Trust-based Routing Research Articles

Malicious Nodes Detection and Avoidance Using Trust-based Routing in Critical Data Handling Wireless Sensor Network Applications

Malicious Nodes Detection and Avoidance Using Trust-based Routing in Critical Data Handling Wireless Sensor Network Applications

A Hybrid Intrusion Detection System to Mitigate Biomedical Malicious Nodes

This paper proposes an intrusion detection system to prevent malicious node attacks that may result in failure links in wireless body area networks. The system utilizes a combination of Optimized Convolutional Neural Networks and Support Vector Machine techniques to classify nodes as malicious or not, and links as failure or not. In case of detection, the system employs a trust-based routing strategy to isolate malicious nodes or failure links and ensure a secure path. Furthermore, sensitive data is encrypted using a modified RSA encryption algorithm. The experimental results demonstrate the improved network performance in terms of data rate, delay, packet delivery ratio, energy consumption, and network security, by providing effective protection against malicious node attacks and failure links. The proposed system achieves the highest classification rate and sensitivity, surpassing similar methods in all evaluation metrics.

A Comprehensive Approach with Trust-Based Routing, Energy Optimization, and Multi-Routing Protocol for Enhancing Security and Performance in Mobile Ad Hoc Network

In this paper, we propose an aggregate solution to boost the security, performance, and resilience of disaster-based comms networks. This involves trust-based routing, energy optimization, and more than one routing protocol to meet the unique requirements of the ever-changing and unpredictable environments facing disasters in this world. The evaluation brings 4.3 Mbps for the average throughput, 34 ms delay with a packet delivery ratio of 96.9%. Also, energy consumption was optimized and the average consumption of 2458 Joules makes sharing power resources an easy exercise even when you are in a disaster-prone zone. Security analysis of the experiments showed on average 2.7 vulnerabilities across different trials with a range from 2 to 5 in severity level. Therefore, robust security mechanisms guard against potential threats, a reminder that disaster situations can befall any person or community. Overall, disaster response performance was good despite the difficult conditions. Further research and development in this area will continue to refine and improve communication systems for disaster preparedness and response efforts.

TEE-AODV Trust-based Route Selection and Improving Energy Efficiency in MANET

Mobile ad hoc networks (MANETs) are the future communication infrastructure for all devices that operate without the elaborate design found in wired infrastructure networks. Constructing a routing protocol that helps MANETs to achieve (QoS). It is quite challenging to achieve energy-efficient routing, due to its limited resources and node mobility concerns, which would impact node resource stability and lead to congestion and lower user QoS. A novel Trustworthy Energy Efficient AODV protocol has been proposed for improving path selection and locating trusted stations to maximize QoS. This approach employs location data, transmission speed, and directions to estimate the energy usage and trustworthiness of distinct nodes. The proposed TEE-AODV protocol includes two new control information namely Trust Request packet and Trust Reply. (T-RREP) AODV's packet includes a neighbor list and channel trustworthiness. The number of working neighbors in every path is counted, and this information is used to efficiently choose paths. The TEE-AODV calculates a QoS supporting metric for each detected route to identify a traffic-free path at any moment and determines whether it is a trusted route. The NS-2 simulator has been used to assess the suggested approach in terms of specific characteristics, including energy consumption (EC), (PDR), end-to-end delay (), and number of dead nodes. According to the experimental results, the suggested framework enhances reliable route selection while lowering e nd-to-end latency and improving energy efficiency. The experimental findings indicate that the suggested TEE-AODV protocol achieves high energy efficiency rate in the selection of trustworthy route and minimizing the E D .

Trust based Routing – A Novel Approach for Data Security in WSN based Data Critical Applications

Wireless technology has changed the way entities communicate with one another. Wireless networks have created several opportunities in fields such as military, health care, and habitat monitoring, to name a few. However, only a few data-critical applications are built on wireless sensor networks, such as border reconnaissance, detecting infringement, and patient monitoring. These applications require the processing of a large amount of private data. Because most applications are data-sensitive, securing data transmission among wireless sensor networks is crucial. While incorporating data security, the most important requirement of wireless sensor nodes being energy optimized also need to be kept in consideration. There are various forms of assaults that are relevant in Wireless Sensor Networks (WSN). Attacks like Black Hole attacks, sink hole attacks, False data Injection attacks etc. are the most commonly seen attacks on WSNs. The common element in all these attacks is the concept of malicious / compromised node - a node which either drops / modifies the data content while forwarding it. Existing techniques for data security generally use cryptographic algorithms, but the use of cryptographic algorithms is in contrast with the energy optimization requirement of sensor nodes. An energy efficient data security scheme needs to be developed. The proposed system analyses several attacks and proposes a multi-layer data security approach to prevent change of data / dropping of data by the compromised nodes. The proposed system is a routing protocol referred as Trust Based Routing (TBR). A concept of trust value of a node is the core idea of TBR. Forwarding node is selected based on highest trust value and thus avoid malicious / compromised nodes from being involved in the routing process. The trust factor is calculated by considering the number of packets dropped, packets rejected, and the node's remaining energy. The idea of TBR is enhanced by incorporating the concept of past trust and trust of node towards a specific destination. This proposed scheme is referred as Extended Trust Based Routing (ETBR). This scheme is further enhanced by including Direct Trust, Indirect Trust and Energy Trust concepts. This scheme is referred as Consolidated Trust Estimation – Trust Based Routing (CTE-TBR). Network Simulator NS2 is used to simulate the proposed schemes. Various network factors are compared to classic Adhoc On-Demand Vector (AODV) and newly proposed schemes. The result indicates the effectiveness of the proposed data security scheme in terms of energy efficiency and Packet Delivery ratio (PDR).

T-AODV: A trust-based routing against black-hole attacks in VANETs

T-AODV: A trust-based routing against black-hole attacks in VANETs

RTRV: An RSU-assisted trust-based routing protocol for VANETs

Routing in vehicular ad hoc networks (VANETs) enables efficient communication and data exchange between vehicles and infrastructure components, which is a critical aspect of providing intelligent transportation systems (ITS). Due to the dynamic nature of VANETs, where vehicles have high mobility and frequent link disconnections, secure routing protocols are essential for establishing reliable and efficient communication paths. Employing trust criteria in routing is an effective solution that routing protocols use to become secure. In this paper, we propose an RSU-assisted trust-based routing protocol for VANETs (RTRV), which considers trust criteria to provide secure routing. To identify and limit malicious nodes from disrupting the routing, RTRV provides a more reliable monitoring process for trust management, which increases resistance to trust-based attacks. In the monitoring process, as each data packet is sent to a next-hop, two vehicles observe the behavior of the next-hop in forwarding the packet, and the observations are then used to update the direct trust of the next-hop. The observations are additionally reported to RSUs, which gather the reports to update the indirect trust of the next-hop and provide recommendations to vehicles within their transmission range. In addition to indirect trust management, in RTRV, RSUs play an active role in the routing of data packets, which improves the routing performance. We simulated the proposed protocol in different scenarios with SUMO and OMNeT++ tools and then compared it with the TGRV protocol. The results of our analysis conclusively show that RTRV outperforms TGRV in terms of packet delivery ratio, end-to-end delay, and average hop count in both scenarios, with and without RSU. Specifically, in the RTRV without RSU scenario, when the percentage of malicious vehicles reaches 25%, RTRV achieves a 3.8% higher packet delivery ratio compared to TGRV. Furthermore, RTRV demonstrates significantly reduced end-to-end delay and average hop count, with values 0.14 and 0.11 times lower than TGRV, respectively. In addition, the cost and security analysis demonstrate that the proposed protocol has acceptable overheads in memory, communication, and computation and is also resistant to critical trust-based attacks.

An Empirical Study of Destructive Nodes Characterization in Wireless Networks

Mobile Ad hoc Networks (MANETs) are deployed in various new public and domestic environments, going up to new needs in terms of concert and effectiveness. According to the wireless dynamic nature, some needed services like security for network maintenance, trust-based routing and resource management among network nodes are not carried out as good as expected. Also, the ad-hoc networks are vulnerable to secure communications and multiple attacks can participate in various layers of a network stack. Destructive nodes have chances to change or discard routing specifications, sometimes it can send false routes to capture source data packets to pass through themselves. Some protocols have been designed to address the complication from secure data communication. Even though, a secure protocol cannot handle all kinds of attack detection and elimination in all situations. New secure data communication wireless protocols need to focus these challenges, because security care is not natively built in MANET. Therefore, in this research paper, analysis of destructive nodes characterization and impacts on wireless networks investigated the multiple attacks behaviour, activities of the attacks all through neighbour selection, path establishment from source to destination, creating awareness of attack presence detection knowledge to the normal devices during path discovery and data transmission mechanisms. Legitimate nodes need to be building with secure transmission knowledge to make sure trusted communication, ensure validation, honesty, and privacy to classify the attacks in MANETs.

Performance Analysis of Routing Protocol Using Trust-Based Hybrid FCRO-AEPO Optimization Techniques

Mobile Ad hoc Networks (MANETs) offer numerous benefits and have been used in different applications. MANETs are dynamic peer-to-peer networks that use multi-hop data transfer without the need for-existing infrastructure. Due to their nature, for secure communication of mobile nodes, they need unique security requirements in MANET. In this work, a Hybrid Firefly Cyclic Rider Optimization (FCRO) algorithm is proposed for Cluster Head (CH) selection, it efficiently selects the CH and improves the network efficiency. The Ridge Regression Classification algorithm is presented in this work to sense the malicious nodes in the network and the data is transmitted using trusted Mobile nodes for the QoS enactment metric improvement. A trust-based routing protocol (TBRP) is introduced utilizing the Atom Emperor Penguin Optimization (AEPO) algorithm, it identifies the best-forwarded path to moderate the routing overhead problem in MANET. The planned method is implemented using Matlab software and the presentation metrics are packet delivers ratio, packet loss ratio (PLR), routing overhead, throughput, end-to-end delay (E2ED), transmission delay, energy consumption and network lifetime. The suggested AEPO algorithm is compared with the prevailing PSO-GA, TID-CMGR, and MFFA. The AEPO algorithm’s performance is approximately 1.5%, 3.2%, 2%, 3%, and 4% higher than the existing methods for PLR, packet delivers ratio, throughput, and E2ED and network lifetime. The sender nodes can increase their information transmission rates and reduce delays in appreciation of this evaluation. Additionally, the suggested technique has a perfect benefit in terms of demonstrating the genuine contribution of distinct nodes to trust evaluation (TE).

A Novel Nature-Inspired Routing Scheme for Improving Routing Quality of Service in Power Grid Monitoring Systems

In the process of power grid monitoring, image data are mostly transmitted in segments by wireless image sensor networks (WISNs), and the external attacks or interference on a communication link easily causes the data to be retransmitted. Therefore, high quality of service (QoS) routing is essential to the secure and robust operation of the WISNs. Specifically, the QoS routing optimization in WISN is a nondeterministic polynomial hard problem, which usually cannot be solved by ordinary algorithms. Based on this motivation, a novel heuristic trust scheme (i.e., multi-objective trust routing scheme based on cloned elite slime mould algorithm (CESMA-MTRS)) is proposed to greatly improve the security and QoS of WISN. The scheme designs a novel trust difference test mechanism to enhance the identification of malicious nodes. Furthermore, a new elite archiving strategy and clone optimization strategy are designed to improve the convergence speed of grid monitoring data transmission routing search. The simulation results show that when the network scale is 100 and the attacked node scale is 30%, CESMA-MTRS reduces the delay by 8% and 15% and the packet loss rate by 24% and 27%, respectively, compared with trust-based routing protocol called the On-demand trust-based multicast routing protocol (ODTMRP) and evolutionary selfcooperative trust approach.

Trust-based co-operative routing for secure communication in mobile ad hoc networks

The working of a Mobile Ad hoc NETwork (MANET) relies on the supportive cooperation among the network nodes. But due to its intrinsic features, a misbehaving node can easily lead to a routing disorder. This paper presents two trust-based routing schemes, namely Trust-based Self-Detection Routing (TSDR) and Trust-based Co-operative Routing (TCOR) designed with an Ad hoc On-demand Distance Vector (AODV) protocol. The proposed work covers a wide range of security challenges, including malicious node identification and prevention, accurate trust quantification, secure trust data sharing, and trusted route maintenance. This brings a prominent solution for mitigating misbehaving nodes and establishing efficient communication in MANET. It is empirically validated based on a performance comparison with the current Evolutionary Self-Cooperative Trust (ESCT) scheme, Generalized Trust Model (GTM), and the conventional AODV protocol. The extensive simulations are conducted against three different varying network scenarios. The results affirm the improved values of eight popular performance metrics overcoming the existing routing schemes. Among the two proposed works, TCOR is more suitable for highly scalable networks; TSDR suits, however, the MANET application better with its small size. This work thus makes a significant contribution to the research community, in contrast to many previous works focusing solely on specific security aspects, and results in a trade-off in the expected values of evaluation parameters and asserts their efficiency.

A selfish node trust aware with Optimized Clustering for reliable routing protocol in Manet

Reliable routing in MANET is a pivotal element in real-world scenarios. The former works introduced a novel routing protocol namely, Trustable Energy-based Clustering and Optimized Routing (TECROP). However, this failed to concentrate on the selfish nodes. The proposed research focuses on the progress of a routing protocol that could handle the presence of selfish nodes. The proposed Selfish Node aware Trustable and Optimized Clustering-based Routing (SN-TOCRP) follows hierarchical clustering for creating node clusters. The selection of cluster Heads (CH) is done through the nature-inspired Fuzzy based Crow Search Algorithm. Selfish node detection is done by using introducing the authentication method which will authenticate the cluster heads. Bandwidth-aware Trust-based Routing Protocol (BTRP) is deployed to detect as well as isolate both misbehaving as well as faulty nodes. This also helps in diagnosing common misbehaviour attacks like a wormhole, Sybil, and selfishness with better results. Trust estimation and trust-based routing will be based on Modified Glow swarm optimization. The detailed experimental analysis done on the NS2 simulator proves that the proposed work achieves improved results with Packet delivery ratio of 96%, loss ratio of 0.045%, average delay of 0.325 ms with throughput is 76 Kbps, end to end delay of 0.425 ms and of energy consumption of 88 mJ.

Certrust: An SDN-Based Framework for the Trust of Certificates against Crossfire Attacks in IoT Scenarios

The low-intensity attack flows used by Crossfire attacks are hard to distinguish from legitimate flows. Traditional methods to identify the malicious flows in Crossfire attacks are rerouting, which is based on statistics. In these existing mechanisms, the identification of malicious flows depends on the IP address. However, the IP address is easy to be changed by attacks. Compared with the IP address, the certificate is more challenging to be tampered with or forged. Moreover, the traffic trend in the network is towards encryption. The certificates are popularly utilized by IoT devices for authentication in encryption protocols. DTLShps proposed a new way to verify certificates for resource-constrained IoT devices by using the SDN controller. Based on DTLShps, the SDN controller can collect statistics on certificates. In this paper, we propose Certrust, a framework based on the trust of certificates, to mitigate the Crossfire attack by using SDN for IoT. Our goal is threefold. First, the trust model is built based on the Bayesian trust system with the statistics on the participation of certificates in each Crossfire attack. Moreover, the forgetting curve is utilized instead of the traditional decay method in the Bayesian trust system for achieving a moderate decay rate. Second, for detecting the Crossfire attack accurately, a method based on graph connectivity is proposed. Third, several trust-based routing principles are proposed to mitigate the Crossfire attack. These principles can also encourage users to use certificates in communication. The performance evaluation shows that Certrust is more effective in mitigating the Crossfire attack than the traditional rerouting schemes. Moreover, our trust model has a more appropriate decay rate than the traditional methods.

Toward Attack-Resistant Route Mutation for VANETs: An Online and Adaptive Multiagent Reinforcement Learning Approach

Vehicular Ad hoc Networks (VANETs) are prone to packet drop attacks because of their inherent distributed architecture and dynamic topology. Existing security schemes mainly focus on multi-path and trust-based routing. Unfortunately, the former causes high energy consumption and the latter requires trust assessment, which is not easy to implement in practice. Route mutation (RM) is emerging as an active defense technology that changes routes periodically. Traditional RM is conceived for fixed network topologies, and needs a centralized controller, so that it cannot be applied to VANETs. Therefore, the present contribution investigates RM in VANETs by proposing a Grid-based extended Joint Action Learning approach (Grid-eJAL). To the best of our knowledge, this is the first contribution that designs an online and adaptive multi-agent reinforcement learning (MARL) for RM to mitigate attacks in VANETs. Differently from existing MARL schemes, Grid-eJAL allows vehicles to share parameters to accelerate the convergence speed of learning. In Grid-eJAL, the area of interest is split in equally sized grids and, when a vehicle transmits packets, the next hop with the minimum angle of mobility is selected within the grid considered as optimal by the learning policy. The convergence of Grid-eJAL is proved theoretically. Finally, extensive simulation results highlight the effectiveness of Grid-eJAL compared to representative state-of-the-art solutions.

AODV (ST_AODV) on MANETs with Path Security and Trust-based Routing

The nodes of the MANET are connected by an autonomous that has no predetermined structure (Mobile ad hoc Network). When a node's proximity to other nodes is maintained dynamically via the use of relying nodes, the MANET network's node-to-node connection is un-trusted because of node mobility. If a node relies on self-resources at any point in time, it runs the risk of acting as a selfish or malicious node, the untrusted selfish or malicious node in the network. An end-to-end routing route that is secure has been presented to enhance the security of the path based on the AODV routing protocol using ST AODV (Secure and Trust ADV). To do this, we must first identify the selfish/malicious nodes in the network and analyse their past activity to determine their current trust levels. A node's stage of belief is indicated by the packet messages it sends. In order to resolve each route, trust must be identified and the path's metadata in RREP must be updated.

Planning for Automated Vehicles with Human Trust

Recent work has considered personalized route planning based on user profiles, but none of it accounts for human trust. We argue that human trust is an important factor to consider when planning routes for automated vehicles. This article presents a trust-based route-planning approach for automated vehicles. We formalize the human-vehicle interaction as a partially observable Markov decision process (POMDP) and model trust as a partially observable state variable of the POMDP, representing the human’s hidden mental state. We build data-driven models of human trust dynamics and takeover decisions, which are incorporated in the POMDP framework, using data collected from an online user study with 100 participants on the Amazon Mechanical Turk platform. We compute optimal routes for automated vehicles by solving optimal policies in the POMDP planning and evaluate the resulting routes via human subject experiments with 22 participants on a driving simulator. The experimental results show that participants taking the trust-based route generally reported more positive responses in the after-driving survey than those taking the baseline (trust-free) route. In addition, we analyze the trade-offs between multiple planning objectives (e.g., trust, distance, energy consumption) via multi-objective optimization of the POMDP. We also identify a set of open issues and implications for real-world deployment of the proposed approach in automated vehicles.

An enhanced encryption-based security framework in the CPS Cloud

The rapid advancement of computation techniques and cloud computing has led to substantial advancements in Cyber-Physical Systems (CPS), particularly in the field of health care. There are a variety of ways in which CPS is used in healthcare today, including delivering intelligent feedback systems, automatically updating patient data digitally, monitoring patients passively with biosensors, etc. In recent years, cyber-physical systems have become capable of making lifesaving decisions as they are becoming more connected to the cloud. However, healthcare has become one of the most critical issues for many. A CPS network uses the Internet of Medical Things (IoMT) to continuously monitor patients’ health metrics such as body temperature, heart rate, etc. Due to physical connectivity restrictions, networks are more susceptible to security threats. In spite of the fact that the data is stored in the cloud, it is necessary to provide security regardless of device security and network security. Several cyber-security vulnerabilities have been identified in cloud-based healthcare systems in particular. To give patients a reliable healthcare experience, security concerns with CPSs need to be addressed carefully. In this context, this paper proposes a Cross-Breed Blowfish and MD5 (CBM) approach to improve the security of health data in the CPS cloud. The proposed model uses the wireless sensor network, in which data acquired by the network is transmitted via the transmitting node. Using the fuzzified effective trust-based routing protocol (FET-RP), the most efficient path for data travel is selected. The best route is determined using Butter-Ant Optimization (BAO) algorithm. The proposed method conveys data throughput encryption and decryption in a decoded format. The encrypted data is then stored in the cloud database for security reasons. The route finding algorithm is the one which is sending the data from one end to other end. The data is encrypted based on the source and destination. We compare the performance metrics of our recommended technique to those of other existing techniques, such as RSA, Two fish, ICC, and FHEA, in order to ensure that it performs optimally. The values of Cross Breed Blowfish and MD5 and FET-RP with regard to the performance metrics in terms of encryption (60 ms), decryption (55 ms), latency (60 s), throughput (97 mbps), security level (98%), and execution time (57 ms) which outperforms the conventional methods by 10–15%. Also the proposed encryption shows the considerable improvement in the level of security making our model a real world solution.

E2DA: Energy Efficient Data Aggregation and End-to-End Security in 3D Reconfigurable WSN

This paper deals with energy consumption and security limitations in the reconfigurable WSN’s in order to improve the network lifetime with end-to-end data privacy. The network consists set of nodes placed in distributed environment such that each node performs a reconfiguration task to support users requirements. We proposed novel solutions that improved network lifetime through efficient reconfigurable routing and less network traffic. We proposed an in-network task to eliminate duplicate packets at the node level using hashing distance computation (HDC). Further, we introduced power-efficient reconfigurable cell-by-cell golden sector-based emperor penguin colony (CbC GSEPC) for trust-based routing. In terms of data confidentiality in the energy-constrained environment, a lightweight key expandable cryptography method was proposed for end-to-end confidentiality. Additionally, a reading-based dual validation (RbDV) audits the information at sink level for intrusion detection and isolates suspicion nodes. The proposed and existing works simulated with NS-3.26 and the results show that the proposed work’s average energy consumption is 5.82% lower than the existing 2D-WSN while offering end-to-end confidentiality and node reconfiguration opportunity.

Trust-Based Intelligent Routing Protocol with Q-Learning for Mission-Critical Wireless Sensor Networks.

Mission-critical wireless sensor networks require a trustworthy and punctual routing protocol to ensure the worst-case end-to-end delay and reliability when transmitting mission-critical data collected by various sensors to gateways. In particular, the trustworthiness of mission-critical data must be guaranteed for decision-making and secure communications. However, it is a challenging issue to meet the requirement of both reliability and QoS in sensor networking environments where cyber-attacks may frequently occur and a lot of mission-critical data is generated. This study proposes a trust-based routing protocol that learns the trust elements using Q-learning to detect various attacks and ensure network performance. The proposed mechanism ensures the prompt detection of cyber threats that may occur in a mission-critical wireless sensor network and guarantees the trustworthy transfer of mission-critical sensor data. This paper introduces a distributed transmission technology that prioritizes the trustworthiness of mission-critical data through Q-learning results considering trustworthiness, QoS, and energy factors. It is a technology suitable for mission-critical wireless sensor network operational environments and can reliably operate resource-constrained devices. We implemented and performed a comprehensive evaluation of our scheme using the OPNET simulator. In addition, we measured packet delivery rates, throughput, survivability, and delay considering the characteristics of mission-critical sensor networks. The simulation results show an enhanced performance when compared with other mechanisms.

Security-driven prioritization for tactical mobile networks

Public safety and security authorities utilize tactical mobile networks to enable communications in situations where fixed infrastructure is unavailable. While these rapidly deployable networks rely on open standards, and support emerging applications and cross-organization federation, they are also vulnerable for cyber and insider threats. Active cybersecurity and threat detection solutions must work in isolated tactical bubbles without human interactions and cannot assume connectivity to remote security operations centers. This study explores the concept of a tactical security operations center (T-SOC) and presents an approach to adapting mobile network applications based on security monitoring and analytics. The security-driven prioritization of traffic flows demonstrates, first, a security decision making that is based on traffic analysis and security posture assessment and, second, an intelligent security response to availability threats by dynamically adjusting quality parameters of live video streams. We describe field trialed implementations based on open-source components and measure and analyze trade-offs between edge and cloud-based deployments. We also discuss other potential reaction strategies, such as trust-based routing, and present directions for future research.