Trustworthiness Of Nodes Research Articles

Security and Energy Management in Cooperative Spectrum Sensing

The revolutionary concept of cognitive radio (CR) has been developed to address the issue of spectrum scarcity in wireless networks. Cooperative spectrum sensing is seen as a promising strategy to dramatically increase spectrum sensing performance in cognitive radio networks, but it is vulnerable to Spectrum sensing data falsification (SSDF) attacks. Existing security references have concentrated on ways to lessen the impact of a SSDF attacking, but these approaches have relied heavily on assumptions such as the attackers being a numerical minority and there being a trustworthy node for data fusion. This realisation inspires us to conduct a thorough examination of the SSDF attack and fusion centre (FC) techniques outside of these constraints. In particular, analysing complex harmful actions allows us to think about a generic SSDF attack model that is more general than the existing methods. According to this overview of attacks, the resultant condition renders the FC clear to attack. Based on these considerations, the ideal attack approach to maximise Bayes risk is examined for both the unknown and known fusion rule scenarios. Second, by casting it as a combinatorial optimisation issue, energy efficiency of cooperative sensing is discussed. In order to enhance the overall energy efficiency of system, a deep learning framework is built based on this formulation by combining graph neural network with reinforcement learning. The efficacy of this method has been demonstrated through simulation tests at varying network scales.

An Enhanced Trust-Based Secure Route Protocol for Malicious Node Detection

The protection of ad-hoc networks is becoming a severe concern because of the absence of a central authority. The intensity of the harm largely depends on the attacker’s intentions during hostile assaults. As a result, the loss of Information, power, or capacity may occur. The authors propose an Enhanced Trust-Based Secure Route Protocol (ETBSRP) using features extraction. First, the primary and secondary trust characteristics are retrieved and achieved routing using a calculation. The complete trust characteristic obtains by integrating all logical and physical trust from every node. To assure intermediate node trustworthiness, we designed an ETBSRP, and it calculates and certifies each mobile node's reputation and sends packets based on that trust. Connection, honesty, power, and capacity are the four trust characteristics used to calculate node reputation. We categorize Nodes as trustworthy or untrustworthy according to their reputation values. Fool nodes are detached from the routing pathway and cannot communicate. Then, we use the cryptographic functions to ensure more secure data transmission. Finally, we eliminate the untrustworthy nodes from the routing process, and the datagram from the origin are securely sent to the target, increasing throughput by 93.4% and minimizing delay.

An Improved Multi-Objective Particle Swarm Optimization Routing on MANET

A Mobile Ad hoc Network (MANET) is a group of low-power consumption of wireless mobile nodes that configure a wireless network without the assistance of any existing infrastructure/centralized organization. The primary aim of MANETs is to extend flexibility into the self-directed, mobile, and wireless domain, in which a cluster of autonomous nodes forms a MANET routing system. An Intrusion Detection System (IDS) is a tool that examines a network for malicious behavior/policy violations. A network monitoring system is often used to report/gather any suspicious attacks/violations. An IDS is a software program or hardware system that monitors network/security traffic for malicious attacks, sending out alerts whenever it detects malicious nodes. The impact of Dynamic Source Routing (DSR) in MANETs challenging blackhole attack is investigated in this research article. The Cluster Trust Adaptive Acknowledgement (CTAA) method is used to identify unauthorised and malfunctioning nodes in a MANET environment. MANET system is active and provides successful delivery of a data packet, which implements Kalman Filters (KF) to anticipate node trustworthiness. Furthermore, KF is used to eliminate synchronisation errors that arise during the sending and receiving data. In order to provide an energy-efficient solution and to minimize network traffic, route optimization in MANET by using Multi-Objective Particle Swarm Optimization (MOPSO) technique to determine the optimal number of clustered MANET along with energy dissipation in nodes. According to the research findings, the proposed CTAA-MPSO achieves a Packet Delivery Ratio (PDR) of 3.3%. In MANET, the PDR of CTAA-MPSO improves CTAA-PSO by 3.5% at 30% malware.

EdgeTrust: A Lightweight Data-Centric Trust Management Approach for IoT-Based Healthcare 4.0

Internet of Things (IoT) is bringing a revolution in today’s world where devices in our surroundings become smart and perform daily-life activities and operations with more precision. The architecture of IoT is heterogeneous, providing autonomy to nodes so that they can communicate with other nodes and exchange information at any time. IoT and healthcare together provide notable facilities for patient monitoring. However, one of the most critical challenges is the identification of malicious and compromised nodes. In this article, we propose a machine learning-based trust management approach for edge nodes to identify nodes with malicious behavior. The proposed mechanism utilizes knowledge and experience components of trust, where knowledge is further based on several parameters. To prevent the successful execution of good and bad-mouthing attacks, the proposed approach utilizes edge clouds, i.e., local data centers, to collect recommendations to evaluate indirect and aggregated trust. The trustworthiness of nodes is ranked between a certain limit, and only those nodes that satisfy the threshold value can participate in the network. To validate the performance of the proposed approach, we have performed extensive simulations in comparison with existing approaches. The results show the effectiveness of the proposed approach against several potential attacks.

Over-the-Air Software-Defined Vehicle Updates Using Federated Fog Environment

With recent advancements in software-defined vehicles, over-the-air (OTA) software updates are crucial to roll out new software and patches for connected vehicles. Traditionally, outdated vehicles are recalled by the manufacturers, however, owners are notoriously difficult to reach with recall notices. Also, organizational and procedural challenges result in many outdated vehicles with insecure and unstable software. In this paper, we propose a dissemination framework for OTA updates using a federated fog environment. Pushing the update to all vehicles via the fog nodes may congest the network, leading up to a single-point failure for update dissemination, as well as, disruption to other installed services at the fog nodes. We propose a software-defined mechanism to select a pivot, which gets the update from the fog node and streams it to other vehicles. Moreover, a timer-barrier mechanism is proposed to identify any malicious vehicles that are barred from participating in pivot selection. The experimental evaluation demonstrates that the proposed scheme improves network convergence time by 40% with 95% node trustworthiness.

DeTTO: Dependency-Aware Trustworthy Task Offloading in Vehicular IoT

In this paper, we investigate the dependency-aware trustworthy task offloading problem (DeTTO), especially in an IoT-enabled vehicular network, where a large computation-intensive task offloaded from a vehicle is fragmented into multiple subtasks and then offloaded to multiple trusted nodes. First, we formulate the task offloading problem as a graph optimization problem intending to find an optimal set of trustworthy nodes for offloading the subtasks. We aim to minimize the task completion delay and energy consumption, while satisfying the dependency relations between the subtasks and the trust requirements of the tasks. We consider three types of dependency structures – fully independent task, fully dependent task, and partially dependent task. For a fully independent task with no dependency between the subtasks, we propose a greedy algorithm to get the optimal set of nodes for task offloading. After showing the NP-hardness of solving the dependent task offloading problem, we propose a two-fold efficient heuristic approach for the tasks with all dependent subtasks. We adopt the solution approaches used by the first two types of tasks for a partially dependent task. Through simulation experiments, we analyze the performance of the proposed algorithms for three types of intra-task dependencies. The experimental results show that the proposed algorithms significantly reduce the delay and energy consumption, when compared to the benchmark schemes.

ReTREM: A responsibility based trust revision model for determining trustworthiness of fog nodes

Fog computing complements the Internet of Things (IoT)-Cloud paradigm by enabling local computation near IoT devices. Fog nodes support IoT applications such as health care monitoring, drone surveillance, and accident reporting. A minor breach or inaccurate results (caused when applications are placed at malicious fog nodes) can cause chaos or severe damage to users. Hence, it is of utmost vital that these applications must be processed at trustful fog nodes. In this work, we have proposed a responsibility-based trust revision model, named ReTREM, for determining and monitoring the trustworthiness of fog nodes. ReTREM categorizes fog nodes based on their response when they violate the promised service quality, i.e., their intention behind malicious behavior. A separate penalty mechanism is proposed for each category of fog node to discourage their malicious behavior. ReTREM considers two types of trust scores: TS1 and TS2. TS1 is calculated based on various metrics related to trust, such as reliability decay, data integrity, social rating, and feedback rating, while the fog broker gives TS2. Trust score (TS), i.e., the sum of TS1 and TS2, is revised based on various proposed penalty mechanisms. The proposed model rewards the fog node that does not violate the promised service. The proposed model is simulated and compared with the two variants of application placement, and it outperforms. The proposed model encourages fog nodes to provide the best service and increases the users’ faith. The proposed model also tackles various trust-based attacks resulting in fair competition and opportunity among fog nodes.

A Novel Energy Aware Clustering Mechanism with Fuzzy Logic in MANET Environment

A Mobile Ad Hoc Networks (MANETs) comprises of the vast range of devices such as sensors, smart phones, laptops and other mobile devices that connect with each other across wireless networks and collaborate in a dispersed fashion to offer network functions in the absence of a permanent infrastructure. The Cluster Head (CH) selection in a clustered MANET is still crucial for lowering each node's energy consumption and increasing the network's lifetime. However, in existing clustering mechanism trust of the all nodes are presumed those causes increased challenge in the MANET environment. Security is a crucial factor when constructing ad-hoc networks. In a MANET, energy consumption in route optimization is dependent on network resilience and connectivity. The primary objective of this study is to design a reliable clustering mechanism for MANETs that takes energy efficiency into account. For trusted energy-efficient CH in the nodes, a safe clustering strategy integrating energy-efficient and fuzzy logic based energy clustering is proposed to address security problems brought about by malicious nodes and to pick a trustworthy node as CH. To improve the problem findings Bat algorithm (BAT) is integrated with Particle Swarm Optimization (PSO). The PSO technique is inspired because it imitates the sociological characteristics of the flock of the birds through random population. The BAT is a metaheuristic algorithm inspired by microbat echolocation behavior that uses pulse average with global optimization of the average path in the network. Hybrid Particle Swarm Optimization (HPSO) and BAT techniques are applied to identify the best route between the source and destination. According to the simulation results, the suggested Fuzzy logic Particle Swarm Optimization BAT (FLPSO-BAT) technique has a minimum latency of 0.0019 milliseconds, with energy consumption value of 0.09 millijoules, maximal throughput of 0.76 bits per sec and detection rate of 90.5% without packet dropping attack.

Trust-Aware Routing Mechanism through an Edge Node for IoT-Enabled Sensor Networks.

Although IoT technology is advanced, wireless systems are prone to faults and attacks. The replaying information about routing in the case of multi-hop routing has led to the problem of identity deception among nodes. The devastating attacks against the routing protocols as well as harsh network conditions make the situation even worse. Although most of the research in the literature aim at making the IoT system more trustworthy and ensuring faultlessness, it is still a challenging task. Motivated by this, the present proposal introduces a trust-aware routing mechanism (TARM), which uses an edge node with mobility feature that can collect data from faultless nodes. The edge node works based on a trust evaluation method, which segregates the faulty and anomalous nodes from normal nodes. In TARM, a modified gray wolf optimization (GWO) is used for forming the clusters out of the deployed sensor nodes. Once the clusters are formed, each cluster’s trust values are calculated, and the edge node starts collecting data only from trustworthy nodes via the respective cluster heads. The artificial bee colony optimization algorithm executes the optimal routing path from the trustworthy nodes to the mobile edge node. The simulations show that the proposed method exhibits around a 58% hike in trustworthiness, ensuring the high security offered by the proposed trust evaluation scheme when validated with other similar approaches. It also shows a detection rate of 96.7% in detecting untrustworthy nodes. Additionally, the accuracy of the proposed method reaches 91.96%, which is recorded to be the highest among the similar latest schemes. The performance of the proposed approach has proved that it has overcome many weaknesses of previous similar techniques with low cost and mitigated complexity.

A Novel Trust Adaptability Approach for Secure Data Transmissions using Enhanced Collaborative Nodes Trustworthiness in Mobile Ad-hoc Networks

Objectives: To find an efficient security routing model based on trust adaptability by considering various transmission parameters that influence the node’s behavior. Methods: Enhanced Collaborative Trust Based Approach (ECTBA) was applied to isolate the malicious nodes from routing by computing their enhanced collaborative trust value based on the node’s behavior using transmission parameters. Parameters that influence the node’s behavior like the number of data packets and control packets forwarded, dropped, or misrouted by the node are quantified to compute direct trust value and neighbor reputation. Node’s Enhanced Collaborative trust value was generated by the combination of direct and neighbor observations. Findings: The proposed strategy is compared with several cases like the Direct Trust Based Approach (DTBA), where routing involves trustworthy nodes categorized based on only direct trust, existing methods like Belief-dependent trust evolution method(BETM), Novel extended trust-dependent method (NETM) where routing is done with nodes that are categorized as trustworthy depending on direct and indirect observations and simple AODV routing performed with all the possible random nodes without any trust detection. The performance parameters of the proposed ECTBA exhibit a success rate of 10.2% in false positives detection (FPD),network throughput of 438.12 Kbps,and packet delivery ratio (PDR) of 92.3%.This method proves to be a better method when compared with the traditional trust-based security methods(BETM and NETM) in terms of efficiency. Novelty: This research suggested a novel and fine-tuned method for quantifying a node’s trustworthiness and for secure routing that coupled the direct and indirect observations into enhanced collaborative trustbased on the node’s behavior by considering the transmission parameters. The present work highlights the combination of data packets forwarding behavior and control packets forwarding behavior in computing enhanced collaborative trust to decide the involvement of intermediate trustworthy nodes’ which is not tried before but this study does. Keywords: Dynamic Topology; Direct Trust; Neighbor Trust; Secure Routing; Enhanced Collaborative Trust

A Security Certification Scheme for Information-Centric Networks

Information-Centric Networking is an emerging alternative to host-centric networking designed for large-scale content distribution and stricter privacy requirements. Recent research on Information-Centric Networking focused on the protection of the network from attacks targeting the content delivery protocols, while assuming genuine content can always be retrieved from trustworthy nodes. In this paper, we depart from the assumption of the trustworthiness of network nodes and propose a novel certification methodology for information-centric networks that supports continuous security verification of non-functional properties. Our methodology provides a complete and detailed view of the network security status, increasing the trustworthiness of the network and its services. The proposed approach builds on an enhanced certification model capturing the evolution of the system over time. It also defines certification services that fully integrate with existing networks to collect evidence on the target of certification and carry out the certification process. It finally proposes two certification processes, centralized and decentralized, balancing the impact on the network and the system performance. Efficiency, performance, and soundness of our approach are experimentally evaluated in a simulated Named Data Networking (NDN) network targeting property availability.

A trusted effective approach for forecasting the failure of data link and intrusion in wireless sensor networks

Wireless sensor network (WSN) is often utilised in many different industries for a variety of purposes. The improved network access area and quick network setup make it an excellent technology. However, certain eradicating problems will also be present, slowing the expansion of the network. The forecasting of connection failure is still thought to be a mystery in the WSN area. The suggested method provides a thorough understanding of connection failure. This method introduces the proficient trustworthy Node ID dependent resource reservation protocol (PTN-RRP). Then, using technique weighted on end-to-end latency, the shortest path is identified. Major issues with WSN include data link failure and intrusions. This method might increase the rate of detection since it determines the shortest route from a particular preliminary point to the target. The hop address and sequence number are added to the protocol by determining the shortest path. Following the discovery of the shortest path, the likelihood of connection failure is calculated. The established trustworthy etiquette adopted has a link failure structure that can foresee and resolve connection failure-related problems. After that, a posterior probability evaluation approach is used to determine the type of assault in order to determine the attack reason intended for the connection failure. Finally, a simulation study is used to evaluate the performance of the suggested strategy. The simulation's results show that the proposed technique is extremely successful in identifying link breaks, and the developed algorithm for recognising the shortest path will shorten the time required to find the shortest way. To demonstrate the usefulness of the suggested methodology, the performance result is contrasted with currently used procedures.

Blockchain-Assisted Adaptive Reconfiguration Method for Trusted UAV Network

Due to the high mobility of nodes and the complexity of the mission environment, mission-oriented UAV networks are not only subject to frequent topology changes, but also to the risk of being compromised, hijacked and corrupted. As a result, an operating UAV network is essentially a Byzantine distributed system whose physical structure and node trustworthiness change over time. How to implement the global management of UAV networks to achieve a rational allocation of UAV network resources and reconfiguration of trusted networks is a problem worthy of in-depth study. The method proposed in this paper introduces a lightweight storage blockchain in the UAV network through two-stage consensus, firstly performing data consensus on the local state records of the nodes, then performing decision consensus on the data consensus results using algorithms such as fuzzy K-Modes clustering and global trustworthiness assessment, and finally recording the decision consensus results into a new block as the new configuration information of the UAV network. A lightweight storage blockchain-assisted trusted zone routing protocol (BC_TZRP) is designed to dynamically and adaptively build configurable trusted networks in a way that the blockchain continuously adds new blocks. Using QualNet simulation experimental software, an experimental comparison between the classical routing protocol for mobile self-organizing networks and the traditional consensus algorithm for blockchains is conducted. The results show that the approach has significant advantages in terms of packet delivery rate, routing overhead and average end-to-end delay, and can effectively improve the overall working life and fault tolerance of the UAV network.

Reputation-Based Consensus for Blockchain Technology in Smart Grid

Accurate and thorough measurement of all nodes' trustworthiness should create a safer network environment. We designed a blockchain-based, completely self-operated reputation system, R360, to enhance network security in decentralized network. R360 is a multi-factor measurement on the reputation of all nodes in the network. Specifically, a node's function, defense capability, quality of service provided, availability, malicious behavior, and resources are evaluated, providing a more accurate picture about a node's trustworthiness, which in turn should enhance the security of blockchain operations in a non-trust environment. Our design has been implemented on a single-machine, simulated setting, which demonstrates that, comparing to systems with few dimensions, R360's consensus took less time while at the same time providing better security to the system. Further security analysis shows that our design can defend common security attacks on reputation systems. Such a blockchain-based, self-operated reputation system could be used to manage smat grids for more efficient energy production and delivery.

Enhancing the Routing Security through Node Trustworthiness using Secure Trust Based Approach in Mobile Ad Hoc Networks

Mobile Ad Hoc Networks, also known as MANETs are the part of networks which utilizes the technologies like Internet of Things. Internet is filled with known as well as unknown sources which are still considered as a challenge. Considering the security challenges in the topology, infrastructure and bandwidth of these networks the below secure trust approach is proposed. Among all the existing and proposed approaches to provide the security to these networks seemed lengthy and complex. The study here focuses on the trust factor of the nodes to simulate the challenge of providing the secure transmission. The challenge is approached through calculations from the simulations based on node distribution and its trustworthiness. In order to evaluate the performance of the proposed secure trust mechanism, three scenarios are considered here. Routing between source ad destination without any trust calculation, routing with only direct trust factor and routing with proposed secure trust calculation from the node’s distributions. The goal of proposed method in this paper is about the secure routing for transmission by simulating the important factor Node trustworthiness using new trust mechanism proposed i.e. secure trust. The metrics from the simulations shows the efficient performance of the proposed method.

LAP-BFT: Lightweight Asynchronous Provable Byzantine Fault-Tolerant Consensus Mechanism for UAV Network

Mission-oriented UAV networks operate in nonsecure, complex environments with time-varying network partitioning and node trustworthiness. UAV networks are thus essentially asynchronous distributed systems with the Byzantine General problem, whose availability depends on the tolerance of progressively more erroneous nodes in the course of a mission. To address the resource-limited nature of UAV networks, this paper proposes a lightweight asynchronous provable Byzantine fault-tolerant consensus method. The consensus method reduces the communication overhead by splitting the set of local trusted state transactions and then dispersing the reliable broadcast control transmission (DRBC), introduces vector commitments to achieve multivalue Byzantine consensus (PMVBA) for identity and data in a provable manner and reduces the computational complexity, and the data stored on the chain is only the consensus result (global trustworthiness information of the drone nodes), avoiding the blockchain’s “storage inflation” problem. This makes the consensus process lighter in terms of bandwidth, computation and storage, ensuring the longevity and overall performance of the UAV network during the mission. Through QualNet simulation platform, existing practical asynchronous consensus algorithms are compared, and the proposed method performs better in terms of throughput, consensus latency and energy consumption rate.

Blockchain based optimized data storage with secure communication for Internet of Vehicles considering active, passive, and double spending attacks

In the study, we propose a double blockchain based vehicular system for optimizing data storage and securing communication in Internet of Vehicles (IoV) networks. We propose an incentive mechanism and a reputation system to reduce the selfish and distrustful behavior of IoV nodes. Moreover, we propose an encryption technique to protect the proposed system from passive and active attacks. The proposed technique is a hybrid of Rivest Shamir Adleman and Advanced Encryption Standard-256 (AES-256+RSA). We use this hybrid approach to efficiently and secretly share the users' secret keys. To address the issues of high resource consumption and communication delay, we propose a cache memory technique for miner and controller nodes. In addition, we propose an authentication mechanism to remove malicious nodes from the system. The simulation results depict that our system provides security, scalability, optimized data storage, node's trustworthiness, and transparent data sharing.

A node pairing approach to secure the Internet of Things using machine learning

Although the reception of Internet of Things (IoT) services increased efficiency and allowed companies to automate the production through remote and intelligent control, it also increases the risk of potential threats and security attacks. The attackers can compromise the security of the IoT node and join it to a botnet, manipulate sensitive data or take control of a full network. Securing the IoT nodes from security attacks remains a challenge, especially under the multiple attack models where several attacks can be launched simultaneously. The objective of this paper is to prevent a type of multiple attacks that are the foundation to manipulate sensor nodes’ data that are: clone attack, tamper attack, false data injection, node malfunctioning, and physical layer attack. A solution is proposed to secure the IoT nodes against these attacks. The proposed solution is based on a novel sensor pairing approach to evaluate the trustworthiness of IoT nodes’ sensor data using multiple machine learning models. In particular, we propose a threat model and the pairing algorithm to pair each sensor node with the sensor node located in the neighbor. We compare the performance of three machine learning models, which are decision tree, Support Vector Machine (SVM), and k-Nearest Neighbors (KNN) by using two publically available real-world datasets in terms of attack detection accuracy, training time, and testing time. Our proposed system achieved up to 100% accuracy rate and nearly twice faster training and testing time as compared to the existing solutions. We provide the memory and energy consumption analysis to show that the proposed method has lower communication and memory overhead on IoT nodes than previous solutions against IoT security attacks. Our dataset just contains the data of a pair of sensors and is reduced up to eight times smaller in size than the actual dataset.

Novel Link Establishment Communication Scheme against Selfish Attack Using Node Reward with Trust Level Evaluation Algorithm in MANET

Mobile network nodes are trustworthy nodes; the data access in transmitting and receiving the data packet is not efficient in different network procedures. The malicious nodes are available in routing path making the packet loss, in packet transmission for time instance. Since the regular mobile network must not verify every mobile node approved else illegal, subsequently the reserved level of nodes also was not considered, to injure the packet transmission procedure by link failure called connection loss. It minimizes the transmission rate and network lifetime and improves packet latency and energy usage. The proposed novel link establishment communication (NLEC) technique is used to find the dependable routing path against intruder node available in the network. This scheme selects genuine node for routing path production, by using the node reward with dependence level estimating algorithm to compute every node trust level and resource range, to disconnect higher trust level node and lower trust level node; higher trust level node is a genuine node which performs secure communication. Lower trust level node is selfish node and they are detected and ignored from path. It increases the lifespan of network and throughput and minimizes the end to end delay and energy consumption.

A hybrid trust model based on communication and social trust for vehicular social networks

Vehicular social networks are emerging hybrid networks that combine traditional vehicular networks and social networks, with two key types of nodes, that is, vehicles and drivers. Since vehicle behaviors are controlled or influenced by drivers, the trustworthiness of a vehicle node is essentially determined by its own communication behaviors and its driver’s social characteristics. Therefore, human factors should be considered in securing the communication in vehicular social networks. In this article, we propose a hybrid trust model that considers both communication trust and social trust. Within the proposed scheme, we first construct a communication trust model to quantify the trust value based on the interactions between vehicle nodes, and then develop a social trust model to measure the social trust based on the social characteristics of vehicle drivers. Based on these two trust models, we compute the combined trust assessment of a vehicle node in vehicular social networks. Extensive simulations show that the proposed hybrid trust model improves the accuracy in evaluating the trustworthiness of vehicle nodes and the efficiency of communication in vehicular social networks.