malicious nodes
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2022 ◽  
Vol 22 (3) ◽  
pp. 1-22
Yi Liu ◽  
Ruihui Zhao ◽  
Jiawen Kang ◽  
Abdulsalam Yassine ◽  
Dusit Niyato ◽  

Federated Edge Learning (FEL) allows edge nodes to train a global deep learning model collaboratively for edge computing in the Industrial Internet of Things (IIoT), which significantly promotes the development of Industrial 4.0. However, FEL faces two critical challenges: communication overhead and data privacy. FEL suffers from expensive communication overhead when training large-scale multi-node models. Furthermore, due to the vulnerability of FEL to gradient leakage and label-flipping attacks, the training process of the global model is easily compromised by adversaries. To address these challenges, we propose a communication-efficient and privacy-enhanced asynchronous FEL framework for edge computing in IIoT. First, we introduce an asynchronous model update scheme to reduce the computation time that edge nodes wait for global model aggregation. Second, we propose an asynchronous local differential privacy mechanism, which improves communication efficiency and mitigates gradient leakage attacks by adding well-designed noise to the gradients of edge nodes. Third, we design a cloud-side malicious node detection mechanism to detect malicious nodes by testing the local model quality. Such a mechanism can avoid malicious nodes participating in training to mitigate label-flipping attacks. Extensive experimental studies on two real-world datasets demonstrate that the proposed framework can not only improve communication efficiency but also mitigate malicious attacks while its accuracy is comparable to traditional FEL frameworks.

Dr. Sultanuddin SJ ◽  
Dr. Md. Ali Hussain ◽  

Mobile ad hoc networks (MANETs) have evolved into a leading multi-hop infrastructure less wireless communication technology where every node performs the function of a router. Ad- hoc networks have been spontaneously and specifically designed for the nodes to communicate with each other in locations where it is either complex or impractical to set up an infrastructure. The overwhelming truth is that with IoT emergence, the number of devices being connected every single second keeps increasing tremendously on account of factors like scalability, cost factor and scalability which are beneficial to several sectors like education, disaster management, healthcare, espionage etc., where the identification and allocation of resources as well as services is a major constraint. Nevertheless, this infrastructure with dynamic mobile nodes makes it more susceptible to diverse attack scenarios especially in critical circumstances like combat zone communications where security is inevitable and vulnerabilities in the MANET could be an ideal choice to breach the security. Therefore, it is crucial to select a robust and reliable system that could filter malicious activities and safeguard the network. Network topology and mobility constraints poses difficulty in identifying malicious nodes that can infuse false routes or packets could be lost due to certain attacks like black hole or worm hole. Hence our objective is to propose a security solution to above mentioned issue through ML based anomaly detection and which detects and isolates the attacks in MANETs. Most of the existing technologies detect the anomalies by utilizing static behavior; this may not prove effective as MANET portrays dynamic behavior. Machine learning in MANETs helps in constructing an analytical model for predicting security threats that could pose enormous challenges in future. Machine learning techniques through its statistical and logical methods offers MANETs the learning potential and encourages towards adaptation to different environments. The major objective of our study is to identify the intricate patterns and construct a secure mobile ad-hoc network by focusing on security aspects by identifying malicious nodes and mitigate attacks. Simulation-oriented results establish that the proposed technique has better PDR and EED in comparison to the other existing techniques.

Manxiang Yang ◽  
Baopeng Ye ◽  
Yuling Chen ◽  
Tao Li ◽  
Yixian Yang ◽  

AbstractK-anonymity has been gaining widespread attention as one of the most widely used technologies to protect location privacy. Nevertheless, there are still some threats such as behavior deception and service swing, since utilizing distributed k-anonymity technology to construct an anonymous domain. More specifically, the coordinate of the honest node will be a leak if the malicious nodes submit wrong locations coordinate to take part in the domain construction process. Worse still, owing to service swing, the attacker increases the reputation illegally to deceive honest nodes again. To overcome those drawbacks, we propose a trusted de-swinging k-anonymity scheme for location privacy protection. Primarily, we introduce a de-swinging reputation evaluation method (DREM), which designs a penalty factor to curb swinging behavior. This method calculates the reputation from entity honesty degree, location information entropy, and service swing degree. Besides, based on our proposed DREM, a credible cloaking area is constructed to protect the location privacy of the requester. In the area, nodes can choose some nodes with a high reputation for completing the construction process of the anonymous domain. Finally, we design reputation contracts to calculate credit automatically based on smart contracts. The security analysis and simulation results indicate that our proposed scheme effectively resists malicious attacks, curbs the service swing, and encourages nodes to participate honestly in the construction of cloaking areas.

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 411
Saba Awan ◽  
Nadeem Javaid ◽  
Sameeh Ullah ◽  
Asad Ullah Khan ◽  
Ali Mustafa Qamar ◽  

In this paper, an encryption and trust evaluation model is proposed on the basis of a blockchain in which the identities of the Aggregator Nodes (ANs) and Sensor Nodes (SNs) are stored. The authentication of ANs and SNs is performed in public and private blockchains, respectively. However, inauthentic nodes utilize the network’s resources and perform malicious activities. Moreover, the SNs have limited energy, transmission range and computational capabilities, and are attacked by malicious nodes. Afterwards, the malicious nodes transmit wrong information of the route and increase the number of retransmissions due to which the SNs’ energy is rapidly consumed. The lifespan of the wireless sensor network is reduced due to the rapid energy dissipation of the SNs. Furthermore, the throughput increases and packet loss increase with the presence of malicious nodes in the network. The trust values of SNs are computed to eradicate the malicious nodes from the network. Secure routing in the network is performed considering residual energy and trust values of the SNs. Moreover, the Rivest–Shamir–Adleman (RSA), a cryptosystem that provides asymmetric keys, is used for securing data transmission. The simulation results show the effectiveness of the proposed model in terms of high packet delivery ratio.

2022 ◽  
Vol 12 (1) ◽  
pp. 476
Kashif Naseer Qureshi ◽  
Luqman Shahzad ◽  
Abdelzahir Abdelmaboud ◽  
Taiseer Abdalla Elfadil Eisa ◽  
Bandar Alamri ◽  

The rapid advancement in the area of the Internet of Vehicles (IoV) has provided numerous comforts to users due to its capability to support vehicles with wireless data communication. The exchange of information among vehicle nodes is critical due to the rapid and changing topologies, high mobility of nodes, and unpredictable network conditions. Finding a single trusted entity to store and distribute messages among vehicle nodes is also a challenging task. IoV is exposed to various security and privacy threats such as hijacking and unauthorized location tracking of smart vehicles. Traceability is an increasingly important aspect of vehicular communication to detect and penalize malicious nodes. Moreover, achieving both privacy and traceability can also be a challenging task. To address these challenges, this paper presents a blockchain-based efficient, secure, and anonymous conditional privacy-preserving and authentication mechanism for IoV networks. This solution is based on blockchain to allow vehicle nodes with mechanisms to become anonymous and take control of their data during the data communication and voting process. The proposed secure scheme provides conditional privacy to the users and the vehicles. To ensure anonymity, traceability, and unlinkability of data sharing among vehicles, we utilize Hyperledger Fabric to establish the blockchain. The proposed scheme fulfills the requirement to analyze different algorithms and schemes which are adopted for blockchain technology for a decentralized, secure, efficient, private, and traceable system. The proposed scheme examines and evaluates different consensus algorithms used in the blockchain and anonymization techniques to preserve privacy. This study also proposes a reputation-based voting system for Hyperledger Fabric to ensure a secure and reliable leader selection process in its consensus algorithm. The proposed scheme is evaluated with the existing state-of-the-art schemes and achieves better results.

Subiksha. V

Abstract: Due to the characteristics like limited resources and dynamic topology, wireless sensor networks (WSNs) are facing two major problems such as security and energy consumption. To deal with various improper behaviors of nodes the trust-based solutions are possible but still exist a variety of attacks, high energy consumption, and communication congestion between nodes. Therefore, this paper proposes an advanced and efficient trust-based secure and energy-efficient routing protocol (TBSEER) to solve these network problems and to avoid malicious nodes. Efficient Adaptable Ant Colony Optimization Algorithm (EAACO) calculates the comprehensive trust value through adaptive direct trust value, indirect trust value, and energy trust value, which can be resistant to internal network attacks such as sinkhole, black hole, selective forwarding, and hello flood attacks. In addition, to fast identify the malicious nodes in the WSN, the adaptive penalty mechanism and volatilization factor are used. Moreover, the nodes only need to calculate the direct trust value, and the indirect trust value is obtained by the sink, so as to further reduce the energy consumption caused by iterative calculations. To actively avoid network attacks, the cluster heads find the safest multi-hop routes based on the comprehensive trust value. The simulation results show that the proposed EAACO reduces network energy consumption, speeds up the identification of malicious nodes, as well as resists all common attacks. Keywords: Comprehensive trust value, direct trust value, indirect value, EAACO, network attacks, wireless sensor networks

Atul Patial

Abstract: MANET (mobile ad hoc network) is considered to be a network with no centralized control. This network typically faces two major challenges related to routing and security. Both these issues affect the performance of this network to a large extent. The black hole attack belongs to the category of active attacks that are launched to reduce the network throughput and other parameters. The research works carried out in the past used different techniques to isolate malicious nodes, but with the inclusion of extra hardware and software tools. The various techniques for the security in MANET are analyzed in terms of certain parameters. Keywords: MANET, Black Hole, Security Techniques

Andrey Silva

The constant growing on the number of vehicles is increasing the complexity of traffic in urban and highway environments. It is paramount to improve traffic management to guarantee better road usage and people’s safety. Through efficient communications, Vehicular Ad hoc Networks (VANETs) can provide enough information for traffic safety initiatives, daily traffic data processing, and entertainment information. However, VANETs are vulnerable to malicious nodes applying different types of net-work attacks, where an attacker can, for instance, forge its position to receive the data packet and drop the message. This can lead vehicles and authorities to make incorrect assumptions and decisions, which can result in dangerous situations. Therefore, any data dissemination protocol designed for VANET should consider security issues when selecting the next-hop forwarding node. In this paper, we propose a security scheme designed for position-based routing algorithms, which analyzes nodes position, transmission range, and hello packet interval. The scheme deals with malicious nodes performing network attacks, faking their positions forcing packets to be dropped. We used the Simulation of Urban MObility (SUMO) and Network Simulator-version 3 (NS-3) to compare our proposed scheme integrated with two well-known position-based algorithms. The results were collected in an urban Manhattan grid environment varying the number of nodes, the number of malicious nodes, as well as the number of source-destination pairs. The results show that the proposed security scheme can successfully improve the packet delivery ratio while maintaining low average end-to-end delay of the algorithms. 

Electronics ◽  
2021 ◽  
Vol 10 (24) ◽  
pp. 3105
Haleem Farman ◽  
Abizar Khalil ◽  
Naveed Ahmad ◽  
Waleed Albattah ◽  
Muazzam A. Khan ◽  

The Internet of vehicles (IoV) depicts a reality where ordinary things are connected to vehicular ad-hoc networks (VANETs), allowing them to transmit and collaborate. By placing these regular objects in VANETs and making them available at any time, this network and data sharing may raise real privacy and security issues. Thus, group-based communication is mostly preferred in the literature. However, in heavy network scenarios, cluster-based communication mostly leads to additional overload in the form of the group leader that causes delay and disrupts the performance of a network. Due to the interaction of VANETs with applications that are not stable for life, privacy and security mechanism for detecting many malicious nodes is in great demand. Therefore, a multi-phantom node selection has been proposed in this paper to select trustworthy, normal, and malicious nodes. The multi-phantom node scheme is proposed to reduce the phantom node load, where the multi-lateral nodes in a cluster act as a phantom node to share the load. A multi criteria decision-making (MCDM) methodology (analytic network process) is used to optimize the phantom node to pre-serve privacy using the privacy preserved trust relationship (PTR) model. The results show checking the stability of parameters and using sensitivity analysis by considering different scenarios for the most optimal phantom node to preserve vehicle location privacy. The impact of the proposed model will be more clearly visible in its real-time implementation in urban areas vehicle networks.

Electronics ◽  
2021 ◽  
Vol 10 (24) ◽  
pp. 3074
Deepak Prashar ◽  
Mamoon Rashid ◽  
Shams Tabrez Siddiqui ◽  
Dilip Kumar ◽  
Amandeep Nagpal ◽  

Localization and security are among the most dominant tasks of wireless sensor networks (WSN). For applications containing sensitive information on the location parameters of the event, secure localization is mandatory and must not be compromised at any cost. The main task, as if any node is malicious, is to authenticate nodes that are involved in the localization process. In this paper, we propose a secure hop-based algorithm that provides a better localization accuracy. In addition, to maintain the security of the localization process, the digital signature approach is used. Moreover, the impact of malicious nodes on the proposed scheme has also been observed. The proposed approach is also contrasted with the basic DV-Hop and improved DV-Hop based on error correction. From the simulation outcomes, we infer that this secure digital-signature-based localization strategy is quite robust against any node compromise attacks, thereby boosting its precision. Comparisons between the proposed algorithm and the state of the art were made on the grounds of different parameters such as the node quantity, ratio of anchor nodes, and range value towards the localization error.

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