Malicious Node Detection Research Articles

Secure Low-Energy Routing Protocol Based on Dynamic Trust Awareness and Load Balancing in Wireless Sensor Networks

Since wireless sensor networks (WSNs) have the requirements of high security and energy conservation, a distributed secure low-energy routing protocol (SLERP) based on dynamic trust awareness and load balancing is proposed. In order to reduce the adverse influence of malicious nodes in the network, the Chebyshev neural network is used to predict the dynamic trust degree of network nodes to accelerate the speed and accuracy of malicious node detection. Based on the comprehensive consideration of the average dynamic trust degree of cluster-head nodes, the load of the cluster-head node, network energy consumption, network lifetime, and accurate route evaluation model are established by the analytic hierarchy process (AHP). The search methods of secure low-energy routing and chromosome crossing and the mutation method are designed based on the genetic algorithm (GA), so as to quickly establish the optimal cluster-head node-set and the optimal routing path of each node. Simulation results show that SLERP can significantly improve the detection speed and detection success rate of malicious nodes, reduce the network energy consumption and load, and effectively extend the network lifetime.

An Efficient and Secure Fog Based Routing Mechanism in IoT Network

The Internet of Things (IoT) networks are the most prone to internal as well as external attacks in addition to energy consumption problems. Conventional security solutions are not able to address these issues effectively due to the limited resources of sensor nodes participating in IoT communications. In this work, an Efficient and Secure Fog Based Routing Mechanism (ESFRM) is proposed to protect the network from faulty internal as well as external attacks. Every node participating in IoT communications calculates the comprehensive trust value of the next intermediate node which is the addition of direct trust, indirect trust and energy trust values before forwarding the data. This comprehensive trust value is then compared with the comprehensive threshold trust value to decide whether the particular node is a rogue node or a valid normal node. Further, an enhanced RSA (Rivest, Shamir, Adleman) algorithm is implemented to provide three levels of data security from Cluster Head (CH) to fog node, from fog node to cloud server and directly from CH to cloud server. For this purpose, an efficient CH selection technique is also proposed in this work. The proposed methodology is compared with the Secure Energy-efficient Fog-based Routing (SEFR) protocol and Trust-aware Secure Routing Protocol (TSRP). The evaluation results show that the proposed ESFRM outperforms the conventional schemes with respect to energy consumption, malicious node detection and transmission rate.

Election of MPR Nodes and Detection of Malicious Nodes Based on a Byzantine Fault in the OLSR Protocol Case of a Scale-Free Network

V2X (Vehicle-to-Everything) communications play a crucial role in enabling the efficient and reliable exchange of information among vehicles, infrastructure, and other entities in smart transportation systems. However, the inherent vulnerabilities and dynamic nature of V2X networks present significant challenges for ensuring secure and trustworthy communication. By enhancing the security of the OLSR (Optimized Link State Routing) protocol through secure MultiPoint Relays (MPRs) Selection, this research aims to provide a robust approach that enhances the overall security posture of V2X networks, enabling safe and secure interactions between vehicles and their environment. The proposed method is based on the Byzantine general’s problem, which is the principle used in blockchain. Compared to the classical flooding mechanism, this technique greatly reduces network traffic overhead and improves the efficiency of bandwidth utilization. The results demonstrated that the proposed algorithm performed better than the well-used UM-OLSR implementation. The outcome proved that our MPR election algorithm guarantees a better packet delivery ratio, and it also performs very well in the detection and isolation of malicious nodes, leading to increased security of the OLSR protocol control plane.

Enhancing Performance and Security in the Metaverse: Latency Reduction Using Trust and Reputation Management

In the rapidly evolving landscape of distributed systems, security stands as a significant challenge, especially in the face of network node attacks. Such threats introduce profound complexities into the dynamics of security protocols, trust management, and resource allocation, issues further amplified by the metaverse’s exponential growth. This paper proposes an innovative solution, offering unique technical contributions to address these multi-faceted challenges. We unveil a trust-based resource allocation framework designed to facilitate the secure and efficient sharing of computational resources within the metaverse. This system has the potential to markedly diminish latency, thereby enhancing overall performance. In parallel, we introduce a reputation system that systematically monitors latency across a spectrum of metaverse entities, providing valuable insights for making informed resource allocation decisions. Moreover, we advocate for a decentralized trust management system, specifically designed to withstand potential security breaches without reliance on a centralized authority. This significantly fortifies both system security and user trust. Alongside this, we unveil an inventive proof-of-trust consensus mechanism that fosters trust and collaboration among metaverse entities during resource allocation, thereby cultivating a more secure ecosystem. Our proposed model poses a robust challenge to malicious entities, and it substantially bolsters the security architecture. The simulation results lend substantial credence to the effectiveness of our approach, demonstrating significant improvements in latency reduction, scalability, and the detection of malicious nodes, thereby outperforming existing methodologies.

Novel Graph-Based Machine Learning Technique to Secure Smart Vehicles in Intelligent Transportation Systems

Intelligent Transport Systems (ITS) is a developing technology that will significantly alter the driving experience. In such systems, smart vehicles and Road-Side Units (RSUs) communicate through the VANET. Safety apps use these data to identify and prevent hazardous situations in real-time. Detection of malicious nodes and attack traffic in Intelligent Transportation Systems (ITS) is a current research subject. Recently, researchers are proposing graph-based machine learning techniques to identify malicious users in the ITS environment, through which it is easy to analyze the network traffic and detect the malicious devices. Therefore, graph-based machine learning techniques could be a technique that efficiently detect malicious nodes in the ITS environment. In this context, this article aims to provide a technique for resolving authentication and security issues in ITS using lightweight cryptography and graph-based machine learning. Our solution uses the concepts of identity based authentication technique and graph-based machine learning in order to provide authentication and security to the smart vehicle in ITS. By authenticating smart vehicles in ITS and identifying various cyber threats, our proposed method substantially contributes to the development of intelligent transportation communication environment.

Generative Adversarial Learning for Trusted and Secure Clustering in Industrial Wireless Sensor Networks

Traditional machine learning techniques have been widely used to establish the trust management systems. However, the scale of training dataset can significantly affect the security performances of the systems, while it is a great challenge to detect malicious nodes due to the absence of labeled data regarding novel attacks. To address this issue, this paper presents a generative adversarial network (GAN) based trust management mechanism for Industrial Wireless Sensor Networks (IWSNs). First, type-2 fuzzy logic is adopted to evaluate the reputation of sensor nodes while alleviating the uncertainty problem. Then, trust vectors are collected to train a GAN-based codec structure, which is used for further malicious node detection. Moreover, to avoid normal nodes being isolated from the network permanently due to error detections, a GAN-based trust redemption model is constructed to enhance the resilience of trust management. Based on the latest detection results, a trust model update method is developed to adapt to the dynamic industrial environment. The proposed trust management mechanism is finally applied to secure clustering for reliable and real-time data transmission, and simulation results show that it achieves a high detection rate up to 96%, as well as a low false positive rate below 8%.

Watchdog malicious node detection and isolation using deep learning for secured communication in MANET

Mobile Ad-hoc Networks (MANETs) are wireless networks formed dynamically by connecting or leaving nodes to and from the network without any fixed infrastructure. These categories of wireless networks are susceptible to different attacks based on their dynamic topological structure. Due to this, security is a primary constraint in MANETs to preserve communication between mobile nodes. A Deep Neural Learned Projective Pursuit Regression-based Watchdog Malicious Node Detection and Isolation (DNLPPR-WMNDI) technique is proposed and modelled in this paper to improve the security feature of MANETs. The newly proposed DNLPPR-WMNDI technique initially selects the neighbouring nodes by applying the projection pursuit regression function. In multicasting, the route paths are established through the intermediate node with the help of control commands named RREQ and RREP. After then, Watchdog Malicious Node Detection and Isolation (WMNDI) technique is applied to detect malicious nodes based on the data packet forwarding time. Basically, a malicious node is affected by a node isolation attack. For better communication, a malicious node is isolated from the network and multicast routing is carried out by selecting the next neighbouring node and this improves the communication security. Simulation is done for the developed technique based on different performance metrics.

An efficient trust-based decision-making approach for WSNs: Machine learning oriented approach

Wireless Sensor Networks (WSNs) are often used for critical applications where trust and security are of paramount importance. Trust evaluation is one of the key mechanisms to ensure the security and reliability of WSNs. Traditional trust evaluation schemes rely on fixed, predetermined thresholds, or rules and static attack models, which may not be suitable for all situations such as dynamic and heterogeneous network environments with new and unknown attack scenarios as well as have several problems such as limited security and scalability, limited accuracy, incomplete coverage, lack of adaptability that can limit their effectiveness. Machine Learning (ML) has been shown to be an effective tool for trust evaluation in WSNs, offering several benefits over existing schemes such as greater adaptability, scalability, and accuracy since ML algorithms can analyze and learn from the data collected in real-time from multiple sources (sensor readings, network traffic, and user behavior) enabling them to dynamically adjust their decision-making criteria based on the current network conditions. Trust-aware ML-based security mechanisms achieve safety and efficient decision-making by reducing uncertainty and risk to accomplish real-world tasks. This paper presents a Machine Learning (ML)-based trust evaluation model in the unattended autonomous WSN environment to achieve reliability, adaptability, scalability, and accuracy by generating quick and reliable trust values dynamically. The proposed machine learning algorithm extracts various trust features such as Co-Location Relationship (CLR), Co-Work Relationship (CWR), Cooperativeness-Frequency-Duration (CFD), and Reward (R) to obtain a robust trust rating of sensor devices and predict future misbehavior. These trust features are combined to generate a final trust rating before making any decision about the reliability of any sensor device. Moreover, the projected trust model (ETDMA) integrate direct communication trust and indirect trust with the help of a logical time window that periodically records the trustworthy and suspicious interactions. Simulation experiments exhibit the effectiveness of the proposed trust evaluation method in terms of change in trust values, malicious nodes detection (94%), FNR (0.9%), F1-Score (0.6), and accuracy (92%) in the presence of 50 malicious nodes.

Chained-Drones: Blockchain-based privacy-preserving framework for secure and intelligent service provisioning in Internet of Drone Things

We leverage blockchain technology for malicious node detection in Internet of Drone Things (IoDTs). IoDTs additionally provide a secure mechanism of service delivery based on cascade encryption and feature assessment. By making localization and service delivery more challenging, malicious nodes deter new users. The identification and removal of such nodes is necessary for establishing trust between entities. Using federated learning, the proposed system identifies malware. Using SVM and RF classifiers, federated learning is capable of identifying malicious nodes. For the purpose of identifying malicious nodes, we examine their transparency and latency. Nodes that provide services to others provide one another incentives. During service delivery in IoDTs, the challenges that must be addressed are provider and consumer mistrust. These concerns are addressed via feature assessment and cascade encryption. The digital signature used in cascade encryption renders the service provider irrefutable. Due to feature evaluation, consumers get their requested services without fail. Functional testing ensures that a service fulfills industry requirements. Our simulation results demonstrate that our non-spurning model works as designed. Accuracy, recall, and overall F1 score are compared between SVM and RF classifiers. The accuracy of SVM is 79%, its F1 score is 0.88, and its recall is 0.78. The RF classifier has an accuracy rate of 95%, a precision rate of 0.92, an F1 score of 0.96, and a recall rate of 1. Utilizing RF significantly improves the precision of identifying malicious nodes.

Regional autonomous security cooperative spectrum sensing method based on trust value

AbstractIn order to solve the problems of spectrum resource shortage, low spectrum utilization and shadow effect, a regional autonomous security cooperative spectrum sensing (CSS) method based on trust value is proposed to address the vulnerability of CSS technology in cognitive radio to malicious node attacks. Considering the difference in node position, the vulnerability of CSS to malicious node attacks, the large amount of centralized sensing data processing, and the large energy consumption of distributed sensing, based on the trust value update mechanism of ‘slow growth and fast recovery’, the perception area is divided and managed, and the final perception result is determined by majority voting based on the centralized sensing. The convergence value of the time difference of the perceptual process trust value is used to detect the anomaly of the node, and the cumulative difference in trust values between the nodes can detect and locate the malicious node. Experimental simulation results show that the proposed model is superior to traditional algorithms in spectral perception accuracy and malicious node detection.

Realization of a Robust Fog-Based Green VANET Infrastructure

This article proposes an efficient employment of a self-powered fog-based vehicular ad hoc network (VANET) infrastructure. Miscellaneous techniques and algorithms are suggested to help the realization of such framework. In the current work, we decided to enhance the network architecture of the traditional VANET by adopting the concept of self-powered fog computing concepts for better networking, computing, and storage performance. The green fog layer consists of three components: a self-powered edge server, wireless solar routers, and a new device resulted from the integration between a solar-powered smart camera and a solar-powered road side unit in order to create a better sensing mechanism of the road traffic. A proper power management strategy is suggested to be installed locally in the self-powered devices to decrease their power utilization and lengthen the lifetime of their batteries. On the system level, the design steps concentrate on building a sustainable, secured, reliable, and scalable communication infrastructure and this was done by adopting several approaches, such as VANET fog clustering, malicious nodes detection, and a combination of various security methods. The performance of the different methods and algorithms suggested in this article is evaluated using different simulation and experimental tools to discover their impact on enhancing the robustness of the fog-based Green VANET.

Hybrid Fuzzy Rule Algorithm and Trust Planning Mechanism for Robust Trust Management in IoT-Embedded Systems Integration

The Internet of Things (IoT) is rapidly expanding and becoming an integral part of daily life, increasing the potential for security threats such as malware or cyberattacks. Many embedded systems (ESs), responsible for handling sensitive data or facilitating secure online activities, must adhere to stringent security standards. For instance, payment processors employ security-critical components as distinct chips, maintaining physical separation from other network components to prevent the leakage of sensitive information such as cryptographic keys. Establishing a trusted environment in IoT and ESs, where interactions are based on the trust model of communication nodes, is a viable approach to enhance security in IoT and ESs. Although trust management (TM) has been extensively studied in distributed networks, IoT, and ESs, significant challenges remain for real-world implementation. In response, we propose a hybrid fuzzy rule algorithm (FRA) and trust planning mechanism (TPM), denoted FRA + TPM, for effective trust management and to bolster IoT and ESs reliability. The proposed system was evaluated against several conventional methods, yielding promising results: trust prediction accuracy (99%), energy consumption (53%), malicious node detection (98%), computation time (61 s), latency (1.7 ms), and throughput (9 Mbps).

A trust management-based secure routing protocol with AUV-aided path repairing for Underwater Acoustic Sensor Networks

Underwater Acoustic Sensor Networks (UASNs) are widely applied in marine based applications, but traditional security mechanisms are not feasible due to the underwater sensor nodes’ limited constraints and capabilities. Moreover, few existing routing protocols for underwater sensor networks considered underwater transmision limited constraints. In this paper, we propose T-SAPR, a trust management-based secure routing protocol with AUV-aided path repairing that addresses these challenges. T-SAPR uses an attention-based long short-term memory (LSTM) to build a trust model that considers node trust, communication trust, and environment surrounding’s trust. T-SAPR evaluates trust evidence to measure the trust level of the sensor nodes in the network and uses energy, packet delivery ratio, and latency jointly to seek optimal routing policies in the reward function of Q-Learning. Additionally, we propose a repairing mechanism with the assistance of AUVs to improve the reliability of UASNs in cases of multiple sensor node failures or the detection of malicious nodes. Evaluation results show that T-SAPR identifies malicious nodes by detecting their anomalous behavior while improving the packet delivery ratio and energy efficiency by 13.3% and 48.1% as compared to existing algorithms such as GEDAR, TADR-EAODV, and AFSA-ACOA-SCP.

Networking and Data Communication

The method may efficiently find rogue nodes by inspecting network traffic in real time. Our system is capable of creating a dynamic blacklist of Network nodes that may be used to block nodes whose trust levels are below a certain minimum. The detection of malicious nodes and the computation of node trust levels are handled by a central server. To increase the network and infrastructure's security, the method may be used in conjunction with other security tools including database activity monitoring, white listing, and data loss prevention. Session based encrypt is used to secure the node from the compromised attacker. Attacker request will be accepted by unique key of individual node validation process. Intruders wouldn't be able to steal TOP-SECRET data or information if this were done.. [1]

WSN Network Node Malicious Intrusion Detection Method Based on Reputation Score

Wireless sensor network (WSN) is the Internet of Things by a large number of sensors in the external physical environment to obtain data information, and use wireless communication technology to provide users with information transmission services. At this stage, communication and security mechanisms are the main problems faced by WSN. This is because most of the existing sensors are powered by batteries with very limited energy, and most of them are deployed in an outdoor open environment, which is easy to be captured as a malicious node. Network attacks. However, the existing malicious node detection methods have shortcomings such as low efficiency, high energy consumption, and insufficient performance. Therefore, this paper proposes a WSN malicious node intrusion detection method based on genetic algorithm optimization of LEACH hierarchical routing protocol. Based on the optimization of the LEACH protocol by genetic algorithm, the method integrates the reputation evaluation mechanism, and screens and eliminates malicious nodes by calculating direct reputation, indirect reputation and comprehensive reputation, thereby ensuring the safe operation of WSN. The simulation results show that this method can effectively resist the attack of malicious nodes on WSN, and has obvious advantages over other methods.

Design a Secure Routing and Monitoring Framework Based on Hybrid Optimization for IoT-Based Wireless Sensor Networks

Wireless Sensor Networks (WSNs) have employed in recent years for many different applications and functions. But, it has the critical task to detect the malicious node because node malicious attacks are dangerous attacks, and the concept of a malicious attack is opponents enter the network, search accidentally, and capture one or more normal nodes. A lot of research developed to overcome this problem, but no precise results are found. In this paper, design a Hybrid Vulture and African Buffalo with Node Identity Verification (HVAB-NIV) model to predict the malicious nodes in the WSN. The fitness functions of the HVAB-NIV have operated to recognize the energy level of each node and improve the performance of node detection. The developed replica includes three stages that monitor each node, calculate the energy level and detect the malicious node. More than 100 node inputs were initialized in the proposed technique and implemented in the MATLAB tool. The suggested mechanism enhances the performance of malicious node detection and gains good accuracy for detecting nodes also, it saves running time and power consumption. The experimental results of the developed model has validated with other existing replicas to running time, False Prediction Rate (FPR), detection accuracy, True Prediction Rate (TPR), and power consumption. The developed methods achieve better results by gaining a high rate of accuracy detection, less running time, and false rate detection.

An Adaptive Real-Time Malicious Node Detection Framework Using Machine Learning in Vehicular Ad-Hoc Networks (VANETs)

Modern vehicle communication development is a continuous process in which cutting-edge security systems are required. Security is a main problem in the Vehicular Ad Hoc Network (VANET). Malicious node detection is one of the critical issues found in the VANET environment, with the ability to communicate and enhance the mechanism to enlarge the field. The vehicles are attacked by malicious nodes, especially DDoS attack detection. Several solutions are presented to overcome the issue, but none are solved in a real-time scenario using machine learning. During DDoS attacks, multiple vehicles are used in the attack as a flood on the targeted vehicle, so communication packets are not received, and replies to requests do not correspond in this regard. In this research, we selected the problem of malicious node detection and proposed a real-time malicious node detection system using machine learning. We proposed a distributed multi-layer classifier and evaluated the results using OMNET++ and SUMO with machine learning classification using GBT, LR, MLPC, RF, and SVM models. The group of normal vehicles and attacking vehicles dataset is considered to apply the proposed model. The simulation results effectively enhance the attack classification with an accuracy of 99%. Under LR and SVM, the system achieved 94 and 97%, respectively. The RF and GBT achieved better performance with 98% and 97% accuracy values, respectively. Since we have adopted Amazon Web Services, the network's performance has improved because training and testing time do not increase when we include more nodes in the network.

A systematic study on the challenges, characteristics and security issues in vehicular networks

PurposeThe purpose of this study is to extensively explore the vehicular network paradigm, challenges faced by them and provide a reasonable solution for securing these vulnerable networks. Vehicle-to-everything (V2X) communication has brought the long-anticipated goal of safe, convenient and sustainable transportation closer to reality. The connected vehicle (CV) paradigm is critical to the intelligent transportation systems vision. It imagines a society free of a troublesome transportation system burdened by gridlock, fatal accidents and a polluted environment. The authors cannot overstate the importance of CVs in solving long-standing mobility issues and making travel safer and more convenient. It is high time to explore vehicular networks in detail to suggest solutions to the challenges encountered by these highly dynamic networks.Design/methodology/approachThis paper compiles research on various V2X topics, from a comprehensive overview of V2X networks to their unique characteristics and challenges. In doing so, the authors identify multiple issues encountered by V2X communication networks due to their open communication nature and high mobility, especially from a security perspective. Thus, this paper proposes a trust-based model to secure vehicular networks. The proposed approach uses the communicating nodes’ behavior to establish trustworthy relationships. The proposed model only allows trusted nodes to communicate among themselves while isolating malicious nodes to achieve secure communication.FindingsDespite the benefits offered by V2X networks, they have associated challenges. As the number of CVs on the roads increase, so does the attack surface. Connected cars provide numerous safety-critical applications that, if compromised, can result in fatal consequences. While cryptographic mechanisms effectively prevent external attacks, various studies propose trust-based models to complement cryptographic solutions for dealing with internal attacks. While numerous trust-based models have been proposed, there is room for improvement in malicious node detection and complexity. Optimizing the number of nodes considered in trust calculation can reduce the complexity of state-of-the-art solutions. The theoretical analysis of the proposed model exhibits an improvement in trust calculation, better malicious node detection and fewer computations.Originality/valueThe proposed model is the first to add another dimension to trust calculation by incorporating opinions about recommender nodes. The added dimension improves the trust calculation resulting in better performance in thwarting attacks and enhancing security while also reducing the trust calculation complexity.

Trust aware ad hoc routing protocol with key management based mechanism and optimal energy‐efficient cluster head selection in mobile ad hoc networks

SummaryThe implementation of Mobile Ad hoc Networks in numerous domains is necessitated by the fast evolving wireless service requirements as well as deployment demands over the last few decades. The application areas are environmental monitoring, disaster rescue operations, military communications, and other safety‐critical sectors. The underlying routing protocol has a considerable impact on the effectiveness of an ad hoc network deployment in a specific situation. MANETs are vulnerable to major security risks that are difficult to combat with current security measures. As a result, several safe routing protocols have been created to improve MANET security. This work proposes a safe and energy‐efficient routing mechanism using group key management. Initially, Particle Swarm Optimization (PSO) was adopted for efficient selection of cluster heads and malicious node detection, which aims to establish the trust among connected nodes that can enhance security as every participating node will produce as well as propagate authentic, accurate, and trusted content within the network. Two specialized nodes, dubbed Calculator Key (CK) and Distribution Key (DK), are then responsible for producing, verifying, and distributing secret keys among nodes employing Asymmetric key cryptography. The proposed method is named Optimal Cluster_ Trust Asymmetric Key Management Protocol (OptCH_TAKMP), which is considered an excellent energy‐efficient cluster head election process with a securable routing mechanism. It is compared with three state‐of‐art methods. Simulation results show that proposed OptCH_TAKMP achieves 31.4% of routing overhead, 23% of end‐to‐end delay, 78.6% of energy efficiency, 94.8% of throughput, 28.2% of average latency, 91.4% of malicious detection rate, and 92.4% of packet delivery ratio, 85.2% of network lifetime, 19.2% of communication cost and 28.6% of trust computation error.

Cluster head selection and malicious node detection using large-scale energy-aware trust optimization algorithm for HWSN

Cluster head selection and malicious node detection using large-scale energy-aware trust optimization algorithm for HWSN