Sybil Nodes Research Articles

Enhancing Security in Social Networks through Machine Learning: Detecting and Mitigating Sybil Attacks with SybilSocNet

This study contributes to the Sybil node-detecting algorithm in online social networks (OSNs). As major communication platforms, online social networks are significantly guarded from malicious activity. A thorough literature review identified various detection and prevention Sybil attack algorithms. An additional exploration of distinct reputation systems and their practical applications led to this study’s discovery of machine learning algorithms, i.e., the KNN, support vector machine, and random forest algorithms, as part of our SybilSocNet. This study details the data-cleansing process for the employed dataset for optimizing the computational demands required to train machine learning algorithms, achieved through dataset partitioning. Such a process led to an explanation and analysis of our conducted experiments and comparing their results. The experiments demonstrated the algorithm’s ability to detect Sybil nodes in OSNs (99.9% accuracy in SVM, 99.6% in random forest, and 97% in KNN algorithms), and we propose future research opportunities.

Decentralized Distance-based Strategy for Detection of Sybil Attackers and Sybil Nodes in VANET

Decentralized Distance-based Strategy for Detection of Sybil Attackers and Sybil Nodes in VANET

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

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.

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.

Evaluating multidimensional RSSI-based approaches for identifying Sybil nodes in VANETs to improve vehicle network security

Evaluating multidimensional RSSI-based approaches for identifying Sybil nodes in VANETs to improve vehicle network security

SBPA: Sybil-Based Backdoor Poisoning Attacks for Distributed Big Data in AIoT-Based Federated Learning System

Federated learning (FL) enables a great deal of distributed independent participants to collaborate in training without sharing data. Malicious adversary can poison the local model by backdoor poisoning attacks and utilize the characteristic that server cannot trace original data to make the poisoned model directly aggregated. Especially in the AIoT-FL network that generates large amounts of data in real-time, such an attack is more powerful. In this paper, we design a sybil-based backdoor poisoning attacks (SBPA) against the above vulnerability. Malicious participants inject backdoor triggers into distributed Big Data to covertly complete data poisoning. During subsequent iterative aggregation, the joint model activates the backdoor during testing to achieve misclassification for the backdoor images. Besides, malicious participants create some sybil nodes to join the aggregation by taking advantage of the vulnerability of system devices to be easily disconnected. They are committed to making the poisoned local models aggregated with higher probability. Their goal works on making the final global model misclassify backdoor images while keeping high classification accuracy on the other non-backdoor samples. We conduct extensive experiments on multiple datasets and exhibit more robust performance than the state-of-the-art in various metrics under both data distribution including i.i.d. and non-i.i.d. scenarios.

A novel scheme based on coarse‐grained localization and fine‐grained isolation for defending against Sybil attack in low power and lossy networks

AbstractDue to the lack of security mechanisms and sensor resources, the IPv6 Routing Protocol for Low power and Lossy networks (RPL) is vulnerable to various attacks, among which Sybil attack has received a lot of attention. This paper proposes a novel scheme based on coarse‐grained localization and fine‐grained isolation (CLFI) for defending Sybil attack. In CLFI, normal neighbor nodes are identified based on received signal strength indicator (RSSI). To further identify Sybil nodes, a physical address identifier called is defined, which is calculated as the difference of RSSI at two different receivers when they are receiving the same packet. In order to facilitate the calculation of , a set of anchor nodes are deployed to broadcast the received RPL control packets. Then Sybil behaviors can be detected by comparing the physical address identifier with the logical address embedded in the received packets. The proposed CLFI scheme has been completely implemented in the Contiki operating system. The extensive evaluation in comparison with the state‐of‐the‐art scheme indicates that CLFI can quickly localize and isolate Sybil nodes.

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.

BFT-IoMT: A Blockchain-Based Trust Mechanism to Mitigate Sybil Attack Using Fuzzy Logic in the Internet of Medical Things.

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.

SCA: Sybil-Based Collusion Attacks of IIoT Data Poisoning in Federated Learning

With the massive amounts of data generated by industrial Internet of Things (IIoT) devices at all moments, federated learning (FL) enables these distributed distrusted devices to collaborate to build machine learning model while maintaining data privacy. However, malicious participants still launch malicious attacks against the security vulnerabilities during model aggregation. This article is the first to propose Sybil-based collusion attacks (SCA) in the IIoT-FL system for the vulnerabilities mentioned above. The malicious participants use label flipping attacks to complete local poisoning training. Meanwhile, they can virtualize multiple Sybil nodes to make the local poisoning models aggregated with the greatest possibility during aggregation. They focus on making the joint model misclassify the selected attack class samples during the testing phase, while other nonattack classes kept the main task accuracy similar to the nonpoisoned state. Exhaustive experimental analysis demonstrates that our SCA has a superior performance on multiple aspects than the state-of-the-art.

Detection method to eliminate Sybil attacks in Vehicular Ad-hoc Networks

A Sybil attack is caused by a malicious vehicle node stealing fake identities and continuously generating fake vehicles on the road to create the illusion of congestion, which endangers normal vehicles on the road. Because Sybil vehicle nodes have trajectories and motion states similar to those of normal vehicles, they are more difficult to detect in high-density traffic environments. The real-time authentication of vehicles is impossible in the existing traffic environment; thus, malicious vehicle nodes with a high degree of stealth can continuously attack and are difficult to stop. In this paper, we propose a Sybil attack detection method based on basic security message (BSM) packets, which exploits the characteristic that BSM packets have a unique sending source and uses the spatiotemporal relationship of dynamic vehicle location changes to detect and trace Sybil attacks. A weighted integration strategy is proposed for increasing the detection precision without machine-learning model prediction. Experimental results indicated that the proposed method can detect Sybil attacks in real time and is not affected by the attack density or traffic density. Moreover, it can detect Sybil nodes and trace malicious nodes simultaneously, with precisions of ¿98% and ¿94%, respectively, resolving the difficulties of existing detection schemes.

Sybil Attack Detection in VANET Using Machine Learning Approach

VANET (Vehicular Ad-hoc Network) is a subclass of MANET in which many cars can connect with one another via node to node or equipment erected on the side of the road. However, due to the adaptability of centres and the unexpected trade in geography, there may be opportunities for attacks in VANET. One of the ostensible assaults is the Sybil attack, in which the attacker fabricates unequivocally unique equal personalities to undermine the value of VANET. Sybil creates fictitious identities inside the community as well in order to sabotage attempts to mediate conversations between community nodes. Sybil assaults have an impact on carrier transportation in relation to things like traffic congestion, road safety, and multimedia entertainment. VANETs therefore announce a security mechanism to protect you from Sybil attacks. In this regard, this work puts forth the SDTC method, which completely relies on machine learning techniques to prevent Sybil assaults in VANETs. In order to reduce identification time, increase detection accuracy, and enhance scalability, the SDTC (Sybil node detecting the use of Classification) mechanism uses a few vehicle-specific Extreme Learning Machine (ELM) features. The results suggest that SDTC is a suitable strategy to reduce Sybil assaults and sustain provider service in VANETs.

THC-RPL: A lightweight Trust-enabled routing in RPL-based IoT networks against Sybil attack.

The Internet of Things (IoT) and its relevant advances have attracted significant scholarly, governmental, and industrial attention in recent years. Since the IoT specifications are quite different from what the Internet can deliver today, many groundbreaking techniques, such as Mobile Ad hoc Networks (MANETs) and Wireless Sensor Networks (WSN), have gradually been integrated into IoT. The Routing Protocol for Low power and Lossy network (RPL) is the de-facto IoT routing protocol in such networks. Unfortunately, it is susceptible to numerous internal attacks. Many techniques, such as cryptography, Intrusion Detection System (IDS), and authorization have been used to counter this. The large computational overhead of these techniques limits their direct application to IoT nodes, especially due to their low power and lossy nature. Therefore, this paper proposes a Trust-based Hybrid Cooperative RPL protocol (THC-RPL) to detect malicious Sybil nodes in an RPL-based IoT network. The proposed technique is compared and evaluated with state-of-the-art and is found to outperform them. It detects more attacks while maintaining the packet loss ratio in the range of 15-25%. The average energy consumption of the nodes also remains in the ratio of 60-80 mj. There is approximately 40% more energy conservation at node level with an overall 50% increase in network lifetime. THC-RPL has 10% less message exchange and 0% storage costs.

SybilHunter: Hybrid graph-based sybil detection by aggregating user behaviors

Nowadays, online social networks (OSNs) support users in sharing their information and providing valuable data for various applications, e.g., rating and recommendation systems, search engine systems, etc. In such online social network-based applications, information quality is essential. However, the easy-to-use interactive user interfaces and free publication mechanisms facilitate malicious users to launch Sybil Attacks. They create fake identities, pollute user-generated content, and cause server information quality problems, such as buzz, rumor, spam, etc. Most existing graph-based sybil detection approaches only consider the static graph structure features and heavily rely on the prior knowledge of labeled nodes. “Limited-attack-edge” assumption, the critical assumption of these approaches has been proved invalid in many scenarios.In this paper, we propose SybilHunter, a hybrid graph-based sybil detection approach by aggregating user social behavior patterns. Our approach refines the OSN structure, quantifies nodes’ similarity according to the dynamic user behavior features to evaluate user pairs’ trustworthiness and consistency. Then it constructs a weighted-strong-social (WSS) graph, based on which SybilHunter outputs sybil nodes. We simulate and evaluate our approach under a Weibo dataset. The AUC of SybilHunter achieves 0.945, which is significantly higher than typical graph-based sybil detection methods, such as SybilRank, SybilWalk, etc.

Physical Identification Based Trust Path Routing Against Sybil Attacks on RPL in IoT Networks

Routing protocol for low power and lossy networks (RPL) is a popular protocol used in IPv6 based wireless Internet of Things (IoT) networks. Wireless IoT devices using RPL without centralized security management are extremely vulnerable to Sybil attacks, as a malicious node can forge multiple identities and easily conduct denial-of-service (DoS) attacks. Several countermeasures have been investigated to prevent DoS caused by Sybil attacks in RPL, but crucial limitations still remain. In this letter, a physical identification based trust path routing (PITrust) scheme that uses the received signal strength indicator (RSSI) and a centralized trust scheme to improve Sybil node detection is proposed, which is shown to improve the detection rate and packet delivery ratio when compared to other schemes.

Resilient Consensus for Multi-Agent Systems in the Presence of Sybil Attacks

This paper investigates the problem of resilient consensus control for discrete-time linear multi-agent systems under Sybil attacks. We consider a node to be a Sybil node if it can generate a large number of false identities in the graph as a way of gaining disproportionate influence on the consensus performance of the network. Such attacks can easily invalidate existing resilient consensus algorithms that assume an upper bound on the number of malicious nodes in the network. To this end, we first built a new attack model based on the characteristics of the Sybil nodes. In addition, a quantized-data-based transmission scheme was developed for identifying and resisting Sybil nodes in the network. Then, an attack-resilient consensus algorithm was developed, where each normal node sends the quantitative data information with a specific label, which is generated by truncated normal distribution sampling to their neighbors. We give sufficient graphical conditions for attack models considering limited energy to ensure the consensus of linear multi-agent systems. Finally, numerical simulation examples are provided to validate the effectiveness of the proposed methods.

Preventing active re-identification attacks on social graphs via sybil subgraph obfuscation

Active re-identification attacks constitute a serious threat to privacy-preserving social graph publication, because of the ability of active adversaries to leverage fake accounts, a.k.a. sybil nodes, to enforce structural patterns that can be used to re-identify their victims on anonymised graphs. Several formal privacy properties have been enunciated with the purpose of characterising the resistance of a graph against active attacks. However, anonymisation methods devised on the basis of these properties have so far been able to address only restricted special cases, where the adversaries are assumed to leverage a very small number of sybil nodes. In this paper, we present a new probabilistic interpretation of active re-identification attacks on social graphs. Unlike the aforementioned privacy properties, which model the protection from active adversaries as the task of making victim nodes indistinguishable in terms of their fingerprints with respect to all potential attackers, our new formulation introduces a more complete view, where the attack is countered by jointly preventing the attacker from retrieving the set of sybil nodes, and from using these sybil nodes for re-identifying the victims. Under the new formulation, we show that k-symmetry, a privacy property introduced in the context of passive attacks, provides a sufficient condition for the protection against active re-identification attacks leveraging an arbitrary number of sybil nodes. Moreover, we show that the algorithm K-Match, originally devised for efficiently enforcing the related notion of k-automorphism, also guarantees k-symmetry. Empirical results on real-life and synthetic graphs demonstrate that our formulation allows, for the first time, to publish anonymised social graphs (with formal privacy guarantees) that effectively resist the strongest active re-identification attack reported in the literature, even when it leverages a large number of sybil nodes.

User behavior-based and graph-based hybrid approach for detection of Sybil Attack in online social networks

The immense popularity of social networks has rendered them vulnerable to security threats such as Sybil attacks. In Sybil attacks, attackers send several friend requests from fake identities, and some of the genuine users might accept them unknowingly and become victims of Sybil attacks. Once genuine users accept friend requests from Sybil identities, attackers can spam, phish, and conduct other harmful actions in the target networks. Though genuine users have links to Sybil users unknowingly, they do less interaction and have low strength of relationship with them. Thus, the relationship strength measure can be useful to detect a Sybil attack. Considering the relationship strength measure, we propose a behavior-based and graph-based hybrid approach to detect a Sybil attack in Online Social Networks (OSNs). We identify the behavior features, use them to measure the relationship strength, and identify attack edges. We use the graph-based feature(betweenness-centrality) to leverage attack-edge identification and detect Sybil nodes. We have evaluated our scheme using real-world datasets, and experimental results validate the proposed scheme’s effectiveness.

TMTACS: Two-Tier Multi-Trust-Based Algorithm to Countermeasure the燬ybil Attacks

Mobile Ad hoc Networks (MANETs) have always been vulnerable to Sybil attacks in which users create fake nodes to trick the system into thinking they’re authentic. These fake nodes need to be detected and deactivated for security reasons, to avoid harming the data collected by various applications. The MANET is an emerging field that promotes trust management among devices. Transparency is becoming more essential in the communication process, which is why clear and honest communication strategies are needed. Trust Management allows for MANET devices with different security protocols to connect. If a device finds difficulty in sending a message to the destination, the purpose of the communication process won’t be achieved and this would disappoint both that device and all of your devices in general. This paper presents, the Two-Tier Multi-Trust based Algorithm for Preventing Sybil Attacks in MANETs (TMTACS). The TMTACS provides a two-tier security mechanism that can grant or revoke trust in the Nodes of the MANET. It’s a smart way to identify Sybil nodes in the system. A proficient cluster head selection algorithm is also defined, which selects cluster head efficiently and does load balancing to avoid resource consumption from a single node only. Also, for routing efficient path is selected to deteriorate energy consumption and maximize throughput. The recent technique is compared with Secured QoS aware Energy Efficient Routing (SQEER), Adaptive Trust-Based Routing Protocol (ATRP), and Secure Trust-Aware Energy-Efficient Adaptive Routing (STEAR) in terms of Packet Delivery Ratio (PDR), consumption of energy etc. The simulation was performed on MATrix LABoratory (MATLAB) and the results achieved by the present scheme are better than existing techniques.