Sybil Attack Detection Research Articles

Retraction notice: An RSSI-based Sybil attack detection system with continuous authentication using a novel lightweight multimodal biometrics

Retraction notice: An RSSI-based Sybil attack detection system with continuous authentication using a novel lightweight multimodal biometrics

Detecting Sybil Attacks in VANET: Exploring Feature Diversity and Deep Learning Algorithms with Insights into Sybil Node Associations

Detecting Sybil Attacks in VANET: Exploring Feature Diversity and Deep Learning Algorithms with Insights into Sybil Node Associations

Developing an optimized routing protocol with rumor riding technique for detection of Sybil attack in VANET environment

SummaryAn 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.

DTMS: A Dual Trust-Based Multi-level Sybil Attack Detection Approach in WSNs

DTMS: A Dual Trust-Based Multi-level Sybil Attack Detection Approach in WSNs

Doppler-Shift-Based Sybil Attack Detection for Mobile IoT Networks

The rapid growth of Internet of Things (IoT) networks brings new security challenges for service providers. Due to the resource constrained nature of IoT networks, conventional security methods are not always suitable. Therefore, physical layer security (PLS) has come to the forefront, providing a high level of security while respecting the limited resources of IoTs. A Sybil attack is an insider attack in which a malicious node illegitimately fakes multiple identities, to impersonate legitimate nodes in the IoT network. This study introduces a novel Sybil attack detection scheme for mobile IoT networks that corresponds to time-varying channel. Doppler shift caused by mobile IoT nodes is considered as a novel detection metric to identify available Sybil attacks. The proposed scheme is analyzed by both the true positive rate and the false positive rate, which are mathematically formulated to verify the simulation results. Results demonstrate that as the randomness in the mobility pattern of IoT nodes increases, the proposed detection mechanism based on Doppler shift offers improved identification of the Sybil nodes. Moreover, this work provides receiver operating characteristics (RoCs) for mobile IoT networks to evaluate the effect of different system parameters including carrier frequency, velocity, subcarrier spacing (SCS), and the size of cyclic prefix (CP). The performance of the proposed scheme improves with an increase in both the carrier frequency and velocity, as the Doppler shift becomes more pronounced.

An efficient secure and energy resilient trust-based system for detection and mitigation of sybil attack detection (SAN).

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.

Proposed approach for securing VANET by detection and avoidance of Sybil attack

Our world has gone fully mobile, and with advanced technologies, communication in this dynamic era has been possible with MANET and VANET. Along with the facilities extended to our pockets, the security of our data has also been a big concern. The VANET network has numerous obstacles to maintaining information security. One aspect of the suggested study is finding the Sybil node and ensuring measures for avoiding the same in VANET. The proposed model uses improved RC4A data encryption to protect the network from Sybil attacks.

Sybil Attack Detection Based on Signal Clustering in Vehicular Networks

Sybil Attack Detection Based on Signal Clustering in Vehicular Networks

SCA: SYBIL-BASED COLLIONS ATTACKS OF IIOT DATA POISOING IN FEDERATED LEARNING

The project focuses on enhancing security in Vehicular Ad Hoc Networks (VANETs) by proposing a novel approach for detecting Sybil attacks. Sybil attacks in VANETs involve a malicious vehicle creating multiple fake identities to manipulate network operations. This project employs a combination of Proofs of Work (PoW) and location-based techniques to detect and mitigate Sybil attacks effectively, ensuring the integrity and reliability of vehicular communication.

A novel model for Sybil attack detection in online social network using optimal three-stream double attention network

A novel model for Sybil attack detection in online social network using optimal three-stream double attention network

Detecting Sybil Attack During Data Transmission Using Concealed Data Sharing Algorithm in Manet

As the mobile users are increasing sharply, the wireless or mobile ad hoc network data transmission is on the rise and gaining lot of attention from the research communities. The most important issue that has to be handled is the detecting the Sybil attack and increase the reliability and confidentiality of the data transmission. The foremost aim of this paper is to develop a mechanism to detect the Sybil attack early and evade the data loss and privacy to ensure credibility to the users. The proposed algorithm employs a concealed data sharing plan during the route discovery phase and detects and alleviates the attack to guarantee attack free data transmission in the MANET. The data sharing plan allows only the nodes that are trustworthy and permit the data to be transmitted across them, thereby evading the malicious nodes during the route discovery process and curtails the wastage of time and energy of the nodes.

Enhancing security in online social networks: introducing the DeepSybil model for Sybil attack detection

Enhancing security in online social networks: introducing the DeepSybil model for Sybil attack detection

An Implementation of Effective Machine Learning Approaches to Perform Sybil Attack Detection (SAD) in IoT Network

The rapid expansion of the technology industry has resulted in the emergence of a number of new areas of research, one of which is known as "intrusion detection." The objective of an Intrusion Detection System, often known as an IDS, is to categorize user behaviors as either benign or malicious, and then to notify the relevant parties in accordance with this classification. However, a variety of strategies for attack detection have been developed, such as the Sybil attack; however, present techniques are restricted to concentrating on both aspects simultaneously because to constraints in detection accuracy and energy consumption. One such strategy is the Sybil attack. In order to circumvent these restrictions, a framework for the detection of Sybil attacks that is more effective in terms of detection accuracy (security) and energy usage (power) has been presented. Nevertheless, the suggested structure is simple, uncomplicated to understand, and does not need any computing requirements. In this particular architecture, in addition to the Cooja-Contiki simulator, three distinct machine learning methods are used. The NSL-KDD dataset is used in order to test the performance of the proposed framework. This dataset attained the maximum accuracy possible, which was 99.1s%. In addition, in this study, a comparison examination of the suggested work with an existing technique that is considered to be state-of-the-art is carried out in order to determine whether approach is more effective.

Statement of Retraction: Machine Learning Technique to Detect Sybil Attack on IoT Based Sensor Network

Statement of Retraction: Machine Learning Technique to Detect Sybil Attack on IoT Based Sensor Network

Detecting Sybil Attack in Blockchain and Preventing through Universal Unique Identifier in Health Care Sector for privacy preservation

Health care data requires data secrecy, confidentiality, and distribution through public networks. Blockchain is the latest and most secure framework through which health care data can be transferred on the public network. Blockchain has gained attention in recent year’s due to its decentralized, distributed, and immutable ledger framework. However, Blockchain is also susceptible to many attacks in the permission less network, one such attack is known as Sybil attack, where several malicious nodes are created by the single node and gain multiple undue advantages over the network. In this research work, the Blockchain network is created using the smart contract method which gets hampered due to Sybil attack. Thus, a novel method is proposed to prevent Sybil attack in the network for privacy preservation. Universal Unique Identifier code is used for identification and prevention of the Sybil attack in the self-created networks. Results depict that proposed method correctly identifies the chances of attack and the prevention from the attack. The approach has been evaluated on performance metrics namely, true positive rate and accuracy which were attained as 87.5 % and 91% respectively, in the small network. This demonstrates that the proposed work attains improved results as compared to other latest available methods.

Sybil Attack Detection in Internet of Flying Things-IoFT: A Machine Learning Approach

Sybil attack refers to the situation when a malicious node falsely claims to have numerous identities and is known to be one of the security threats to the Internet of Things (IoT). Due to recent increase usage of unmanned aerial vehicles (UAVs) in various applications, Sybil attack has been identified as a threat to the flying ad hoc network (FANET) paradigm and its integration with the IoT to form the Internet of Flying Things (IoFT). In this paper, we propose an intelligent Sybil attack detection approach for FANETs-based IoFT using physical layer characteristics of the radio signals emitted from the UAVs as detected by two ground nodes. A supervised machine learning approach is employed and experimented with several different classifiers available in the Weka workbench platform. The experiment was carried out based on two features of the radio signals, namely, the received signal strength difference (RSSD) and the time difference of arrival (TDoA). Simulation results revealed that the proposed scheme can achieve a high correct classification accuracy of above 91% on average, even for smart malicious nodes with power control capability operating at power levels not directly trained. In addition to its high performance, the proposed scheme is also less susceptible to various attacks commonly carried out on the upper layers, such as data spoofing, due to the use of only intrinsically generated physical layer data. Furthermore, no additional communications overheads of the UAV nodes are required for the functionality of this scheme.

Highly accurate sybil attack detection in vanet using extreme learning machine with preserved location

Highly accurate sybil attack detection in vanet using extreme learning machine with preserved location

Floyd–Warshalls algorithm and modified advanced encryption standard for secured communication in VANET

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.

PrSLoc: Sybil attack detection for localization with private observers using differential privacy

During the localization process in wireless networks, risks such as Sybil attacks and nodes’ location privacy leakage can exist due to the open and shared nature of wireless networks. However, it is challenging to obtain accurate location for sensor nodes while preserving the privacy of observer nodes who assist in sensor node localization while in the presence of Sybil attacks. Therefore, in this paper, we propose a secure and private localization algorithm, PrSLoc, based on Differential Privacy and Approximate Point-In-Triangulation (APIT, a range-free positioning algorithm). Specifically, APIT is applied for sensor nodes’ localization based on the received signal strength (RSS) from observer nodes as APIT doesn’t require special hardware or additional devices to estimate node positions. Besides, Differential Privacy technique is utilized to protect the privacy of observer nodes’ location information (i.e., nodes’ identity, location, and transmission power). Additionally, we mitigate Sybil attacks using a novel lightweight Sybil detection approach introduced in this paper that is based on the twice difference of the perceived RSS. Simulation results demonstrate that PrSLoc can efficiently eliminate Sybil nodes while preserving the observers’ privacy during wireless localization processes. Furthermore, the computation overhead and storage cost of the proposed PrSLoc are competitive compared with existing work. Our code can be found here: https://github.com/learning-lemon/PrSLoc-Sybil-Attack-Detection-for-Localization-with-Private-Observers-using-Differential-Privacy.git.

A novel Sybil attack detection scheme in mobile IoT based on collaborate edge computing

BackgroundInternet 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.ResultsA 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.