LCSS Based Sybil Attack Detection and Avoidance in Clustered Vehicular Networks

Vehicular ad hoc networks (VANETs) are being increasingly advocated for traffic control, accident avoidance, and management of parking lots and public areas. Security and privacy are two major concerns in VANETs. Unfortunately, in VANETs, most privacy-preserving schemes are vulnerable to Sybil attacks, whereby a malicious user can pretend to be multiple (other) vehicles. In this paper, we present a lightweight and scalable protocol to detect Sybil attacks. In this protocol, a malicious user pretending to be multiple (other) vehicles can be detected in a distributed manner through passive overhearing by s set of fixed nodes called road-side boxes (RSBs). The detection of Sybil attacks in this manner does not require any vehicle in the network to disclose its identity; hence privacy is preserved at all times. Simulation results are presented for a realistic test case to highlight the overhead for a centralized authority such as the DMV, the false alarm rate, and the detection latency. The results also quantify the inherent trade-off between security, i.e., the detection of Sybil attacks and detection latency, and the privacy provided to the vehicles in the network. From the results, we see our scheme being able to detect Sybil attacks at low overhead and delay, while preserving privacy of vehicles.

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.

Sybil attack is an encounter in which the personality of a assaulter is contaminated into huge number of incognito personality which is produced to build up the route of the network. In this paper, we deliberate the timestamp approach for Sybil attack in associated system, self correlate network, and cordial network system. Also assorted mechanisms to extenuate the Sybil attack are evaluated, we have proposed timestamp approach for prevention and detection of Sybil attack. We have compared our result with EBRS Approach our approach shows better results as compared to previous approach.

Vehicular ad hoc networks (VANETs) 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 VANETs. 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 neighborhood 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. This estimated speed is obtained using the traffic flow fundamental diagram of the road’s portion where the vehicles are moving.

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.

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

As the derivative of the vehicular ad hoc network, the flying ad hoc network (FANET) will play a crucial role in the B5G/6G era since it provides wide coverage and on-demand deployment services in a distributed manner. The detection of Sybil attacks is essential to ensure trusted communication in FANET. Nevertheless, the conventional methods only utilize the untrusted information that UAV nodes passively “heard” from the “auditory” domain (AD), resulting in severe communication disruptions and even collision accidents. In this paper, we present a novel VA-mapping solution that matches the neighbors observed from AD and “visual” domain (VD), which is the first solution that enables UAVs to accurately correlate what they “see” from and “hear” to detect the Sybil attacks. The overhead in AD is reduced by the proposed topology-aware beacon interval. Relative entropy is utilized to describe the similarity of observed characteristics from dual domains. The dynamic weight algorithm is proposed to distinguish neighbors according to the characteristics’ popularity. The matching model of AD and VD is established and solved by the proposed vampire bat optimizer. Experiment results show that the proposed VA-mapping solution removes the unreliability of individual characteristics and single domains. It reduces the overhead without sacrificing the accuracy of the neighbor list. Furthermore, it outperforms the conventional RSSI-based and mobility-based methods in Sybil detection.

In vehicular ad hoc networks (VANETs), vehicles are enabled to exchange information to detect and mitigate critical situations. VANETs are vulnerable to many security threats. One severe attack is Sybil attack, in which a malicious node forges large number of fake identities in order to disrupt the proper functioning of VANET applications. Fake information reported by a single malicious vehicle may not be highly convincing because most of the VANET applications require several vehicles to reinforce a particular information before accepting as a truth. A Sybil attacker pretends multiple vehicles in order to reinforce false messages. Proposed methodology exploits the characteristics of Sybil nodes as the neighbours of fake identities (originated from a malicious node) share significant common neighbouring nodes. Motivation behind the design of proposed approach is to locate Sybil nodes quickly without using secret information exchange and special hardware support. We evaluate our proposed approach on the realistic traffic scenario.

With increase in human population and economic development, population of private vehicles on road has also increased. This has resulted in increased accident probability and fatalities on the roads. Vehicular Ad-hoc Networks (VANETs) have shown promise of bringing down road accidents and fatalities thereof by enabling communication between vehicles. VANETs also allow the road operators to control and monitor vehicles for rash driving and quick relief. However, these networks face challenges of Design, Routing, Communication and Security. Use of wireless medium for communication has left these networks vulnerable to different type of security attacks. One of the important vulnerability in such networks is caused when a malicious vehicle or RSU is able to acquire multiple identities. The attack is termed as Sybil Attack. In VANETs, a malicious vehicle may send wrong messages related to traffic, accident ahead, road closed, etc. This may force the motor driver to take a different route thus making him prone to an untoward incident. In this paper, we present a survey on different detection schemes for detecting Sybil attack. Comparison among these schemes is also presented. Keywords: Vehicular Ad hoc Networks (VANETs), VANETs Architecture, Security Attacks, Sybil Attack detection.

As wireless sensor networks (WSN) are deployed in fire monitoring, object tracking applications, security emerges as a central requirement. A case that Sybil node illegitimately reports messages to the master node with multiple non-existent identities (ID) will cause harmful effects on decision-making or resource allocation in these applications. In this paper, we present an efficient and lightweight solution for Sybil attack detection based on the time difference of arrival (TDOA) between the source node and beacon nodes. This solution can detect the existence of Sybil attacks, and locate the Sybil nodes. We demonstrate efficiency of the solution through experiments. The experiments show that this solution can detect all Sybil attack cases without missing.

Wireless networks are vulnerable to sybil attacks, in which a sybil node forges multiple identifications to trick the system and conduct harmful attacks. The traditional approach to address sybil attacks is to employ cryptographic-related methods. However, conventional security approaches may not always desirable due to their infrastructural overhead. In this paper, we propose to utilize K-means cluster analysis for detecting sybil attacks based on the spatial correlation between the signal strength and physical locations. Our approach requires minimal overhead to wireless devices. We have evaluated our methods through experimentation using both an 802.11 (WiFi) network as well as an 802.15.4 (ZigBee) network in two office buildings. Our results show that the proposed sybil attack detector is highly effective with over 95% detection rates and under 5% false positive rates.

Detecting Sybil attacks in wireless sensor networks using UWB ranging-based information

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.

SummaryAn attack on VANET known as a Sybil attack involves the attacker claiming or stealing multiple identities with the intent of causing havoc on the network's operation by disseminating false information. To protect the VANET network from Sybil attack, a variety of methods have been proposed. In order to compensate for the Sybil attack on VANETs, a rumor riding with salp swarm optimization (SSO) based Routing protocol is developed in this research work. The primary goal of this investigation is to discover how VANETs are vulnerable to the Sybil attack in order to improve their security. Rumor riding will be used to identify the Sybil attack in the suggested strategy. VANETs are protected by the Blowfish Algorithm, which uses SSO to select the best key for each network. The collective intelligence and dynamic adaptation of SALP swarm optimization algorithm enable efficient identification of rumor sources in complex networks. The algorithm is scalable, flexible, and adept at reducing false positives and negatives, making it suitable for large‐scale monitoring. Its parallel processing capabilities and robustness ensure rapid and reliable detection. Using the NS2 platform, the proposed method is compared to existing benchmark in terms of delay, packet delivery ratio (PDR), packet loss, throughput, and attack detection rate. The proposed SSO has reduced the packet loss by nearly 5% and delay by nearly 7%, when compared with existing techniques. The delivery ratio of packet for proposed model is increased up to 8% of PDR, when compared with ACO, PSO, and FA. According to this evidence, it is clear that the proposed SSO outperformed existing approaches in a variety of performance metrics.

Protecting Routing Data in WSNs with use of IOTA Tangle