Biblio
Wireless networking opens up many opportunities to facilitate miniaturized robots in collaborative tasks, while the openness of wireless medium exposes robots to the threats of Sybil attackers, who can break the fundamental trust assumption in robotic collaboration by forging a large number of fictitious robots. Recent advances advocate the adoption of bulky multi-antenna systems to passively obtain fine-grained physical layer signatures, rendering them unaffordable to miniaturized robots. To overcome this conundrum, this paper presents ScatterID, a lightweight system that attaches featherlight and batteryless backscatter tags to single-antenna robots to defend against Sybil attacks. Instead of passively "observing" signatures, ScatterID actively "manipulates" multipath propagation by using backscatter tags to intentionally create rich multipath features obtainable to a single-antenna robot. These features are used to construct a distinct profile to detect the real signal source, even when the attacker is mobile and power-scaling. We implement ScatterID on the iRobot Create platform and evaluate it in typical indoor and outdoor environments. The experimental results show that our system achieves a high AUROC of 0.988 and an overall accuracy of 96.4% for identity verification.
Internet of Things (IoT), commonly referred to a physical object connected to network, refers to a paradigm in information technology integrating the advances in terms of sensing, computation and communication to improve the service in daily life. This physical object consists of sensors and actuators that are capable of changing the data to offer the improvement of service quality in daily life. When a data exchange occurs, the exchanged data become sensitive; making them vulnerable to any security attacks, one of which, for example, is Sybil attack. This paper aimed to propose a method of trustworthiness management based upon the authentication and trust value. Once performing the test on three scenarios, the system was found to be capable of detecting the Sybil attack rapidly and accurately. The average of time to detect the Sybil attacks was 9.3287 seconds and the average of time required to detect the intruder object in the system was 18.1029 seconds. The accuracy resulted in each scenario was found 100% indicating that the detection by the system to Sybil attack was 100% accurate.
Securing multi-robot teams against malicious activity is crucial as these systems accelerate towards widespread societal integration. This emerging class of ``physical networks'' requires research into new methods of security that exploit their physical nature. This paper derives a theoretical framework for securing multi-agent consensus against the Sybil attack by using the physical properties of wireless transmissions. Our frame-work uses information extracted from the wireless channels to design a switching signal that stochastically excludes potentially untrustworthy transmissions from the consensus. Intuitively, this amounts to selectively ignoring incoming communications from untrustworthy agents, allowing for consensus to the true average to be recovered with high probability if initiated after a certain observation time T0 that we derive. This work is different from previous work in that it allows for arbitrary malicious node values and is insensitive to the initial topology of the network so long as a connected topology over legitimate nodes in the network is feasible. We show that our algorithm will recover consensus and the true graph over the system of legitimate agents with an error rate that vanishes exponentially with time.
Blockchain technology is useful with the record keeping of digital transactions, IoT, supply chain management etc. However, we have observed that the traditional attacks are possible on blockchain due to lack of robust identity management. We found that Sybil attack can cause severe impact in public/permissionless blockchain, in which an attacker can subvert the blockchain by creating a large number of pseudonymous identities (i.e. Fake user accounts) and push legitimate entities in the minority. Such virtual nodes can act like genuine nodes to create disproportionately large influence on the network. This may lead to several other attacks like DoS, DDoS etc. In this paper, a Sybil attack is demonstrated on a blockchain test bed with its impact on the throughput of the system. We propose a solution directive, in which each node monitors the behavior of other nodes and checks for the nodes which are forwarding the blocks of only particular user. Such nodes are quickly identified, blacklisted and notified to other nodes, and thus the Sybil attack can be restricted. We analyze experimental results of the proposed solution.
In VANET, Sybil nodes generated by attackers cause serious damages to network protocols, resource allocation mechanisms, and reputation models. Other types of attacks can also be launched on the basis of Sybil attack, which bring more threats to VANET. To solve this problem, this paper proposes a Sybil nodes detection method based on RSSI sequence and vehicle driving matrix - RSDM. RSDM evaluates the difference between the RSSI sequence and the driving matrix by dynamic distance matching to detect Sybil nodes. Moreover, RSDM does not rely on VANET infrastructure, neighbor nodes or specific hardware. The experimental results show that RSDM performs well with a higher detection rate and a lower error rate.
The pace of technological development in automotive and transportation has been accelerating rapidly in recent years. Automation of driver assistance systems, autonomous driving, increasing vehicle connectivity and emerging inter-vehicular communication (V2V) are among the most disruptive innovations, the latter of which also raises numerous unprecedented security concerns. This paper is focused on the security of V2V communication in vehicle ad-hoc networks (VANET) with the main goal of identifying realistic attack scenarios and evaluating their impact, as well as possible security countermeasures to thwart the attacks. The evaluation has been done in OMNeT++ simulation environment and the results indicate that common attacks, such as replay attack or message falsification, can be eliminated by utilizing digital signatures and message validation. However, detection and mitigation of advanced attacks such as Sybil attack requires more complex approach. The paper also presents a simple detection method of Sybil nodes based on measuring the signal strength of received messages and maintaining reputation of sending nodes. The evaluation results suggest that the presented method is able to detect Sybil nodes in VANET and contributes to the improvement of traffic flow.
Vehicular networks are susceptible to variety of attacks such as denial of service (DoS) attack, sybil attack and false alert generation attack. Different cryptographic methods have been proposed to protect vehicular networks from these kind of attacks. However, cryptographic methods have been found to be less effective to protect from insider attacks which are generated within the vehicular network system. Misbehavior detection system is found to be more effective to detect and prevent insider attacks. In this paper, we propose a machine learning based misbehavior detection system which is trained using datasets generated through extensive simulation based on realistic vehicular network environment. The simulation results demonstrate that our proposed scheme outperforms previous methods in terms of accurately identifying various misbehavior.
A common tool to defend against Sybil attacks is proof-of-work, whereby computational puzzles are used to limit the number of Sybil participants. Unfortunately, current Sybil defenses require significant computational effort to offset an attack. In particular, good participants must spend computationally at a rate that is proportional to the spending rate of an attacker. In this paper, we present the first Sybil defense algorithm which is asymmetric in the sense that good participants spend at a rate that is asymptotically less than an attacker. In particular, if T is the rate of the attacker's spending, and J is the rate of joining good participants, then our algorithm spends at a rate f O($\surd$(TJ) + J). We provide empirical evidence that our algorithm can be significantly more efficient than previous defenses under various attack scenarios. Additionally, we prove a lower bound showing that our algorithm's spending rate is asymptotically optimal among a large family of algorithms.
A frequent problem of Internet services are Sybil attacks, i.e., malicious users create numerous fake identities for themselves. To avoid this, many services employ obstacles like Captchas to force (potentially malicious) users to invest human attention in creating new identities for the service. However, this only makes it more difficult but not impossible to create fake identities. Sybil attacks are especially encountered as a problem in decentralized systems since no single trust anchor is available to judge new users as honest or malicious. The avoidance of a single centralized trust-anchor, however, is desirable in many cases. As a consequence, various decentralized Sybil detection approaches have been proposed. The most promising ones are based on leveraging the trust relationships embedded within social graphs. While most of these approaches are focusing on detecting large existing groups of Sybil identities, our approach Detasyr instead restricts the creation of numerous Sybil identities. For that, tickets are distributed through the social graph and have to be collected, allowing for decentralized and privacy preserving authorization. Additionally, it offers a proof of authorization to users that are considered to be honest, allowing them to display their authorization towards others.
Cooperative Intelligent Transport Systems (C-ITS) are expected to play an important role in our lives. They will improve the traffic safety and bring about a revolution on the driving experience. However, these benefits are counterbalanced by possible attacks that threaten not only the vehicle's security, but also passengers' lives. One of the most common attacks is the Sybil attack, which is even more dangerous than others because it could be the starting point of many other attacks in C-ITS. This paper proposes a distributed approach allowing the detection of Sybil attacks by using the traffic flow theory. The key idea here is that each vehicle will monitor its neighbourhood in order to detect an eventual Sybil attack. This is achieved by a comparison between the real accurate speed of the vehicle and the one estimated using the V2V communications with vehicles in the vicinity. The estimated speed is derived by using the traffic flow fundamental diagram of the road's portion where the vehicles are moving. This detection algorithm is validated through some extensive simulations conducted using the well-known NS3 network simulator with SUMO traffic simulator.
Sybil attacks, wherein a network is subverted by forging node identities, remains an open issue in wireless sensor networks (WSNs). This paper proposes a scheme, called Location and Communication ID (LCID) based detection, which employs residual energy, communication ID and location information of sensor nodes for Sybil attacks prevention. Moreover, LCID takes into account the resource constrained nature of WSNs and enhances energy conservation through hierarchical routing. Sybil nodes are purged before clusters formation to ensure that only legitimate nodes participate in clustering and data communication. CH selection is based on the average energy of the entire network to load-balance energy consumption. LCID selects a CH if its residual energy is greater than the average network energy. Furthermore, the workload of CHs is equally distributed among sensor nodes. A CH once selected cannot be selected again for 1/p rounds, where p is the CH selection probability. Simulation results demonstrate that, as compared to an eminent scheme, LCID has a higher Sybil attacks detection ratio, higher network lifetime, higher packet reception rate at the BS, lower energy consumption, and lower packet loss ratio.
The Internet of Things (IoT) is an emerging technology that plays a vital role in interconnecting various objects into a network to provide desired services within its resource constrained characteristics. In IoT, the Routing Protocol for Low power and Lossy network (RPL) is the standardized proactive routing protocol that achieves satisfying resource consumption, but it does not consider the node's routing behavior for forwarding data packets. The malicious intruders exploit these loopholes for launching various forms of routing attacks. Different security mechanisms have been introduced for detecting these attacks singly. However, the launch of multiple attacks such as Rank attack and Sybil attacks simultaneously in the IoT network is one of the devastating and destructive situations. This problem can be solved by establishing secure routing with trustworthy nodes. The trustworthiness of the nodes is determined using trust evaluation methods, where the parameters considered are based on the factors that influence in detecting the attacks. In this work, Providing Routing Security using the Technique of Collective Trust (PROTECT) mechanism is introduced, and it aims to provide a secure RPL routing by simultaneously detecting both Rank and Sybil attacks in the network. The advantage of the proposed scheme is highlighted by comparing its performance with the performance of the Sec-Trust protocol in terms of detection accuracy, energy consumption, and throughput.
Detecting fake accounts (sybils) in online social networks (OSNs) is vital to protect OSN operators and their users from various malicious activities. Typical graph-based sybil detection (a mainstream methodology) assumes that sybils can make friends with only a limited (or small) number of honest users. However, recent evidences showed that this assumption does not hold in real-world OSNs, leading to low detection accuracy. To address this challenge, we explore users' activities to assist sybil detection. The intuition is that honest users are much more selective in choosing who to interact with than to befriend with. We first develop the social and activity network (SAN), a two-layer hyper-graph that unifies users' friendships and their activities, to fully utilize users' activities. We also propose a more practical sybil attack model, where sybils can launch both friendship attacks and activity attacks. We then design Sybil SAN to detect sybils via coupling three random walk-based algorithms on the SAN, and prove the convergence of Sybil SAN. We develop an efficient iterative algorithm to compute the detection metric for Sybil SAN, and derive the number of rounds needed to guarantee the convergence. We use "matrix perturbation theory" to bound the detection error when sybils launch many friendship attacks and activity attacks. Extensive experiments on both synthetic and real-world datasets show that Sybil SAN is highly robust against sybil attacks, and can detect sybils accurately under practical scenarios, where current state-of-art sybil defenses have low accuracy.
The importance of peer-to-peer (P2P) network overlays produced enormous interest in the research community due to their robustness, scalability, and increase of data availability. P2P networks are overlays of logically connected hosts and other nodes including servers. P2P networks allow users to share their files without the need for any centralized servers. Since P2P networks are largely constructed of end-hosts, they are susceptible to abuse and malicious activity, such as sybil attacks. Impostors perform sybil attacks by assigning nodes multiple addresses, as opposed to a single address, with the goal of degrading network quality. Sybil nodes will spread malicious data and provide bogus responses to requests. To prevent sybil attacks from occurring, a novel defense mechanism is proposed. In the proposed scheme, the DHT key-space is divided and treated in a similar manner to radio frequency allocation incensing. An overlay of trusted nodes is used to detect and handle sybil nodes with the aid of source-destination pairs reporting on each other. The simulation results show that the proposed scheme detects sybil nodes in large sized networks with thousands of interactions.
Proof-of-work (PoW) is an algorithmic tool used to secure networks by imposing a computational cost on participating devices. Unfortunately, traditional PoW schemes require that correct devices perform computational work perpetually, even when the system is not under attack. We address this issue by designing a general PoW protocol that ensures two properties. First, the network stays secure. In particular, the fraction of identities in the system that are controlled by an attacker is always less than 1/2. Second, our protocol's computational cost is commensurate with the cost of an attacker. That is, the total computational cost of correct devices is a linear function of the attacker's computational cost plus the number of correct devices that have joined the system. Consequently, if the network is attacked, we ensure security, with cost that grows linearly with the attacker's cost; and, in the absence of attack, our computational cost is small. We prove similar guarantees for bandwidth cost. Our results hold in a dynamic, decentralized system where participants join and depart over time, and where the total computational power of the attacker is up to a constant fraction of the total computational power of correct devices. We show how to leverage our results to address important security problems in distributed computing including: Sybil attacks, Byzantine Consensus, and Committee Election.