Vision-augmented split-attention neural architectures for Sybil resilience via chaos-driven secure elliptic key synthesis to assured data exchange in CR-VANETs

In recent years, there has been fast development within the area of vehicular ad hoc networks (VANET). In the future, VANET communication will play a first-rate position in improving the protection and performance of the transportation system. If security isn't always furnished in VANET, then it may result in apparent misapplication. One of the dangerous or risky attacks in VANETs is the Sybil, which forges fake identities inside the network to disrupt or compromise the communication among the network nodes. Sybil attacks have an effect on the carrier transport associated with road safety, traffic congestion, multimedia entertainment and others. Thus, VANETs claim for a security mechanism to prevent Sybil attacks. Within this context, this paper proposes a mechanism, known as Sybil Attack Prevention and Detection Mechanism in VANET based on Multi-Factor Authentication (SAPDMV), to detect Sybil attacks in VANETs based on Multi-Factor Authentication. The proposed system works based on the principle of registration, and use identification number, status, Maximum and minimal threshold value and security key for the verification. The paper proposes a Sybil Attack Prevention and Detection Mechanism in VANET (SAPDMV) based on multifactor authentication. The mechanism uses vehicle identification, status, security key, and both minimum and maximum speed thresholds to authenticate nodes and detect Sybil attacks. Implemented and tested using Network Simulator-2.35, the system demonstrates an improved detection rate, reduced false positive and false negative rates, and enhanced network performance metrics such as end-to-end delay, throughput, and packet delivery ratio. The simulation result shows our proposed algorithm enhances detection rate, false positive rate, and false negative rate. The proposed solution is improved to 96%, 5%, and 4%, respectively, compared with the Sybil attack-AODV and existing/old work. The approach is scalable and effective in real-world VANET environments, making it a promising framework for future intelligent transportation systems.

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

Vehicular Ad Hoc Networks (VANETs) bring many benefits and conveniences to road safety and drive comfort in future transportation systems. However, VANETs suffer from almost all security issues as same as wireless networks. Sybil attack is one of the most risky threats since it violates the fundamental assumption of VANETs-based applications that all received information are correct and trusted. Sybil attacker can generate multiple fake identities to disseminate false messages. In this paper, we propose a novel Sybil attack detection method based on Received Signal Strength Indicator (RSSI), Voiceprint, to conduct a widely applicable, lightweight and full-distributed detection for VANETs. Unlike most of previous RSSI-based methods that compute the absolute position or relative distance according to RSSI values, or make statistic testing based on RSSI distributions, Voiceprint adopts RSSI time series as the vehicular speech and compares the similarity among all received series. Voiceprint does not rely on any predefined radio propagation model, and conducts independent detection without support of the centralized node. Moreover, we improve Voiceprint by allowing it to conduct detection on Service Channel (SCH) to shorten observation time. Furthermore, we extend Voiceprint with change-points detection to identify those illegitimate nodes performing power control. Extensive simulations and real-world experiments demonstrate that Voiceprint is an effective method considering the cost, complexity, and performance.

Vehicular <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ad hoc</i> networks (VANETs) have far-reaching application potentials in the intelligent transportation system (ITS) such as traffic management, accident avoidance and in-car infotainment. However, security has always been a challenge to VANETs, which may cause severe harm to the ITS. Sybil attack is considered as a serious security threat to VANETs since the adversary can disseminate false messages with multiple forged identities to attack various applications in the ITS. RSSI-based Sybil nodes detection is an efficient scheme against Sybil attacks, which adopts position estimation, distribution verification or similarity comparison to identify Sybil nodes. However, when Sybil nodes conduct power control to deliberately change transmission powers, the received RSSI values would change correspondingly, which leads to inaccurate localization or different RSSI time series of these Sybil nodes. Thus, it is very difficult to differentiate Sybil nodes from normal nodes via conventional RSSI-based methods. This paper first discusses potential power control models (PCMs) for launching Sybil attacks in VANETs, then presents two simple Sybil attack models and three sophisticated Sybil attack ones with or without power control in detail, finally proposes a power control identification Sybil attack detection (PCISAD) scheme to find anomalous variations in RSSI time series, which are then used to identify Sybil nodes via a linear SVM classifier. Extensive simulations and real-world experiments prove that the proposed scheme can effectively deal with Sybil attacks with power control.

Vehicular Ad Hoc Networks (VANETs) have the potential to enable the next-generation Intelligent Transportation Systems (ITS). In ITS, data contributed by vehicles can build a spatio-temporal view of traffic statistics, which can improve road safety and reduce slow traffic and jams. To preserve drivers&#x2019; privacy, vehicles should use multiple pseudonyms instead of only one identity. However, vehicles may exploit this abundance of pseudonyms and launch Sybil attacks by pretending to be multiple vehicles. Then, these Sybil (or fake) vehicles report false data, e.g., to create fake congestion or pollute traffic management data. In this article, we propose a Sybil attack detection scheme using proofs of work and location. The idea is that each road side unit (RSU) issues a signed time-stamped tag as a proof for the vehicle&#x2019;s anonymous location. Proofs sent from multiple consecutive RSUs are used to create a trajectory which is used as vehicle anonymous identity. Also, contributions from one RSU are not enough to create trajectories, rather the contributions of several RSUs are needed. By this way, attackers need to compromise an infeasible number of RSUs to create fake trajectories. Moreover, upon receiving the proof of location from an RSU, the vehicle should solve a computational puzzle by running proof of work (PoW) algorithm. Then, it should provide a valid solution (proof of work) to the next RSU before it can obtain a proof of location. Using the PoW can prevent the vehicles from creating multiple trajectories in case of low-dense RSUs. To report an event, the vehicle has to send the latest trajectory to an event manager. Then, the event manager uses a matching technique to identify the trajectories sent from Sybil vehicles. The scheme depends on the fact that the Sybil trajectories are bounded physically to one vehicle, and therefore, their trajectories should overlap. Extensive experiments and simulations demonstrate that our scheme achieves high detection rate of Sybil attacks with low false negative and acceptable communication and computation overhead.

Until recently vehicles and transportation systems were considered as the realm of mechanical engineers, but the need for the road safety and desire to be connected to the world, has broaden the industry scope. In order to do so Intelligent Transport System has been introduced and for few years, Vehicular Ad hoc Networks are getting much attention. The advance developments, wireless communication and life safety point towards to take into consideration the need of security in VANETs. In VANET, many attacks are possible and can cause serious damages to life. One such attack is Sybil attack. Sybil attacks have been regarded as a serious security threat to Ad hoc Networks and Sensor Networks. They may also damage the potential applications of Vehicular Ad hoc Networks (VANETs) by creating a deception of traffic congestion. Here we look on how the Sybil attack works and possible ways that an attacker can cause harm by launching these types of attacks in VANETS, along with the detection schemes that can be used to identify Sybil nodes and prevent the network from various hurtful effects.

Wireless Sensor Network (WSN) is the network of minimal expensive and tiny devices for sensing, which is known as sensors. These networks are vulnerable to various types of attacks namely black hole attack, Sybil attack and so forth. Black hole attack is the most harmful attack and also the access way for all the other types of attacks, whereas Sybil attack is a dangerous hazard to sensor networks in which sensor nodes have numerous identities. These attacks may interrupt the performance and functioning of network, which may cause harm to the network system if not properly detected. Hence, an efficacious technique is proposed for black hole and Sybil attack detection utilizing Adaptive Taylor Sail fish optimizer (Adaptive Taylor-SFO) algorithm. Firstly, WSN nodes are simulated in a network and thereafter, routing is executed by Adaptive Taylor-SFO, which is newly designed by an integration of Adaptive concept with Taylor series and Sail Fish optimizer (SFO) for selecting an optimal route by consideration of fitness measures namely, delay, energy, and distance. Finally, detection of black hole and Sybil attack is carried out by Deep stacked auto encoder. Moreover, the network classifier is tuned employing proposed Adaptive Taylor-SFO. Thus, the proposed technique effectually classified into normal, black hole and Sybil attack. Additionally, proposed technique obtained minimal delay of 19.91ms, FNR of 0.0773, FPR of 0.1026, maximal throughput of 125.73kbps and maximal PDR of 97.52%.

Vehicular Ad hoc Networks (VANETs) are vital for efficient and secure vehicle-to-infrastructure communication in intelligent transportation systems. sybil attacks, where malicious entities adopt multiple identities, are a major security concern in VANETs. Detecting and mitigating these attacks is crucial for ensuring communication reliability and trust. This article focuses on detecting sybil attacks in Vehicle-to-Vehicle (V2V) communication by using a novel mechanism that characterizes the wireless channel through Received Signal Strength Indicator (RSSI) and angular spread in both azimuth and elevation planes. By incorporating angular spread alongside RSSI, the proposed mechanism offers more accurate and robust detection, particularly in dense vehicle environments. Utilizing a precise wireless channel model based on ray tracing statistics, the approach outperforms traditional RSSI-based methods. Experimental results confirm the enhanced accuracy and reliability of the proposed mechanism for detecting sybil attacks in V2V communication scenarios.

The Sybil attack is a particularly harmful threat to sensor networks where a single sensor node illegitimately claims multiple identities. A malicious node may generate an arbitrary number of additional node identities using only one physical device. The Sybil attack can disrupt normal functioning of the sensor network, such as the multipath routing, used to explore the multiple disjoint paths between source-destination pairs. But the Sybil attack can disrupt it when a single adversary presents multiple identities, which appear on the multiple paths. Digital certificates are a way to prove identities, but they are not suitable for the sensor network because of the large computational overheads. Researchers have proposed a light-weight identity certificate method to defeat Sybil attacks, but it is not suitable for a large scale sensor network because of the huge memory usage required at each node. In this paper, we propose a light-weight Sybil attack detection method based on a hierarchical architecture in sensor networks. The proposed method can be used in a large scale sensor network, and it only uses the symmetric cryptography avoiding the use of the public key cryptography. Simulation results show that the proposed method only needs a small memory (below 140 KB) for each node (even in a large scale sensor network). In addition, the energy consumption by new identity generations within each group is low (below 60 mJ), which is much lower than the available energy at each sensor node.

Participatory sensing is a revolutionary paradigm in which volunteers collect and share information from their local environment using mobile phones. Different from other participatory sensing application challenges who consider user privacy and data trustworthiness, we consider network trustworthiness problem namely Sybil attacks in participatory sensing. Sybil attacks focus on creating multiple online user identities called Sybil identities and try to achieve malicious results through these identities. In this paper, we proposed a Cloud based Trust Management Scheme (CbTMS) framework for detecting Sybil attacks in participatory sensing network. Our CbTMS was proposed for performing Sybil attack characteristic check and trustworthiness management system to verify coverage nodes in the participatory sensing. To verify the proposed framework, we are currently developing the proposed scheme on OMNeT++ network simulator in multiple scenarios to achieve Sybil identities detection in our simulation environment.

In Industrial Wireless Sensor Networks (IWSNs), Sybil attacks compromise network topology and reduce data reliability by forging virtual nodes, leading to degraded network performance and significantly diminished monitoring accuracy. To address these issues, this study aims to propose a high-accuracy and highly robust Sybil attack detection method to overcome the limitations of traditional detection approaches, such as low precision and difficulty in handling ambiguous probability boundaries. The research designs a collaborative detection mechanism that integrates a CNN-BiLSTM-Attention (CBSA) deep learning module with the K-means clustering algorithm. By combining "multidimensional feature extraction via deep learning + clustering-based classification boundary optimization," an end-to-end Sybil attack detection model (CBSA-Kmeans) is constructed.The specific implementation includes four parts: 1. A Convolutional Neural Network (CNN) processes the raw sensor data matrix to extract spatial local patterns and capture abnormal correlation features among nodes. 2. A Bidirectional Long Short-Term Memory network (BiLSTM) processes the feature sequences output by the CNN. The forward LSTM learns the "past-present" temporal dependencies to identify the cumulative effects of attacks, while the backward LSTM models the "present-past" temporal correlations to trace attack origins. 3. An Attention mechanism is introduced to dynamically focus on key time steps corresponding to critical attack features, generating a weighted context vector and outputting attack probability predictions. 4. The K-means clustering algorithm is employed to perform secondary partitioning on the prediction probability space output by the CBSA module. By measuring Euclidean distances, high-density attack clusters and normal data clusters are constructed to form decision regions, thereby optimizing classification boundaries.Through a progressive approach of "spatial feature extraction → temporal dependency modeling and key feature enhancement → probability space clustering optimization," the model achieves attack detection: CNN first performs preliminary spatial feature screening, BiLSTM and Attention collaboratively mine temporal attack features and highlight critical information, and finally, K-means clusters the prediction probabilities to clarify the boundaries between attack and normal data. Experimental results demonstrate that the CBSA-Kmeans model excels in IWSN Sybil attack detection tasks: it achieves a detection accuracy of 98.2% and a recall rate of 96.7%, representing an improvement of over 12% compared to traditional detection methods. Additionally, the model has minimal negative impact on network performance, increasing IWSN network throughput by 23.5% and reducing data transmission latency by 31.8%, while effectively addressing the ambiguous probability boundary issue present in traditional methods. In conclusion, the CBSA-Kmeans model achieves high-precision and highly robust detection of Sybil attacks in IWSNs through the synergistic integration of deep learning and clustering algorithms, validating the effectiveness and superiority of this collaborative detection mechanism. This method provides a practical technical solution for IWSN security protection, ensuring network topology integrity and data transmission reliability while enhancing operational efficiency and monitoring accuracy. It holds significant practical application value for ensuring the secure and stable operation of wireless sensor networks in industrial settings.

Nowadays, VANET plays significant role to enhance safety, mobility and convenience applications in intelligent transportation system. However, security in such systems still remains a big challenge because of its large coverage, wireless communication and high-speed mobility of the nodes. Sybil attack as a serious security threat creates new identities for neighboring vehicles or steals vehicles identities for the attacker. The purposes of creating invalid identities are network disruption, misleading neighbor vehicles for traffic jams or fatal and tragic accidents, opening the road, disruption in voting based systems, and violating traffic safety briefly. In the present study, a privacy-aware Sybil attack detection (PASAD) scheme is proposed to solve two conflicting goals: to preserve the privacy and to detect the Sybil attack in the vehicle to vehicle (V2V) and vehicle to roadside unit (V2R) communications. The proposed scheme is based on a safe physical authentication and Boneh-Shacham (BS) short group signature scheme. The theatrical analyzes show that this scheme ensures the security requirements like correctness, privacy, unforgeability, traceability, and Sybil attack detection in exchanges of warning messages. Extensive simulations and analysis demonstrate the efficiency and effectiveness of the proposed scheme in vehicular networks.

Vehicular Ad-hoc Networks (VANET) technology provides a fast, easy to deploy and an inexpensive solution for intelligent traffic control and traffic disaster preventive measure. In VANET, moving vehicles communicate using wireless technology. This communication can be used to divert traffic from congested or dysfunctional routes, to seek help in an emergency and to prevent accident escalation in addition to providing intelligent traffic control. However, an attacker can use the same system to spread false warning messages resulting in congestion on certain routes thereby leading to accidents or causing delay in providing help etc. One of the harmful attacks against VANET is Sybil attack, in which an attacker generates multiple identities to feign multiple nodes. In this paper, we present an implementation of simulated Sybil attack scenario in VANET and discuss its impact on network performance. A cooperative approach of Sybil attack detection, inferred through analysis of Sybil attack, is also presented.

Social networks are becoming more popular platforms for communication, interaction and collaboration among relatives & friends. Researchers have recently come out with an emerging class of technology that leverage relationships from social networks to improve security in applications such as electronic mail, overlay routing and web browsing. Peer-to-peer systems are mostly vulnerable to Sybil attacks. The Sybil attack is an attack where an adversary creates multiple Duplicate or False identities to compromise the running of the system. By including false information by the Duplicated entities, an adversary can mislead a system into making decisions benefiting. Defending against Sybil attacks is quite challenging. Over the last few years, there are so many approaches suggested to overcome such issues of fake user behavior but there remain some issues unaddressed. This work gives a novel Sybil attack detection and removal mechanism based on behavioral analysis and neighbor similarity calculation named Enhanced Similarity based Sybil Detection Mechanism (ESSDM). In this approach, neighbor nodes acquainting with more familiarity are identified as the duplicated Sybil nodes. By applying our enhanced similarity based Sybil detection mechanism, the results show that the Sybil attack is considerably minimized, thereby improving security and its performance level.

In recent years, Vehicular ad hoc networks (VANETs) could facilitate the decision-making progress of the drivers for example trip planning with the consideration of traffic. In the VANET, the Sybil attack is a very serious attack that collapses the security. In literature, some of the methods are reviewed to detect Sybil attacks in VANETs, but it fails to achieve Sybil attack detection. Hence, in this paper, Emperor Penguin Optimization-based Routing protocol (EPORP) is developed for detecting the Sybil attack which enhances the VANETs security. The main motive of the research is detecting the Sybil attack in VANETs for enhancing the secure operation. In the proposed approach, the Sybil attack will be detected with the help of the Rumour riding technique. To enhance the security of the VANETs, the Split XOR (SXOR) operation is utilized. In the SXOR operation, the optimal key is selected with the help of Emperor Penguin Optimization (EPO). The proposed method is implemented in the NS2 platform and performances are evaluated by metrics such as delay, throughput, delay, encryption time, and decryption time. The proposed method is compared with existing methods such as Whale Optimization Algorithm (WOA), Particle Swarm Optimization (PSO), and Firefly Algorithm (FA) respectively. While analyzing the delivery ratio, the proposed method has 0.96 s, and the WOA, PSO, and FA are 0.94, 0.92, and 0.90 respectively. From the analysis, the proposed method has a high delivery ratio value compared with the WOA, PSO, and FA methods. Similarly, the other parameters are analyzed and compared with the existing methods.