Selfish Nodes Research Articles

Social Trust Confirmation-Based Selfish Node Detection Algorithm in Socially Aware Networks

Nodes in socially aware networks (SANs) may act selfishly on individual bases due to resource constraints and socially selfish behavior arising from the social preferences of nodes. In response to such selfish behaviors exhibited by nodes, this paper proposes a social trust confirmation-based selfish node detection algorithm (STCDA). This algorithm first utilizes a subjective forwarding willingness detection mechanism to discern selfishness. If a node’s energy is insufficient or its message rejection rate is too high—that is, the node cannot or is unwilling to forward messages—it indicates that the node is selfish. Otherwise, it is evaluated more thoroughly through the node’s social trust detection mechanisms. It calculates the social trust level of nodes based on the benefits of forwarding messages, thereby distinguishing between individually selfish nodes and socially selfish nodes in the network. If further evaluation is needed, the final judgment will be made using the message confirmation feedback detection mechanism. This checks the message information forwarded by nodes in the network. If nodes fail to forward messages after receiving them—excluding reasons such as message expiration or temporary insufficient cache space—it indicates that the nodes are selfish. Results from experimental simulations show that this algorithm performs better than traditional algorithms. Under conditions of 80% selfish nodes, a message TTL of 300 min, and 10 MB of cache space, it improves the message delivery rate by 5.87% and reduces the average delay by 6.2% compared to the existing comprehensive confirmation-based selfish node detection algorithm.

A dual incentive mechanism based on graph attention neural network and contract in mobile opportunistic networks

In mobile opportunistic networks, messages are transmitted through opportunistic contacts between nodes. Hence, the successful delivery of messages heavily relies on the mutual cooperation among nodes in the network. However, due to limited network resources such as node energy and cache space, nodes tend to be selfish, and they are unwilling to actively participate in message forwarding. In response to this challenge, lots of incentive mechanisms have been proposed. However, most of them rely on single incentives, there are issues such as inadequate handling of selfish nodes and vulnerability to malicious attacks, which ultimately lead to poor incentive effects. Therefore, in this paper, a Dual Incentive mechanism based on Graph attention neural network and Contract (DIGC) is introduced to encourage active participation of network nodes in data transmission. This incentive mechanism is divided into two steps. In the first step, the graph attention neural network is used to evaluate the reputation of nodes to achieve the goal of reputation-based incentive, and blockchain is employed to store and manage node reputation to ensure security and transparency. In the second step, an incentive based on contract theory is introduced, where personalized contracts were designed based on the different resources owned by nodes, thereby establishing a reward mechanism to encourage collaborative transmission. Extensive simulations based on two real-life mobility traces have been done to evaluate the performance of our DIGC compared with other existing incentive mechanisms. The results show that, our proposed mechanism can greatly improve throughput and reduce average delay while ensuring the overall delivery performance of the network.

A Resource-Based Dynamic Pricing and Forced Forwarding Incentive Algorithm in Socially Aware Networking

In socially aware networking, nodes typically behave selfishly due to resource constraints and social correlations, resulting in low network performance. To incentivize selfish nodes to actively participate in message forwarding, this paper proposes a resource-based dynamic pricing and forced forwarding incentive algorithm (DFIA). Firstly, the algorithm introduces virtual currency as a transaction medium and then designs a pricing function based on factors such as the node’s resource status, participation contribution, location relevance, and social connectivity. It ensures that the forwarding service is transacted at a reasonable price through bargaining rules. Secondly, a forced forwarding strategy is implemented to compel selfish nodes, which are unwilling to participate in other nodes’ message forwarding, to forward a certain number of non-local messages. Meanwhile, in order to prevent nodes from discarding messages and to ensure successful forwarding to the destination, specific rules are used to allocate contribution values to nodes that successfully participate in message forwarding. Lastly, to avoid false quotation behavior, blockchain technology is employed. Transaction information is packaged into blocks and added to the blockchain after consensus validation by other nodes in the network, ensuring the transparency and immutability of transaction data. Simulation results indicate that compared with the existing incentive algorithms, this algorithm not only enhances message delivery probability but also effectively reduces average latency.

Selfish attack detection and response using cooperative backoff adjustment in wireless sensor networks

In recent times, the proliferation of intelligent internet of things (IoT) and wireless sensor network (WSN) technologies has significantly enhanced both daily life and industrial operations. Despite their widespread application, these networks face challenges with resource monopolization by selfish nodes, which detrimentally impacts network efficiency. This study introduces a novel detection and response strategy for selfish nodes, utilizing backoff adjustment mechanisms to reduce throughput deterioration. The effectiveness of this approach was rigorously evaluated using the NetSim simulator, focusing on the impact of backoff adjustment on network throughput. Simulation results indicate that the implementation of this method in the presence of selfish nodes yields a noteworthy improvement in network throughput, with approximately more than twice. This advancement holds promise for enhancing the robustness and efficiency of IoT and WSN technologies against disruptive selfish node behaviors.

Adaptive Methods for Revenue Model Learning of a Slice Broker in the Presence of Adversaries

AbstractIn the fifth-generation (5G) of mobile networks, Multi-Access Edge Computing (MEC) refers to the deployment of computing resources closer to the end-users for improved service delivery. In the context of 5G MEC, the slice broker plays a crucial role in managing the allocation of resources among the different network slices, which are logical networks on top of a shared infrastructure. The slice broker is a business entity that acts as an intermediary between the slice tenants and the infrastructure provider and is responsible for allocating resources (such as CPU, memory, and network bandwidth) required to set up the network. The slice broker must ensure that resources are allocated in a way that the revenue is maximized. In a dynamic environment, the slice broker must learn the revenue model adaptively and online. Adversaries can significantly reduce the revenue by misleading the system about the resources pretending to be selfish nodes, or creating noise. The slice broker should learn the revenue model in the presence of adversaries. We apply cooperative deep reinforcement learning with consensus mechanism and consensus deep learning to learn the revenue model adaptively. We also compare our proposed methods with the reference solution. Simulation results show that our proposed methods, especially the cooperative version, outperform the reference solution.

Crossover Boosted Grey Wolf Optimizer‐based framework for leader election and resource allocation in Intrusion Detection Systems for MANETs

AbstractMobile Ad hoc Networks (MANETs) is a self‐organizing networks without having a fixed infrastructure for making them susceptible to security threats. Intrusion Detection Systems (IDS) promotes security in MANETs by identifying malicious activities. Leader election is a fundamental aspect of IDS deployment, impacting resource allocation and system efficiency. This article presents a novel approach, the Crossover Boosted Grey Wolf Optimizer (CBGWO), for leader election and resource allocation in MANET‐based IDS. The proposed CBGWO algorithm integrates the Grey Wolf Optimizer (GWO) with innovative crossover operators that have an ability to enhance the capabilities of exploration and exploitation in the optimization process. The leader election problem is solved through applying multi‐objective optimization by considering energy consumption, reputation, and communication overhead. Objective functions are defined to maximize energy efficiency while maintaining a balanced distribution of leadership roles. Extensive simulations are conducted, varying network densities and the percentage of selfish nodes. Results demonstrate the effectiveness of the CBGWO‐based model in balancing energy consumption, prolonging network lifespan, and enhancing intrusion detection by comparing different state‐of‐the‐art models such as PCA‐FELM, CTAA‐MPSO, FLS‐RE, LEACH, DCAIDS, WOA‐GA, and VOELA. The proposed model achieved an energy consumption of 4.31 J, network lifetime of 560.482 ms, and average intrusion detection latency of 0.12 s, respectively. The proposed model outperforms than existing random and connectivity‐based leader election methods that is evaluated by taking main consideration of energy efficiency and network survivability. This research contributes to the field by introducing a robust algorithm for leader election in MANET‐based IDS, addressing challenges posed by network dynamics and resource constraints. The CBGWO‐based approach showcases its potential to achieve effective leader election and efficient resource allocation, thereby enhancing the security and sustainability of MANETs.

SCFS-securing flying ad hoc network using cluster-based trusted fuzzy scheme

AbstractFlying Ad hoc Networks have emerged as a promising technology for number of real-time applications. However, the flexible and unstructured characteristics of these networks make them vulnerable to security threats posed by malicious nodes, such as denial of service attacks, node impersonation, and information breaches. Another major issue is the consideration of those nodes being unable to prove their trustworthiness due to factors like hardware or software failure, or by link interruptions, during the processing of detection of false nodes in the network. The existing mechanisms encompassing encryption, authentication, and intrusion detection highlight limitations to secure real-time applications and services due to the high speed of flying nodes and the absence of fixed network structures. To overcome these constraints, this research paper incorporates a novel framework for evaluating and improving the security of network by introducing an innovative cluster-based approach. Moreover, it presents a fuzzy model that dynamically estimates the trust levels of both individual nodes and clusters, by assigning weight to the parameters to address vulnerabilities. Additionally, a trust reconfiguration mechanism is further proposed to address the issue of nodes unable to substantiate their trust by providing them with additional chances based on the collective trust from previous evaluations. Further, the paper incorporates a dynamic reputation system to proficiently identify and separate malicious and selfish nodes from the network. Simulation results indicate a significant improvement in performance metrics, with a considerable reduction in delay and drop ratio by 41.46% and 36.37%, respectively, while the sufficient rise of 54.71% and 46.05% in throughput and coverage, respectively, comparing with the considered state-of-art.

An artificial immune system‐based algorithm for selfish node detection in Mobile Ad Hoc Networks (MANETs)

AbstractThe Mobile Ad Hoc Network (MANET) depending on dynamic distributed topology presents several challenges such as decentralized infrastructure. In MANET, each node act as a host as well as a router to exchange packet. From Source to destination, the MANET nodes offer the ability of transmission to send, receive, and route traffic. Moreover, the presence of selfish nodes or malicious nodes in MANETs significantly degrades network performance. Identifying and isolating such nodes poses a formidable challenge. Consequently, this research introduces a security model founded on the principles of an Artificial Immune System (AIS) to detect and defend against Selfish Node (SN) attacks. This research work employs the principles of AIS to categorize a node's behavioral state based on the Mature Context Immune Antigen Rate (MCIAR). Subsequently, a hybrid protocol named Reliable History‐dependent Resource Conscious Clustered and Defensive AODV (RRCC‐DAODV) is introduced to detect Selfish Nodes (SNs) and counteract their attacks. Within the RRCC algorithm, the identification of selfish nodes relies on both Trust Value (TV) and Residual Energy (RE). Additionally, the proposed DAODV protocol incorporates the V‐detector algorithm to enhance defense mechanisms against potential attacks. The simulation of the proposed model is carried out in NS3. The proposed mechanism achieves a detection ratio of 94.05%. The simulation outcomes demonstrate the excellence of the implemented system. This superiority is observed when compared to other standard protocols, as indicated by the parameters of throughput, residual energy, packet delivery ratio, and delay.

Hybrid Bayesian and modified grey PROMETHEE-AL model-based trust estimation technique for thwarting malicious and selfish nodes in MANETs

Hybrid Bayesian and modified grey PROMETHEE-AL model-based trust estimation technique for thwarting malicious and selfish nodes in MANETs

Diminishing the selfish nodes by reputation and pricing system through SRA scenario

Diminishing the selfish nodes by reputation and pricing system through SRA scenario

Diminishing the selfish nodes by reputation and pricing system through SRA scenario

Diminishing the selfish nodes by reputation and pricing system through SRA scenario

FAOACA-SND: Fuzzy selfish node detection employing arithmetic optimization algorithm and cell automata in DTN

In recent years, the number of smart devices has grown exponentially. However, the security and privacy of these devices are often neglected, which can lead to serious consequences. In this paper, we propose a method for detecting selfish nodes in DTN. Our method is based on analyzing the behavior of nodes in the network and identifying those that are not following the protocol. Cluster read selection using the hybrid optimized algorithm and cellular automata is used as the first phase of selfish node detection. The second phase uses fuzzy logic to categorize the nodes as selfish or cooperative. Fuzzy logic receives four network parameters from the simulation in MATLAB in a dynamic environment. The four network parameters are packet drop, average delay, residual energy, and nodes' reputations. The simulation results depict that the proposed method is a very accurate way to find selfish nodes. In the proposed mechanism, the proportion of false positives has decreased by 61.53 % compared to the existing mechanism.

Develop a new handling method for selfish nodes in mobile ad-hoc networks

Mobile ad-hoc networks (MANETs) have been a crucial element of next-generation wireless networking technologies during the last decade. Because they allow users to access information and communicate with each other without infrastructure. Selfishness is one of the numerous undesirable behaviors that MANET network nodes may exhibit since this selfish node attempts to safeguard its own resources while accessing the services of other nodes and consuming their resources. Hence, a potential that the network's overall performance may degrade. This study developed a new method named detection, reintroduced, and collaborative of selfish node (DRCSN) that proposed detecting selfish nodes based on two factors: energy and the communication ratio (CR) and handling the rate of selfish nodes. Thus, selfish nodes were exploited to the maximum degree and significantly improve network performance. DRCSN was implemented inside ad-hoc on-demand distance vector (AODV) protocol. The test scenarios were implemented using the network simulator-2 (NS-2); many scenarios were created according to two important network parameters: the number of nodes and movement nodes. The proposed method improved the MANET's performance by increasing both the throughput and packet delivery ratio in the network in addition to that it reduced retransmission rate, delay, and power consumption compared to the related methods.

Task Scheduling of Real-Time Traffic Information Processing Based on Digital Twins

The Intelligent Transportation System under Digital Twins can provide accurate data sources for traffic control. The present work focuses on the real-time information processing and task scheduling problems of the Internet of Vehicles (IoV) system based on Virtual Reality. They are the Quality/Distance Algorithm (QDA), Task Density Algorithm, Distance Balance Algorithm (DBA), and Bionic-DBA (B-DBA). The simulation experiment analysis suggests that the DBA algorithm takes the balance of travel distance into account and effectively improves task quality. The Utility Function in B-DBA and the Biological Heuristic Search Algorithm in Pareto Ant Colony Optimization play a critically important role in enhancing the overall task quality. In addition, a Transmission based on Privacy Protection (TPP) algorithm is designed to protect the attribute-based privacy information in the traffic information transmission system. This algorithm ensures that the real-time traffic information processing system resists various attacks from malicious nodes. It has been verified that when the number of selfish nodes accounts for 30%, the transmission efficiency of the TPP algorithm reaches 0.77. The research content has a practical reference value for providing users with continuous and high-quality IoV network services.

An Improved Selfish Node Detection Algorithm for Cognitive Radio Mobile Ad Hoc Networks

An Improved Selfish Node Detection Algorithm for Cognitive Radio Mobile Ad Hoc Networks

Truthful and Optimal Data Preservation in Base Station-less Sensor Networks: An Integrated Game Theory and Network Flow Approach

We aim to preserve a large amount of data generated inside base station-less sensor networks (BSNs) while considering that sensor nodes are selfish. BSNs refer to emerging sensing applications deployed in challenging and inhospitable environments (e.g., underwater exploration); as such, there do not exist data-collecting base stations in the BSN to collect the data. Consequently, the generated data has to be stored inside the BSN before uploading opportunities become available. Our goal is to preserve the data inside the BSN with minimum energy cost by incentivizing the storage- and energy-constrained sensor nodes to participate in the data preservation process. We refer to the problem as DPP: d ata p reservation p roblem in the BSN. Previous research assumes that all the sensor nodes are cooperative and that sensors have infinite battery power and design a minimum-cost flow-based data preservation solution. However, in a distributed setting and under different control, the resource-constrained sensor nodes could behave selfishly only to conserve their resources and maximize their benefit. In this article, we first solve DPP by designing an integer linear programming (ILP)-based optimal solution without considering selfishness. We then establish a game-theoretical framework that achieves provably truthful and optimal data preservation in BSNs. For a special case of DPP wherein nodes are not energy-constrained, referred to as DPP-W, we design a data preservation game DPG-1 that integrates algorithmic mechanism design (AMD) and a more efficient minimum cost flow-based data preservation solution. We show that DPG-1 yields dominant strategies for sensor nodes and delivers truthful and optimal data preservation. For the general case of DPP (wherein nodes are energy-constrained), however, DPG-1 fails to achieve truthful and optimal data preservation. Utilizing packet-level flow observation of sensor node behaviors computed by minimum cost flow and ILP, we uncover the cause of the failure of the DPG-1. It is due to the packet dropping by the selfish nodes that manipulate the AMD technique. We then design a data preservation game DPG-2 for DPP that traces and punishes manipulative nodes in the BSN. We show that DPG-2 delivers dominant strategies for truth-telling nodes and achieves provably optimal data preservation with cheat-proof guarantees. Via extensive simulations under different network parameters and dynamics, we show that our games achieve system-wide data preservation solutions with optimal energy cost while enforcing truth-telling of sensor nodes about their private cost types. One salient feature of our work is its integrated game theory and network flows approach. With the observation of flow level sensor node behaviors provided by the network flows, our proposed games can synthesize “microscopic” (i.e., selfish and local) behaviors of sensor nodes and yield targeted “macroscopic” (i.e., optimal and global) network performance of data preservation in the BSN.

Dingo algorithm-based forwarder selection and huffman coding to improve authentication

<span>In wireless sensor network (WSN), the high volume of observe and transmitted data among sensor nodes make it requires to maintain the security. Even though numerous secure data transmission approaches designed over a network, an inadequate resource and the complex environment cause not able to used in WSNs. Moreover, secure data communication is a big challenging problem in WSNs especially for the military application. This paper proposes a dingo algorithm-based forwarder selection and huffman coding (DAHC) to improve authentication in internet of things (IoT) WSN. Initially, it detects the anomalous nodes by applying support vector machine (SVM) algorithm based on sensor node energy, node selfishness, and signal to noise ratio (SNR). Next, we using the dingo algorithm to select the forwarder node. This dingo algorithm computes the fitness function based on node degreee, node distance and node energy. Finally, the huffman coding to provide end to end authentication established on node energy from sender to receiver. During data transmission, the huffman coding to build the binary hop count value, it improves the authentication in the WSN. Performance results specify that this approach enhances the detection ratio and throughput.</span>

Efficient selfish node detection using SVM in IoT‐MANET environment

AbstractThe communication between nodes in the Internet of Things‐based mobile ad‐hoc networks environment is typically dependent on their selfless attitude. Whenever a node needs to send a message to another node in the network, it needs the help of one or more nodes to act as a router(s) that will bridge the gap between the source and the destination. All these selfless forwarding's require energy, bandwidth, and other resources. Therefore, some routers often raise a link breakage attack to save their resources. They keep silent after receiving a message; neither they acknowledge it nor they forward it. These selfish nodes must be identified and carefully avoided while choosing routes; otherwise, numerous messages will have to be resent, including control messages like route‐request. The identification depends on the past behavior of the node as well as velocity, the direction of movement, current geographical location and so forth. This article presents a support vector machine‐based node classification method that checks whether nodes are intentionally issuing a link breakage attack or it is really out of the radio range of its predecessor. The simulation results of the proposed method demonstrate that the proposed technique can correctly detect most of the selfish activities in the network and that also in much lesser time. It also enhances the packet delivery ratio and reduces delay and energy consumption.

A novel selfish node detection based on reputation and game theory in Internet of Things

A novel selfish node detection based on reputation and game theory in Internet of Things

Investigation of Different Mechanisms to Detect Misbehaving Nodes in Vehicle Ad-Hoc Networks (VANETs)

The vehicle ad-hoc network (VANET) is a crucial technology that will play a significant role in shaping the future of transition systems, which is widely used as a subset of ad-hoc networks. VANET aims to ensure driver safety by establishing independent communication with nearby vehicles. A key requirement for successful data transmission is cooperation among nodes, as factors such as high mobility, limited radio range, signal fading, and noise lead to packet loss. Security issues in vehicle ad-hoc networks have recently become a major concern. One factor that affects security is the presence of abusive nodes in the network. Like selfish nodes, they are reluctant to share their sources with their neighbors and try to keep their property. The misbehavior of malicious nodes includes dissemination of false traffic information, false location information, and redirection of packets to a wrong path, retransmission of packets, impersonation, so these nodes should be tracked down to ensure the operation of the network. This article provides a complete summary of various research works proposed to detect selfish and malicious nodes and isolate them from honest vehicles. This review article first describes the types of attacks. It then presents the methods proposed by researchers to deal with uncooperative nodes and compares their performance based on parameters such as the number of misbehaving nodes detected, overhead, throughput, layer involved in the attacks.