBAN with 20 and 50 nodes respectively. The results show that in 20 nodes network the PDR and average throughput of the sinkhole is 32% and 53% more than the wormhole attack, respectively. However, NRL is 36% and the average e2e delay is 63% less than the wormhole attack. In 50 nodes network, the PDR of a sinkhole attack is 26% and the average throughput is 10.7% more than the wormhole attack. Though, NRL and average e2e delays are 41.7% and 78.5% less than the wormhole attack, respectively. Hence, the wormhole attack in either network is more destructive than the sinkhole attack. Based on the simulation results, some solutions against the mentioned attacks have been recommended.
... Umashankar et al. [20] presented a detailed review of the literature on wormhole attack detection. However, the latest schemes were not included. ...
The popularity of wireless sensor networks for establishing different communication systems is increasing daily. A wireless network consists of sensors prone to various security threats. These sensor nodes make a wireless network vulnerable to denial-of-service attacks. One of them is a wormhole attack that uses a low latency link between two malicious sensor nodes and affects the routing paths of the entire network. This attack is brutal as it is resistant to many cryptographic schemes and hard to observe within the network. This paper provides a comprehensive review of the literature on the subject of the detection and mitigation of wormhole attacks in wireless sensor networks. The existing surveys are also explored to find gaps in the literature. Several existing schemes based on different methods are also evaluated critically in terms of throughput, detection rate, low energy consumption, packet delivery ratio, and end-to-end delay. As artificial intelligence and machine learning have massive potential for the efficient management of sensor networks, this paper provides AI- and ML-based schemes as optimal solutions for the identified state-of-the-art problems in wormhole attack detection. As per the author’s knowledge, this is the first in-depth review of AI- and ML-based techniques in wireless sensor networks for wormhole attack detection. Finally, our paper explored the open research challenges for detecting and mitigating wormhole attacks in wireless networks.
... A geographic leash specifies the distance between the receiver and sender. A receiving node that receives the packet calculates the transmission time and distance [23]. The receiver can detect whether a packet has passed a wormhole attack through data analysis [24]. ...
... We discuss issues to be addressed in future research to overcome security issues. Wormhole attack is an issue in wireless communication, and no robust detection method is proposed for routing purposes, which accurately detects this type of attack [109]. It is an open research area where work is still required to develop or propose a routing base scheme, which consumes less energy and detects wormhole attacks [110]. ...
A wireless network is used to connect various wired organizational structures and provide connectivity within the organization for employees to move freely by avoiding the hurdle of a physical network. Maintenance of WLAN security is crucial to an organization because WLANs are directly linked to the core organization’s network. In this paper, we reviewed the architectures and protocols of wireless communication, security issues, and type of threats used to launch an attack as well as their solutions. Finally, we discuss open research for future development to make a secure wireless network and safe for data transfer.
... The second, and potentially more dangerous, effect is the use of network capacity. [11]. Denial of Service (DoS) attacks in a flood attack, zombies flood a victim system with a massive amount of traffic, causing the bandwidth of the victim system to become overloaded. ...
