Biblio

In most PUF-based authentication schemes, a central server is usually engaged to verify the response of the device’s PUF to challenge bit-streams. However, the server availability may be intermittent in practice. To tackle such an issue, this paper proposes a new protocol for supporting distributed authentication while avoiding vulnerability to information leakage where CRPs could be retrieved from hacked devices and collectively used to model the PUF. The main idea is to provision for scrambling the challenge bit-stream in a way that is dependent on the verifier. The scrambling pattern varies per authentication round for each device and independently across devices. In essence, the scrambling function becomes node- and packetspecific and the response received by two verifiers of one device for the same challenge bit-stream could vary. Thus, neither the scrambling function can be reverted, nor the PUF can be modeled even by a collusive set of malicious nodes. The validation results using data of an FPGA-based implementation demonstrate the effectiveness of our approach in thwarting PUF modeling attacks by collusive actors. We also discuss the approach resiliency against impersonation, Sybil, and reverse engineering attacks.

Vehicular crowdsensing (VCS) is a subset of crowd-sensing where data collection is outsourced to group vehicles. Here, an entity interested in collecting data from a set of Places of Sensing Interest (PsI), advertises a set of sensing tasks, and the associated rewards. Vehicles attracted by the offered rewards deviate from their ongoing trajectories to visit and collect from one or more PsI. In this win-to-win scenario, vehicles reach their final destination with the extra reward, and the entity obtains the desired samples. Unfortunately, the efficiency of VCS can be undermined by the Sybil attack, in which an attacker can benefit from the injection of false vehicle identities. In this paper, we present a case study and analyze the effects of such an attack. We also propose a defense mechanism based on generative adversarial neural networks (GANs). We discuss GANs' advantages, and drawbacks in the context of VCS, and new trends in GANs' training that make them suitable for VCS.

As a result of the inherent weaknesses of the wireless medium, ad hoc networks are susceptible to a broad variety of threats and assaults. As a direct consequence of this, intrusion detection, as well as security, privacy, and authentication in ad-hoc networks, have developed into a primary focus of current study. This body of research aims to identify the dangers posed by a variety of assaults that are often seen in wireless ad-hoc networks and provide strategies to counteract those dangers. The Black hole assault, Wormhole attack, Selective Forwarding attack, Sybil attack, and Denial-of-Service attack are the specific topics covered in this thesis. In this paper, we describe a trust-based safe routing protocol with the goal of mitigating the interference of black hole nodes in the course of routing in mobile ad-hoc networks. The overall performance of the network is negatively impacted when there are black hole nodes in the route that routing takes. As a result, we have developed a routing protocol that reduces the likelihood that packets would be lost as a result of black hole nodes. This routing system has been subjected to experimental testing in order to guarantee that the most secure path will be selected for the delivery of packets between a source and a destination. The invasion of wormholes into a wireless network results in the segmentation of the network as well as a disorder in the routing. As a result, we provide an effective approach for locating wormholes by using ordinal multi-dimensional scaling and round trip duration in wireless ad hoc networks with either sparse or dense topologies. Wormholes that are linked by both short route and long path wormhole linkages may be found using the approach that was given. In order to guarantee that this ad hoc network does not include any wormholes that go unnoticed, this method is subjected to experimental testing. In order to fight against selective forwarding attacks in wireless ad-hoc networks, we have developed three different techniques. The first method is an incentive-based algorithm that makes use of a reward-punishment system to drive cooperation among three nodes for the purpose of vi forwarding messages in crowded ad-hoc networks. A unique adversarial model has been developed by our team, and inside it, three distinct types of nodes and the activities they participate in are specified. We have shown that the suggested strategy that is based on incentives prohibits nodes from adopting an individualistic behaviour, which ensures collaboration in the process of packet forwarding. To guarantee that intermediate nodes in resource-constrained ad-hoc networks accurately convey packets, the second approach proposes a game theoretic model that uses non-cooperative game theory. This model is based on the idea that game theory may be used. This game reaches a condition of desired equilibrium, which assures that cooperation in multi-hop communication is physically possible, and it is this state that is discovered. In the third algorithm, we present a detection approach that locates malicious nodes in multihop hierarchical ad-hoc networks by employing binary search and control packets. We have shown that the cluster head is capable of accurately identifying the malicious node by analysing the sequences of packets that are dropped along the path leading from a source node to the cluster head. A lightweight symmetric encryption technique that uses Binary Playfair is presented here as a means of safeguarding the transport of data. We demonstrate via experimentation that the suggested encryption method is efficient with regard to the amount of energy used, the amount of time required for encryption, and the memory overhead. This lightweight encryption technique is used in clustered wireless ad-hoc networks to reduce the likelihood of a sybil attack occurring in such networks

Smart cities are a wide range of projects made to facilitate the problems of everyday life and ensure security. Our interest focuses only on the Intelligent Transport System (ITS) that takes care of the transportation issues using the Vehicular Ad-Hoc Network (VANET) paradigm as its base. VANETs are a promising technology for autonomous driving that provides many benefits to the user conveniences to improve road safety and driving comfort. VANET is a promising technology for autonomous driving that provides many benefits to the user's conveniences by improving road safety and driving comfort. The problem with such rapid development is the continuously increasing digital threats. Among all these threats, we will target the Sybil attack since it has been proved to be one of the most dangerous attacks in VANETs. It allows the attacker to generate multiple forged identities to disseminate numerous false messages, disrupt safety-related services, or misuse the systems. In addition, Machine Learning (ML) is showing a significant influence on classification problems, thus we propose a behavior-based classification algorithm that is tested on the provided VeReMi dataset coupled with various machine learning techniques for comparison. The simulation results prove the ability of our proposed mechanism to detect the Sybil attack in VANETs.

The intrusion detection systems are vital for the sustainability of Cooperative Intelligent Transportation Systems (C-ITS) and the detection of sybil attacks are particularly challenging. In this work, we propose a novel approach for the detection of sybil attacks in C-ITS environments. We provide an evaluation of our approach using extensive simulations that rely on real traces, showing our detection approach's effectiveness.

Any decentralized, biased distributed network is susceptible to the Sybil malicious attack, in which a malicious node masquerades as numerous different nodes, collectively referred to as Sybil nodes, causing the network to become unresponsive. Cloud computing environments are characterized by their loosely linked nature, which means that no node has comprehensive information of the entire system. In order to prevent Sybil attacks in cloud computing systems, it is necessary to detect them as soon as they occur. The network’s ability to function properly A Sybil attacker has the ability to construct. It is necessary to have multiple identities on a single physical device in order to execute a concerted attack on the network or switch between networks identities in order to make the detection process more difficult, and thereby lack of accountability is being promoted throughout the network. The purpose of this study is to Various varieties of Sybil assaults have been documented, including those that occur in Peer-to-peer reputation systems, self-organizing networks, and other similar technologies. The topic of social network systems is discussed. In addition, there are other approaches in which it has been urged over time that they be reduced or eliminated Their potential risks are also thoroughly investigated.

Sybil attack is one of the most dangerous internal attacks in Vehicular Ad Hoc Network (VANET). It affects the function of the VANET network by maliciously claiming or stealing multiple identity propagation error messages. In order to prevent VANET from Sybil attacks, many solutions have been proposed. However, the existing solutions are specific to the physical or application layer's single-level data and lack research on cross-layer information fusion detection. Moreover, these schemes involve a large number of sensitive data access and transmission, do not consider users' privacy, and can also bring a severe communication burden, which will make these schemes unable to be actually implemented. In this context, this paper introduces FedMix, the first federated Sybil attack detection system that considers cross-layer information fusion and provides privacy protection. The system can integrate VANET physical layer data and application layer data for joint analyses simultaneously. The data resides locally in the vehicle for local training. Then, the central agency only aggregates the generated model and finally distributes it to the vehicles for attack detection. This process does not involve transmitting and accessing any vehicle's original data. Meanwhile, we also designed a new model aggregation algorithm called SFedAvg to solve the problems of unbalanced vehicle data quality and low aggregation efficiency. Experiments show that FedMix can provide an intelligent model with equivalent performance under the premise of privacy protection and significantly reduce communication overhead, compared with the traditional centralized training attack detection model. In addition, the SFedAvg algorithm and cross-layer information fusion bring better aggregation efficiency and detection performance, respectively.

While vehicle-to-everything communication technology enables information sharing and cooperative control for vehicles, it also poses a significant threat to the vehicles' driving security owing to cyber-attacks. In particular, Sybil malicious attacks hidden in the vehicle broadcast information flow are challenging to detect, thereby becoming an urgent issue requiring attention. Several researchers have considered this problem and proposed different detection schemes. However, the detection performance of existing schemes based on plausibility checks and neighboring observers is affected by the traffic and attacker densities. In this study, we propose a malicious attack detection scheme based on traffic-flow information fusion, which enables the detection of Sybil attacks without neighboring observer nodes. Our solution is based on the basic safety message, which is broadcast by vehicles periodically. It first constructs the basic features of traffic flow to reflect the traffic state, subsequently fuses it with the road detector information to add the road fusion features, and then classifies them using machine learning algorithms to identify malicious attacks. The experimental results demonstrate that our scheme achieves the detection of Sybil attacks with an accuracy greater than 90 % at different traffic and attacker densities. Our solutions provide security for achieving a usable vehicle communication network.

Vehicular Fog Computing (VFC) has been proposed to address the security and response time issues of Vehicular Ad Hoc Networks (VANETs) in latency-sensitive vehicular network environments, due to the frequent interactions that VANETs need to have with cloud servers. However, the anonymity protection mechanism in VFC may cause the attacker to launch Sybil attacks by fabricating or creating multiple pseudonyms to spread false information in the network, which poses a severe security threat to the vehicle driving. Therefore, in this paper, we summarize different types of Sybil attack detection mechanisms in VFC for the first time, and provide a comprehensive comparison of these schemes. In addition, we also summarize the possible impacts of different types of Sybil attacks on VFC. Finally, we summarize challenges and prospects of future research on Sybil attack detection mechanisms in VFC.

Metaverse technologies depend on various advanced human-computer interaction (HCI) devices to be supported by extended reality (XR) technology. Many new HCI devices are supported by wireless Internet of Things (IoT) networks, where a reliable routing scheme is essential for seamless data trans-mission. Routing Protocol for Low power and Lossy networks (RPL) is a key routing technology used in IPv6-based low power and lossy networks (LLNs). However, in the networks that are configured, such as small wireless devices applying the IEEE 802.15.4 standards, due to the lack of a system that manages the identity (ID) at the center, the maliciously compromised nodes can make fabricated IDs and pretend to be a legitimate node. This behavior is called Sybil attack, which is very difficult to respond to since attackers use multiple fabricated IDs which are legally disguised. In this paper, Sybil attack countermeasures on RPL-based networks published in recent studies are compared and limitations are analyzed through simulation performance analysis.

The emerging vehicular edge computing (VEC) technology has the potential to bring revolutionary development to vehicular ad hoc network (VANET). However, the edge computing servers (ECSs) are subjected to a variety of security threats. One of the most dangerous types of security attacks is the Sybil attack, which can create fabricated virtual vehicles (called Sybil vehicles) to significantly overload ECSs' limited computation resources and thus disrupt legitimate vehicles' edge computing applications. In this paper, we present a novel Sybil attack detection system on ECSs that is based on the design of a credibility enhanced temporal graph convolutional network. Our approach can identify the malicious vehicles in a dynamic traffic environment while preserving the legitimate vehicles' privacy, particularly their local position information. We evaluate our proposed approach in the SUMO simulator. The results demonstrate that our proposed detection system can accurately identify most Sybil vehicles while maintaining a low error rate.

Underwater Wireless Sensor Networks (UWSN) has vast application areas. Due to the unprotected nature, underwater security is a prime concern. UWSN becomes vulnerable to different attacks due to malicious nodes. Sybil attack is one of the major attacks in UWSN. Most of the proposed security methods are based on encryption and decryption which consumes resources of the sensor nodes. In this paper, a simple authentication mechanism is proposed for securing the UWSN from the Sybil attack. As the nodes have very less computation power and energy resources so this work is not followed any kind of encryption and decryption technique. An authentication process is designed in such a way that node engaged in communication authenticate neighboring nodes by node ID and the data stored in the cluster head. This work is also addressed sensor node compromisation issue through Hierarchical Fuzzy System (HFS) based trust management model. The trust management model has been simulated in Xfuzzy-3.5. After the simulation conducted, the proposed trust management mechanism depicts significant performance on detecting compromised nodes.

Physical layer authentication (PLA) has recently been discussed in the context of URLLC due to its low complexity and low overhead. Nevertheless, these schemes also introduce additional sources of error through missed detections and false alarms. The trade-offs of these characteristics are strongly dependent on the deployment scenario as well as the processing architecture. Thus, considering a feature-based PLA scheme utilizing channel-state information at multiple distributed radio-heads, we study these trade-offs analytically. We model and analyze different scenarios of centralized and decentralized decision-making and decoding, as well as the impacts of a single-antenna attacker launching a Sybil attack. Based on stochastic network calculus, we provide worst-case performance bounds on the system-level delay for the considered distributed scenarios under a Sybil attack. Results show that the arrival-rate capacity for a given latency deadline is increased for the distributed scenarios. For a clustered sensor deployment, we find that the distributed approach provides 23% higher capacity when compared to the centralized scenario.

Underwater wireless sensor network(UWSN) is an emerging technology for exploring and research inside the ocean. Since it is somehow similar to the normal wireless network, which uses radio signals for communication purposes, while UWSN uses acoustic for communication between nodes inside the ocean and sink nodes. Due to unattended areas and the vulnerability of acoustic medium, UWNS are more prone to various malicious attacks like Sybil attack, Black-hole attack, Wormhole attack, etc. This paper analyzes blackhole attacks in UWSN and proposes an algorithm to mitigate blackhole attacks by forming clusters of nodes and selecting coordinator nodes from each cluster to identify the presence of blackholes in its cluster. We used public-key cryptography and the challenge-response method to authenticate and verify nodes.

In federated learning, machine learning and deep learning models are trained globally on distributed devices. The state-of-the-art privacy-preserving technique in the context of federated learning is user-level differential privacy. However, such a mechanism is vulnerable to some specific model poisoning attacks such as Sybil attacks. A malicious adversary could create multiple fake clients or collude compromised devices in Sybil attacks to mount direct model updates manipulation. Recent works on novel defense against model poisoning attacks are difficult to detect Sybil attacks when differential privacy is utilized, as it masks clients' model updates with perturbation. In this work, we implement the first Sybil attacks on differential privacy based federated learning architectures and show their impacts on model convergence. We randomly compromise some clients by manipulating different noise levels reflected by the local privacy budget ε of differential privacy with Laplace mechanism on the local model updates of these Sybil clients. As a result, the global model convergence rates decrease or even leads to divergence. We apply our attacks to two recent aggregation defense mechanisms, called Krum and Trimmed Mean. Our evaluation results on the MNIST and CIFAR-10 datasets show that our attacks effectively slow down the convergence of the global models. We then propose a method to keep monitoring the average loss of all participants in each round for convergence anomaly detection and defend our Sybil attacks based on the training loss reported from randomly selected sets of clients as the judging panels. Our empirical study demonstrates that our defense effectively mitigates the impact of our Sybil attacks.

The Internet of Vehicles (IoV) is a network that considers vehicles as intelligent machines. They interact and communicate with each other to improve the performance and safety of traffic. IoV solves certain problems, but it has some issues such as response time, which prompted researchers to propose the integration of Fog Computing into vehicular networks. In Vehicular Fog Computing (VFC), the services are provided at the edge of the network to increase data rate and reduce response time. However, in order to satisfy network users, the security and privacy of sensitive data should be guaranteed. Using pseudonyms instead of real identities is one of the techniques considered to preserve the privacy of users, however, this can push malicious vehicles to exploit such a process and launch the Sybil attack by creating several pseudonyms in order to perform various malicious activities. In this paper, we describe the Sybil attack effects on VFC networks and compare them to those in conventional networks, as well as identify the various existing methods for detecting this attack and determine if they are applicable to VFC networks.

In present scenario features like low-cost, power-efficientand easy-to-implement Wireless Sensor Networks (WSN’s) has become one of growing prospects.though, its security issues have become a popular topic of research nowadays. Specific attacks often experience the security issues as they easily combined with other attacks to destroy the network. In this paper, we discuss about detecting the particular attacks like Sybil, Black-holeand Denial of Service (DoS) attacks on WSNs. These networks are more vulnerable to them. We attempt to investigate the security measures and the applicability of the AODV protocol to detect and manage specific types of network attacks in VANET.The RSA algorithm is proposed here, as it is capable of detecting sensor nodes ormessages transmitted from sensor nodes to the base station and prevents network from being attacked by the source node. It also improves the security mechanism of the AODV protocol. This simulation set up is performed using MATLAB simulation tool

Mobile Social Networks have become one of the most convenient services for users to share information everywhere. This crowdsourced information is often meaningful and recommended to users, e.g., reviews on Yelp or high marks on Dianping, which poses the threat of Sybil attacks. To address the problem of Sybil attacks, previous solutions mostly use indirect/direct graph model or clickstream model to detect fake accounts. However, they are either dependent on strong connections or solely preserved by servers of social networks. In this paper, we propose a novel predictable approach by exploiting users' custom patterns to distinguish Sybil attackers from normal users for the application of recommendation in mobile social networks. First, we introduce the entropy of spatial-temporal features to profile the mobility traces of normal users, which is quite different from Sybil attackers. Second, we develop discriminative entropy-based features, i.e., users' preference features, to measure the uncertainty of users' behaviors. Third, we design a smart Sybil detection model based on a binary classification approach by combining our entropy-based features with traditional behavior-based features. Finally, we examine our model and carry out extensive experiments on a real-world dataset from Dianping. Our results have demonstrated that the model can significantly improve the detection accuracy of Sybil attacks.

Underwater networks have the potential to enable unexplored applications and to enhance our ability to observe and predict the ocean. Underwater acoustic sensor networks (UASNs) are often deployed in unprecedented and hostile waters and face many security threats. Applications based on UASNs such as coastal defense, pollution monitoring, assisted navigation to name a few, require secure communication. A new set of communication protocols and cooperative coordination algorithms have been proposed to enable collaborative monitoring tasks. However, such protocols overlook security as a key performance indicator. Spoofing, altering, or replaying routing information can affect the entire network, making UASN vulnerable to routing attacks such as selective forwarding, sinkhole attack, Sybil attack, acknowledgement spoofing and HELLO flood attack. The lack of security against such threats is startling if maintained that security is indeed an important requirement in many emerging civilian and military applications. In this work, we look at one of the most prevalent attacks among UASNs which is Sybill attack and discuss mitigation approaches for it. Then, feasibly implemented the attack in UnetStack3 to simulate real-life scenario.

Security is the main concern for wireless sensor nodes and exposed against malicious attacks. To secure the communication between sensor nodes several key managing arrangements are already implemented. The key managing method for any protected application must minimally deliver safety facilities such as truthfulness. Diffie–Hellman key exchange in the absence of authentication is exposed to MITM (man-in-the-middle) attacks due to which the attacker node can easily interrupt the communication, by appearing as a valid node in the network. In wireless sensor networks, single path routing is very common but it suffers with the two problems i:e link failure which results in data loss and if any node in single path is compromised, there is no alternative to send the data to the destination securely. To overcome this problem, multipath routing protocol is used which provides both availability and consistency of data. AOMDV (Ad-hoc On-demand Multipath Distance Vector Routing Protocol) is used in a proposed algorithm which provides alternative paths to reach the data packets to the destination. This paper presents an algorithm DH-SAM (Diffie-Hellman- Sybil Attack Mitigation) to spot and mitigate Sybil nodes and make the network trusted with the objective of solving the issue of MITM attack in the network. After node authentication, secure keys are established between two communicating nodes for data transmission using the Diffie-Hellman algorithm. Performance evaluation of DH-SAM is done by using different metrics such as detection rate, PDR, throughput, and average end to end (AE2E) delay.

Sybil attacks are one of the most prominent security problems of trust mechanisms in a distributed network with a large number of highly dynamic and heterogeneous devices, which expose serious threat to edge computing based distributed systems. Graphbased Sybil detection approaches extract social structures from target distributed systems, refine the graph via preprocessing methods and capture Sybil nodes based on the specific properties of the refined graph structure. Graph preprocessing is a critical component in such Sybil detection methods, and intuitively, the processing methods will affect the detection performance. Thoroughly understanding the dependency on the graph-processing methods is very important to develop and deploy Sybil detection approaches. In this paper, we design experiments and conduct systematic analysis on graph-based Sybil detection with respect to different graph preprocessing methods on selected network environments. The experiment results disclose the sensitivity caused by different graph transformations on accuracy and robustness of Sybil detection methods.

Vehicular networks are vulnerable to a variety of internal attacks. Misbehavior Detection Systems (MDS) are preferred over the cryptography solutions to detect such attacks. However, the existing misbehavior detection systems are static and do not adapt to the context of vehicles. To this end, we exploit the Software-Defined Networking (SDN) paradigm to propose a context-aware MDS. Based on the context, our proposed system can tune security parameters to provide accurate detection with low false positives. Our system is Sybil attack-resistant and compliant with vehicular privacy standards. The simulation results show that, under different contexts, our system provides a high detection ratio and low false positives compared to a static MDS.

Many blockchain applications use decentralized oracles to trustlessly retrieve external information as those platforms are agnostic to real-world information. Some existing decentralized oracle protocols make use of majority-voting schemes to determine the outcomes and/or rewards to participants. In these cases, the awards (or penalties) grow linearly to the participant stakes, therefore voters are indifferent between voting through a single or multiple identities. Furthermore, the voters receive a reward only when they agree with the majority outcome, a tactic that may lead to herd behavior. This paper proposes an oracle protocol based on peer prediction mechanisms with non-linear staking rules. In the proposed approach, instead of being rewarded when agreeing with a majority outcome, a voter receives awards when their report achieves a relatively high score based on a peer prediction scoring scheme. The scoring scheme is designed to be incentive compatible so that the maximized expected score is achieved only with honest reporting. A non-linear stake scaling rule is proposed to discourage Sybil attacks. This paper also provides a theoretical analysis and guidelines for implementation as reference.

Blockchains and distributed ledgers have brought renewed interest in Byzantine fault-tolerant protocols and decentralized systems, two domains studied for several decades. Recent promising works have in particular proposed to use epidemic protocols to overcome the limitations of popular Blockchain mechanisms, such as proof-of-stake or proof-of-work. These works unfortunately assume a perfect peer-sampling service, immune to malicious attacks, a property that is difficult and costly to achieve. We revisit this fundamental problem in this paper, and propose a novel Byzantine-tolerant peer-sampling service that is resilient to Sybil attacks in open systems by exploiting the underlying structure of wide-area networks.

Dynamic spectrum access (DSA) is a promising platform to solve the spectrum shortage problem, in which auction based mechanisms have been extensively studied due to good spectrum allocation efficiency and fairness. Recently, Sybil attacks were introduced in DSA, and Sybil-proof spectrum auction mechanisms have been proposed, which guarantee that each single secondary user (SU) cannot obtain a higher utility under more than one fictitious identities. However, existing Sybil-poof spectrum auction mechanisms achieve only Sybil-proofness for SUs, but not for primary users (PUs), and simulations show that a cheating PU in those mechanisms can obtain a higher utility by Sybil attacks. In this paper, we propose TSUNAMI, the first Truthful and primary user Sybil-proof aUctioN mechAnisM for onlIne spectrum allocation. Specifically, we compute the opportunity cost of each SU and screen out cost-efficient SUs to participate in spectrum allocation. In addition, we present a bid-independent sorting method and a sequential matching approach to achieve primary user Sybil-proofness and 2-D truthfulness, which means that each SU or PU can gain her maximal utility by bidding with her true valuation of spectrum. We evaluate the performance and validate the desired properties of our proposed mechanism through extensive simulations.