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In this paper, we propose a Proof-of-Location (PoL)-based location verification scheme for mitigating Sybil attacks in vehicular ad hoc networks (VANETs). For this purpose, we employ smart contracts for storing the location information of the vehicles. This smart contract is maintained by Road Side Units (RSUs) and acts as a ground truth for verifying the position information of the neighboring vehicles. To avoid the storage of fake location information inside the smart contract, vehicles need to solve unique computational puzzles generated by the neighboring RSUs in a limited time frame whenever they need to report their location information. Assuming a vehicle has a single Central Processing Unit (CPU) and parallel processing is not allowed, it can solve a single computational puzzle in a given time period. With this approach, the vehicles with multiple fake identities are prevented from solving multiple puzzles at a time. In this way, we can mitigate a Sybil attack and avoid the storage of fake location information in a smart contract table. Furthermore, the RSUs maintain a dedicated blockchain for storing the location information of neighboring vehicles. They take part in mining for the purpose of storing the smart contract table in the blockchain. This scheme guarantees the privacy of the vehicles, which is achieved with the help of a PoL privacy preservation mechanism. The verifier can verify the locations of the vehicles without revealing their privacy. Experimental results show that the proposed mechanism is effective in mitigating Sybil attacks in VANET. According to the experiment results, our proposed scheme provides a lower fake location registration probability, i.e., lower than 10%, compared to other existing approaches. [ABSTRACT FROM AUTHOR]

The BitTorrent network, a well-known Internet-scale P2P system, applies the proportional response protocol to exchange resource, where each participant contributes resource to neighbors in proportion to the amount it received in the previous round. The dynamics of this protocol is known to converge to a market equilibrium. However, an agent may manipulate this protocol by a Sybil attack to create fictitious identities and control them to gain more benefit. We apply the concept of incentive ratio , the percentage of the new utility after a Sybil attack over the benchmark, to measure the incentive of a strategic agent to play a Sybil attack, proving a tight bound of two over general networks. This finding completes the theoretical picture for incentive analysis on Sybil attacks facing the legendary tit-for-tat protocol for Internet bandwidth sharing and other applications. [ABSTRACT FROM AUTHOR]

Summary: The Social Internet of Things (SIoT) allows for more efficient and intelligent communication between entities (humans and objects). Managing the digital identity of objects, protecting the owner's privacy, and resisting impersonation attacks are crucial challenges in SIoT. On the other hand, the classic centralized identity provider servers contain valuable information for hackers and malicious parties. Effective solutions can be presented using decentralized technologies such as blockchains. This paper proposes a decentralized identity management system for SIoT based on smart contract technology. This system drives object identity control; conversely, a zero‐knowledge‐based protocol and double‐spend prevention ideas are proposed for privacy preservation and Sybil attack resistance, respectively. The central idea is to implement self‐sovereign identity (SSI) so that even certifiers are not privy to object‐sensitive information. Additionally, it attempts to prevent the owner from creating Sybil objects using the web of trust method. The method's scalability and convergence can be proven by considering all acknowledgment nodes and different paths. This method was compared with the most novel available methods; the results from this comparison depict the scalability and effectiveness of the proposed method for large networks. [ABSTRACT FROM AUTHOR]

With the advent of the information age, data storage has not only developed from paper information systems to electronic information system storage, but has also extended to cloud database storage methods. To date, we can see the application of big data and cloud in various fields and levels, making induction, arrangement, transmission, and exchange of data easier and faster. The electronic and systematisation of patient medical records in the medical environment is an example. The use of electronic medical records can help improve the quality of medical care, reduce duplication of treatment, and reduce the waste of examination resources. It has many advantages, but also many risks. Medical institutions may abuse electronic medical data without the consent of patients, leading to the leakage of patient privacy data and causing serious social impact. Therefore, this study proposes a medical record exchange and sharing mechanism based on the Hyperledger blockchain architecture combined with proxy reencryption. When pharmacists need to obtain medical records for related research, patients can decide whether to share medical records, and hospitals can also revoke pharmacist access rights at any time to achieve more private, secure, and convenient sharing of medical records. The proposed scheme meets various blockchain security requirements and the BAN (Burrows–Abadi–Needham) logic proof model (Burrows–Abadi–Needham) is applied to assess the correctness of the proposed scheme. The proposed scheme performs well in terms of computational and communication costs. [ABSTRACT FROM AUTHOR]

Public and permissionless blockchain systems are challenged by Sybil attacks, in which attackers use multiple identities to gain control. Traditionally, such attacks are prevented by consensus mechanisms relying on resource expenditure. However, such mechanisms (e.g. proof of work) face criticism for being wasteful. To address this and other concerns, novel blockchain systems backed by new consensus mechanisms have recently emerged. We formalise three key characteristics pursued by these systems: permissionlessness, Sybil attack resistance, and freeness. We demonstrate that no blockchain protocol can simultaneously achieve all three characteristics within the paradigm established by our formalisation. Thus, a trilemma emerges for distributed ledger technology designers, who must balance these characteristics thoughtfully. [ABSTRACT FROM AUTHOR]

In the modern digital market flooded by nearly endless cyber-security hazards, sophisticated IDS (intrusion detection systems) can become invaluable in defending against intricate security threats. Sybil-Free Metric-based routing protocol for low power and lossy network (RPL) Trustworthiness Scheme (SF-MRTS) captures the nature of the biggest threat to the routing protocol for low-power and lossy networks under the RPL module, known as the Sybil attack. Sybil attacks build a significant security challenge for RPL networks where an attacker can distort at least two hop paths and disrupt network processes. Using such a new way of calculating node reliability, we introduce a cutting-edge approach, evaluating parameters beyond routing metrics like energy conservation and actuality. SF-MRTS works precisely towards achieving a trusted network by introducing such trust metrics on secure paths. Therefore, this may be considered more likely to withstand the attacks because of these security improvements. The simulation function of SF-MRTS clearly shows its concordance with the security risk management features, which are also necessary for the network's performance and stability maintenance. These mechanisms are based on the principles of game theory, and they allocate attractions to the nodes that cooperate while imposing penalties on the nodes that do not. This will be the way to avoid damage to the network, and it will lead to collaboration between the nodes. SF-MRTS is a security technology for emerging industrial Internet of Things (IoT) network attacks. It effectively guaranteed reliability and improved the networks' resilience in different scenarios. [ABSTRACT FROM AUTHOR]

Vehicular Ad hoc Network (VANET) has recently gained significant attention as a means of enhancing the mobility, efficiency, and safety of applications in the intelligent transportation system. However, because of its high-speed mobility, wireless connectivity, and extensive node coverage, security is a more difficult procedure. The Sybil security threat on VANET is a growing problem today. The Road Side Unit (RSU) failed to synchronise its clock with the legal vehicle, then unplanned vehicles are predicted, thereby incorrect messages are transferred to them. In this paper, Competitive Dolphin Echolocation Optimisation (CDEO)-based Deep Residual Network is proposed for Sybil attack and RSU misbehaviour detection. Here, the effective routing process is performed using Fractional Glow-Worm Swarm Optimisation (FGWSO)-based traffic-aware routing protocol. In the base station, the Sybil attack detection is done. The Sybil attack detection process is done using a Deep residual network, which is trained by the proposed CDEO algorithm. The CDEO algorithm is devised by incorporating Dolphin Echolocation Optimisation (DEO) technique and Competitive Swarm Optimiser (CSO). Additionally, using the Deep residual network, the RSU misbehaviour detection is done. The performance of the developed method is compared with certain performance metrics, like precision, F1-measure, and recall of 0.9197, 0.9121, and 0.9046. [ABSTRACT FROM AUTHOR]

In the modern digital market flooded by nearly endless cyber-security hazards, sophisticated IDS (intrusion detection systems) can become invaluable in defending against intricate security threats. Sybil-Free Metric-based routing protocol for low power and lossy network (RPL) Trustworthiness Scheme (SF-MRTS) captures the nature of the biggest threat to the routing protocol for low-power and lossy networks under the RPL module, known as the Sybil attack. Sybil attacks build a significant security challenge for RPL networks where an attacker can distort at least two hop paths and disrupt network processes. Using such a new way of calculating node reliability, we introduce a cutting-edge approach, evaluating parameters beyond routing metrics like energy conservation and actuality. SF-MRTS works precisely towards achieving a trusted network by introducing such trust metrics on secure paths. Therefore, this may be considered more likely to withstand the attacks because of these security improvements. The simulation function of SF-MRTS clearly shows its concordance with the security risk management features, which are also necessary for the network’s performance and stability maintenance. These mechanisms are based on the principles of game theory, and they allocate attractions to the nodes that cooperate while imposing penalties on the nodes that do not. This will be the way to avoid damage to the network, and it will lead to collaboration between the nodes. SF-MRTS is a security technology for emerging industrial Internet of Things (IoT) network attacks. It effectively guaranteed reliability and improved the networks’ resilience in different scenarios.

Online Social Networks (OSNs) have become increasingly popular platforms for personal, professional, and social networking. However, the rise of fraudulent events such as fake news and rumors has created security threats within OSNs, leading to significant impacts. Among the major threats are Sybil attacks, where fake accounts, known as Sybils, are generated within OSNs to carry out various malicious activities. To address this issue, this paper proposes the DeepSybil model, which utilizes the Gannet Optimization Algorithm (GOA) and Graph Attention Networks (GATs) to detect Sybil attacks in OSNs. The proposed model starts by gathering various input information using the Social Network Fake Account (SNFA) dataset. This data is then preprocessed to eliminate redundant and irrelevant data points. Next, meaningful features are extracted to capture user behaviors and interactions, while an adjacency matrix represents the links between users to analyze the network structure of OSNs. Categorical features are encoded into binary features using One-hot encoding. The dataset is divided into training and testing sets to evaluate the model's efficiency. The GOA algorithm is employed to optimize the parameters of the proposed model, thereby improving the accuracy of Sybil attack detection. GATs leverage the attention mechanism to accurately identify Sybil attacks within OSNs. The model outputs a probability indicating the likelihood of an account being a Sybil. To evaluate the effectiveness of the proposed model, various performance metrics, including accuracy, precision, F1-score, recall, Receiver Operating Characteristic curve (ROC), and specificity, are employed. The results demonstrate that the proposed model achieves a high accuracy of 96.8%, outperforming existing methods. Thus, the proposed DeepSybil model proves to be a highly suitable solution for accurately detecting Sybil attacks in OSNs. [ABSTRACT FROM AUTHOR]

Online social networks (OSNs) have gained popularity as platforms for professional, personal, and social networking. However, they are also vulnerable to fraudulent events such as rumors or fake news, which can mislead users and have serious consequences. The dissemination of misinformation on social networking sites has become a global threat. To address this, a novel model GO-3DANB is proposed to utilize a three-stream, double attention network-modified BiLSTM (3S-A2 DenseNet-modified BiLSTM) with Gannet optimization algorithm (GOA) to predict Sybils in OSNs. The GO-3DANB model extracts network weights and crucial characteristics using the 3S-A2 DenseNet-modified BiLSTM to automatically extract higher and lower features from social network fake account (SNFA) input data. The SNFA dataset, containing 17 metadata features from fake and real profiles and a total of 921 data points, is collected from Kaggle. The modified BiLSTM is used to establish forward correlation with input sequences and learn dependencies with input sequences in backward sequence, thereby completely extracting context information among various channels. We utilize GOA optimization to assess each individual's fitness for identifying Sybil's attacks. Our proposed method is evaluated using various metrics, including precision, sensitivity, accuracy, specificity, F1-score value, and receiver operating characteristic curve (ROC). In comparison to existing methods, our proposed method, GO-3DANB, achieves 96% accuracy, 96.34% precision, 95.31% specificity, 96.5% sensitivity, 0.96 F1-score value, and 0.97 ROC. Thus, our proposed method demonstrates the potential to address the problem of Sybil detection in OSNs and provides a valuable contribution to the field of Sybil security. [ABSTRACT FROM AUTHOR]

The recent expansion of the Internet of Things (IoT) owes a lot to the significant contribution of the 6LoWPAN protocol, which has been extensively employed in low-power and lossy networks. To facilitate communication in 6LoWPAN networks, the Internet Engineering Task Force (IETF) has suggested the usage of the Routing Protocol for Low-Power and Lossy Networks (RPL). Despite its usefulness, the open and restricted nature of the RPL protocol renders it susceptible to both internal and external attacks. Since IoT devices connected through the RPL protocol have limited resources like processing power, battery life, memory, and bandwidth, ensuring their security is of the utmost importance. One of the primary obstacles to IoT networks is RPL routing attacks, which disrupt the network's normal routing activities and structure. This study investigates the impact of five RPL routing attacks, namely Blackhole, Sybil, Selective Forwarding (SF), Sinkhole, DIO suppression, and DIS flooding, on the IoT networks' performance. The study evaluated the network's performance for normal and five routing attack scenarios using numerous performance metrics including Link throughput, No. of packets generated (control and data), Sensor data throughput, Packet Delivery Ratio (PDR), and Delay in packet delivery. This work conducted simulations using the Tetcos NetSim v12.1 IoT network simulator tool and is the first to analyze IoT network performance under multiple routing assault scenarios with various performance measures. The analysis showed that the performance metrics of PDR, Sensor data throughput, and No. of data packets transmitted decreased significantly in attack scenarios compared to the normal scenario, with an average decreased percentage of 70%, 70%, and 39.4%, respectively. In contrast, the metrics Link throughput, Delay, and No. of control packets transmitted increased in attack scenarios compared to the normal scenario, with average values supplemented by a factor of 35, 255, and 36, respectively. Additionally, the Destination-Oriented Directed Acyclic Graph (DODAG) real-time formation under different scenarios was provided. [ABSTRACT FROM AUTHOR]

Summary: An attack on VANET known as a Sybil attack involves the attacker claiming or stealing multiple identities with the intent of causing havoc on the network's operation by disseminating false information. To protect the VANET network from Sybil attack, a variety of methods have been proposed. In order to compensate for the Sybil attack on VANETs, a rumor riding with salp swarm optimization (SSO) based Routing protocol is developed in this research work. The primary goal of this investigation is to discover how VANETs are vulnerable to the Sybil attack in order to improve their security. Rumor riding will be used to identify the Sybil attack in the suggested strategy. VANETs are protected by the Blowfish Algorithm, which uses SSO to select the best key for each network. The collective intelligence and dynamic adaptation of SALP swarm optimization algorithm enable efficient identification of rumor sources in complex networks. The algorithm is scalable, flexible, and adept at reducing false positives and negatives, making it suitable for large‐scale monitoring. Its parallel processing capabilities and robustness ensure rapid and reliable detection. Using the NS2 platform, the proposed method is compared to existing benchmark in terms of delay, packet delivery ratio (PDR), packet loss, throughput, and attack detection rate. The proposed SSO has reduced the packet loss by nearly 5% and delay by nearly 7%, when compared with existing techniques. The delivery ratio of packet for proposed model is increased up to 8% of PDR, when compared with ACO, PSO, and FA. According to this evidence, it is clear that the proposed SSO outperformed existing approaches in a variety of performance metrics. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach for enhancing the authentication and power optimization of Wireless Sensor Networks (WSNs) by using an Energy efficient OLSR protocol design and Elliptic curve cryptography (ECC) algorithm to prevent Sybil attacks. Sybil attacks are considered the most critical attacks on WSNs as they involve the use of forged identities to gain malicious access to the network, which can lead to data loss and other serious consequences. The proposed approach focuses on the deployment of nodes and the updation of routing tables using an Energy efficient OLSR protocol, which is designed to optimize network performance while minimizing energy consumption. The use of multi-hop and relay-based clustering techniques facilitates cluster formation, which further enhances network performance. To improve the security of transmission, an ECC algorithm is implemented, which provides a higher level of security than traditional cryptographic systems. This approach also includes a flexible scheme for the recognition and prohibition of Sybil attacks during the transmission process, which enhances the energy efficiency of the system. The novelty and contribution of this study lie in the combination of an Energy-efficient OLSR protocol design and the use of an ECC algorithm to prevent Sybil attacks in WSNs. This approach has not been extensively explored in previous research, and the proposed approach is expected to provide significant benefits in terms of improved energy economy and safety for WSNs. The performance evaluation of the proposed approach includes time delay, throughput, energy efficiency, and detection rate, which provides a comprehensive assessment of its effectiveness. Finally, this paper addresses an important research gap in WSNs' security and energy efficiency and has significant practical implications for various fields, including military applications, environmental monitoring, and queue tracking. The proposed approach has the potential to enhance the reliability and security of WSNs, making them more suitable for use in various applications. [ABSTRACT FROM AUTHOR]

Sensor networks have emerged as a promising technology for collecting data and capturing information about the physical world. However, these networks are often deployed in harsh and inaccessible environments, making them susceptible to various security attacks. One of the most severe attacks in wireless sensor networks (WSNs) is the Sybil attack, where a malicious node illegitimately assumes multiple fraudulent identities to deceive and disrupt the network. To address this complex and challenging problem, this paper proposes a dual trust-based multi-level Sybil (DTMS) attack detection approach for WSNs. The approach employs a multi-level detection system that verifies the identity and location of each node. At each level (Cluster Member, Cluster Head, and Base Station), a trust value is calculated based on the node's behavior. The trust value incorporates both communication trust and data trust, ensuring a satisfactory level of trust before accepting information from a node. The DTMS approach incorporates a dynamic reward and penalty coefficient in the trust function to accurately capture the severity of a node's behavior. Additionally, data aggregation techniques are employed to reduce communication overhead and conserve energy. The performance of DTMS is evaluated based on various metrics such as the severity of the trust function, true detection rate, false detection rate, residual energy, network lifetime, and packet loss ratio. Simulation results demonstrate that DTMS can effectively detect Sybil nodes, achieving a 100% detection rate in a malicious environment. Furthermore, a comparison with existing schemes highlights the desirable performance of DTMS across multiple parameters, including true detection rate, false detection rate, energy consumption, packet loss rate, and the number of active nodes. [ABSTRACT FROM AUTHOR]

In recent years, drones have become increasingly prevalent in a wide range of applications, performing complex and critical tasks. To accomplish these tasks, drones cooperate by forming a Flying Ad-Hoc Network (FANET) using specific routing protocols for communication. However, most of the routing protocols used in this type of network lack appropriate built-in security mechanisms, which creates numerous security challenges and concerns. To reduce the resulting security vulnerabilities and mitigate the impact of potential attacks, it is crucial to address these challenges before any deployment of FANETs. In this paper, Sybil and Gray Hole attacks in FANET are analyzed. A commonly used and scalable routing protocol, namely the Greedy Perimeter Stateless Routing (GPSR) protocol, has been considered. After demonstrating the magnitude of the damage caused by the two attacks, a mitigation technique is proposed for each of them resulting in a secured version of the GPSR, namely secGPSR. The robustness of secGPSR is validated through simulation using Omnet++ presenting the results that show the effectiveness of secGPSR in counteracting Sybil and Gray Hole attacks in FANET.

In the field of blockchain security research, especially regarding the core Proof of Work (PoW) consensus mechanism, researchers continue to reveal novel methods of mining attacks. Recently, Wang et al. demonstrated an innovative attack mode: the Selfish Mining-based denial of service (SDoS) attack, revealing how this attack strategy introduces a new threat to the blockchain system through the selfish mining strategy. Expanding upon Wang et al.’s findings, our research refines the mining strategy by integrating the strategy of Equal-Fork Stubborn Mining, a more sophisticated variant of selfish mining that can significantly enhance the attacker’s advantage under certain network conditions. Furthermore, we combine this refined strategy with a Sybil attack aimed at disrupting the normal propagation and confirmation processes of blocks within the blockchain system, effectively reducing the income of honest miners. Therefore, we propose a new mining attack – a denial of service (SSDoS) attack based on selfish mining and sybil attack. Our experimental results indicate that compared to other known mining attacks, the implementation of the SSDoS attack by an attacker drastically reduces the willingness of honest miners to continue their mining activities, leading to a significantly lower level of participation. For an honest miner controlling 10% of the total computational power, the portion of the critical profitability factor calculated in SSDoS that is higher than the one calculated in SDoS accounts for about 25%.

Online media is currently grappling with a crisis characterized by diminishing trust, the widespread dissemination of misinformation, and the alarming proliferation of fake news and experiences. The aim of the study - to delve into the challenges plaguing the digital media landscape and to propose the adoption of biometric technology as a potential solution. Biometrics, as a cutting-edge technology, encompasses the intricate process of quantifying and statistically assessing the unique physical and behavioral characteristics that distinguish individuals from one another. Its multifaceted potential extends far beyond mere identification. It is established that biometrics excels in the vital realms of identity verification, content authentication, and countering malicious activities like bots and Sybil attacks. Furthermore, it is applicable for tailoring personalized user experiences, thus offering a comprehensive solution to address the pressing challenges faced by online media today. The usage of these capabilities, makes biometrics a distinctive and promising avenue to not only restore trust but also combat the pervasive issue of misinformation, ultimately fostering a secure and resilient online media ecosystem.

Cloud computing platforms have been one of the best sources for resource computation and service recommendations in the recent years. Users' reliance on the cloud has increased dramatically during the pandemic period, particularly for data storage and pay-per-use services. Restricted and reliable access control is essential for protecting the data stored in the cloud. The traditional role-based access control techniques are ineffective in multi-tenant computing systems like the cloud. To provide security in cloud computing systems and offer a trusted environment for service providers and service users, we present a trust-oriented role-based access control paradigm in this work. The Trust Management System's reputation is something that TRBAC wants to uphold by ensuring that various cloud threats do not compromise the service requests made and used by individual users. Validation and analysis of the membership credentials of users and roles mapped in the access control list ensure restricted access control in TRBAC. The proposed model intends to assist the data owners in identifying reliable service users and service providers by reviewing the interaction history and assessing direct, indirect, and weighted trust. This paper demonstrates how calculated trust values detect nefarious nodes and recommend defense mechanisms against various security concerns, including the Sybil attack, the On–off attack, the Collusion attack, and the DoS attack. To illustrate the impact of the suggested mitigation techniques and to handle various security concerns, a comparative analysis of the TRBAC model is done with its peers. [ABSTRACT FROM AUTHOR]

This research paper explores the multifaceted nature of diabetes, encompassing a spectrum of disorders characterized by disruptions in insulin, a hormone crucial for facilitating the absorption of blood sugar by the body. Dysregulation in insulin production can elevate blood sugar levels, thereby precipitating various diseases and complications, some of which may prove fatal. The present study advocates the integration of technology in healthcare as a means to aid medical professionals in classifying diabetes and monitoring ketone levels in the blood. A crucial facet of this endeavor involves the establishment of a robust platform for self-management of diabetes, aiming to regulate glucose levels, blood pressure, and the requisite insulin levels essential for the overall functioning of the human body. Leveraging medical sensors based on the Internet of Things (IoT) is proposed to be a transformative approach, promising to streamline costs and enhance the user experience within the medical domain. This research paper delves into the potential implications and benefits of incorporating advanced technology in the management of diabetes, presenting a comprehensive analysis of its impact on healthcare practices and patient outcomes. [ABSTRACT FROM AUTHOR]

Due to the development of transportation technology, the number of vehicles on the road has been exponential over the years. Road safety is one of the crucial tasks of the transportation department because of collations and accidents each year. Using a Vehicular ad hoc network (VANET) makes communication between vehicles possible and reduces the complexities in vehicle transportation. Privacy is one of the significant tasks in the VANET for a safe and uninterrupted transportation process. Sybil attack is one of the significant issues in the VANET in which attackers introduce dummy nodes to confuse or interrupt the other users in the network to reduce the performance to hack the data. This work proposes a new technique to detect and disconnect Sybil from the network to improve its performance. Historical and statistical data with Extreme Learning Machine is used to classify the Sybil attack in the VANET. This work improved the classification accuracy and network performance compared to the conventional Sybil node identification technique. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) comprises numerous resource-constrained devices that generate large volumes of data. The inherent vulnerabilities in IoT infrastructure, such as easily spoofed IP and MAC addresses, pose significant security challenges. Traditional routing protocols designed for wired or wireless networks may not be suitable for IoT networks due to their limitations. Therefore, the Routing Protocol for Low-Power and Lossy Networks (RPL) is widely used in IoT systems. However, the built-in security mechanism of RPL is inadequate in defending against sophisticated routing attacks, including Sybil attacks. To address these issues, this paper proposes a centralized and collaborative approach for securing RPL-based IoT against Sybil attacks. The proposed approach consists of detection and prevention algorithms based on the Random Password Generation and comparison methodology (RPG). The detection algorithm verifies the passwords of communicating nodes before comparing their keys and constant IDs, while the prevention algorithm utilizes a delivery delay ratio to restrict the participation of sensor nodes in communication. Through simulations, it is demonstrated that the proposed approach achieves better results compared to distributed defense mechanisms in terms of throughput, average delivery delay and detection rate. Moreover, the proposed countermeasure effectively mitigates brute-force and side-channel attacks in addition to Sybil attacks. The findings suggest that implementing the RPG-based detection and prevention algorithms can provide robust security for RPL-based IoT networks. [ABSTRACT FROM AUTHOR]

Vehicular Ad-hoc Networks (VANETs) have emerged as part of Intelligent Transportation Systems (ITS), offering the potential to enhance passenger and driver safety, as well as driving conditions. However, VANETs face significant security challenges and various attacks due to their wireless nature and operation in free space. Mutual authentication between vehicles and RSUs is one of the most, if not the most, critical security requirements in VANETs. In this process, maintaining resource authenticity, data authenticity and preserving users' privacy, are key concerns. This paper proposes a pseudonym-based authentication scheme for VANETs, built upon existing approaches. The proposed scheme not only ensures the aforementioned security requirements but also meets critical security requirements for the mentioned process in VANETs, such as non-reputation, unlinkability, and unforgeability. Furthermore, the suggested scheme effectively detects and mitigates the Sybil attack in mutual authentication between vehicles and RSU, a well-known and common threat. By comparing the efficiency and security characteristics of the proposed scheme with other existing approaches, it becomes evident that the suggested scheme surpasses previously proposed methods. [ABSTRACT FROM AUTHOR]

A Sybil attack occurs when an adversary controls multiple system identifiers (IDs). Limiting the number of Sybil (bad) IDs to a minority is critical for tolerating malicious behavior. A popular tool for enforcing a bad minority is resource burning (RB): the verifiable consumption of a network resource. Unfortunately, typical RB defenses require non-Sybil (good) IDs to consume at least as many resources as the adversary. We present a new defense, Ergo , that guarantees (1) there is always a bad minority; and (2) during a significant attack, the good IDs consume asymptotically less resources than the bad. Specifically, despite high churn, the good-ID RB rate is O (T J + J) , where T is the adversary's RB rate, and J is the good-ID join rate. We show this RB rate is asymptotically optimal for a large class of algorithms, and we empirically demonstrate the benefits of Ergo. [ABSTRACT FROM AUTHOR]

Abstract Background Internet of things (IoT) has been used in many places. IoT make devices connected to the Internet via sensor devices to achieve the interconnection between things and things, people and things. Sybil attacker attacks IoT by imitating the identity of users. Few methods are applicable for mobile IoT in previous Sybil attack detecting methods, while the methods are mainly focus on static IoT. Results A distributive and lightweight Sybil attack detection scheme in the mobile IoT is proposed in this paper. This scheme works around received signal strength indications (RSSI). The scheme consists of two rounds. Identity information is sent from member nodes to edge nodes in both of the two rounds. In the first round edge, nodes calculate the possible RSSI interval for each member node; in the second round, they check the RSSI value of member nodes to detect Sybil attacks. Intelligent algorithms are used to predict the position of member nodes, which makes the theoretical interval more accurate. Extensive experimental studies show that in the true and false detection rate, this scheme is superior to many existing schemes.

The smart grid relies on Advanced Metering Infrastructure (AMI) to function. Because of the significant packet loss and slow transmission rate of the wireless connection between smart meters in AMI, these infrastructures are considered Low-power and Lossy Networks (LLNs). The routing protocol in an AMI network is crucial for ensuring the availability and timeliness of data transfer. IPv6 Routing Protocol for Low-power and lossy networks (RPL) is an excellent routing option for the AMI communication configuration. However, it is highly at risk against many external and internal attacks, and its effectiveness may be severely diminished by Sybil assault. Different trust-based techniques have been suggested to mitigate internal attacks. However, existing trust systems have high energy consumption issues, which cause a reduction in the performance of LLNs due to complex calculations at the node level. Therefore, this paper presents a novel fog-enabled GINI index-based trust mechanism (GITM) to mitigate Sybil attacks using the forwarding behavior of legitimate member nodes. Regarding identifying and isolating Sybil assaults, our approach outperforms the state-of-the-art methods. GITM detects and isolates a more significant number of malicious network nodes compared to other techniques within a similar time frame. By using the proposed GITM framework, the Sybil attack detection rate increases by 4.48%, energy consumption reduces by 21%, and isolation latency reduces by 26.30% (concerning time). Furthermore, the end-to-end delay is merely 0.30% more in our case, and the number of control messages decreases by 28%.

Numerous sensitive applications, such as healthcare and medical services, need reliable transmission as a prerequisite for the success of the new age of communications technology. Unfortunately, these systems are highly vulnerable to attacks like Sybil, where many false nodes are created and spread with deceitful intentions. Therefore, these false nodes must be instantly identified and isolated from the network due to security concerns and the sensitivity of data utilized in healthcare applications. Especially for life-threatening diseases like COVID-19, it is crucial to have devices connected to the Internet of Medical Things (IoMT) that can be believed to respond with high reliability and accuracy. Thus, trust-based security offers a safe environment for IoMT applications. This study proposes a blockchain-based fuzzy trust management framework (BFT-IoMT) to detect and isolate Sybil nodes in IoMT networks. The results demonstrate that the proposed BFT-IoMT framework is 25.43% and 12.64%, 12.54% and 6.65%, 37.85% and 19.08%, 17.40% and 8.72%, and 13.04% and 5.05% more efficient and effective in terms of energy consumption, attack detection, trust computation reliability, packet delivery ratio, and throughput, respectively, as compared to the other state-of-the-art frameworks available in the literature. [ABSTRACT FROM AUTHOR]

Vehicular Ad Hoc Networks (VANETs), which have the strategic goal of ensuring service delivery on roads and smart cities, are made up of a number of critical components, including the integration and communication between vehicles, sensors, and stationary road-side components. VANETs have many distinctive characteristics, including quick movable nodes, self-organization, scattered networks, and often changing topologies. Since attack prevention is still a contentious topic, security, data integrity, and user private information continue to be major concerns despite the recent development of VANETs. In particular, detecting Sybil attacks, in which one node attempts to appear as many, seemingly conflicts with the goal of privacy preservation, and existing schemes fail on either one or both accounts. In order to prevent Sybil attacks, VANETs assert that they have security measures in place. A new framework for VANET security and sybil attack detection was developed in this study. It groups the cars together using Kernel k-harmonic means (KKHM). Kernel functions are here included into the k-means problem's cost function via kernelizing in the feature space. Cluster centres are believed to exist in a higher level Hilbert space called the feature space. Using the Floyd-Warshall algorithm (FWA), cluster heads are chosen. The Deep Neural Network (DNN) is used to identify the malicious CH by extracting the necessary information from the CH. For the purpose of optimizing DNN parameters, a new technique known as gradient-based elephant herding optimization (GBEHO) is presented. To initialize the population in the search space, the gradient-based method GBO is paired with the elephant optimization algorithm (EHO). Second, by including Gaussian chaos mapping, the initialized population is enhanced in order to correct the disparity between the first exploration and utilization of the EHO. Additionally, two operators are programmed to modify the agents' position update strategy: the random wandering and variation operators. Last but not least, the Modified Advanced Encryption Standard (MAES) is used to send the data in the CH safely to the cloud. According on experimental findings, the suggested model increases the security rate of 96% and reduces the encryption time of 19(s) for 100 (kb) data which is better when compared to other existing models.

P2P networks are particularly vulnerable to Sybil and Eclipse attacks, especially those based on Distributed Hash Tables (DHT). However, detecting Sybil and Eclipse attacks is a challenging task, and existing methods are ineffective due to unequal sample distribution, incomplete definitions of discriminating features, and weak feature perception. This paper proposes a Fuzzy Secure Kademlia (FSK) that detects and mitigates the Sybil and Eclipse attack. At first, a node requests authentication by providing its MAC address, location, Node Angle (NA), and Node Residual Energy (NRE) to an infrastructure server. As long as the packet's ID, location, NA, and NRE match the packet's received ID, it can be recognized as normal. The incoming packet, however, is detected as Sybil or Eclipse attack packets if copies are made in locations other than those specified. When the Sybil or Eclipse attack has been detected, locate the multiplied nodes. By using the FSK, the malicious node can be removed, preventing it from causing any harm to the network. The suggested framework is compared with existing methods in terms of detection time, and energy consumption. Experimental results indicate that the suggested FSK technique achieves a better detection time of 29.4%, 25.5%, 22.6%, and 18.1% than CSI, DHT, CMA, and EDA methods. [ABSTRACT FROM AUTHOR]

Background: Internet of things (IoT) has been used in many places. IoT make devices connected to the Internet via sensor devices to achieve the interconnection between things and things, people and things. Sybil attacker attacks IoT by imitating the identity of users. Few methods are applicable for mobile IoT in previous Sybil attack detecting methods, while the methods are mainly focus on static IoT. Results: A distributive and lightweight Sybil attack detection scheme in the mobile IoT is proposed in this paper. This scheme works around received signal strength indications (RSSI). The scheme consists of two rounds. Identity information is sent from member nodes to edge nodes in both of the two rounds. In the first round edge, nodes calculate the possible RSSI interval for each member node; in the second round, they check the RSSI value of member nodes to detect Sybil attacks. Intelligent algorithms are used to predict the position of member nodes, which makes the theoretical interval more accurate. Extensive experimental studies show that in the true and false detection rate, this scheme is superior to many existing schemes. [ABSTRACT FROM AUTHOR]