Detecting sybil attacks inwireless and sensor networks using cluster analysis

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.

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

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.

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.

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.

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

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.

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.

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.

The mobile ad-hoc network (MANET) output is critically impaired by the versatility and resource constraint of nodes. Node mobility affects connection reliability, and node resource constraints can lead to congestion, which makes the design of a routing MANET protocol with quality of service (QoS) very difficult. An adaptive clustering reputation model (ACRM) method is proposed to improve energy efficiency with a cluster-based framework. The proposed framework is employed to overcome the problems of data protection, privacy, and policy. The proposed ACRM-MRT approach that includes direct and indirect node trust computation is introduced along with the master recovery timer (MRT) for achieving an efficient service recovery process, and its service recovery time is calculated through the service execution process. During data transmission in MANET, various types of attacks can occur, of which the Sybil attack is the most dangerous. To address this problem, this paper proposed a method for the detection and prevention of Sybil attacks using a resilient scheme. The proposed method can improve system energy efficiency and address security, safety, and privacy issues of wireless network applications. Finally, the performance of the proposed method is evaluated regarding the time delay, throughput, energy efficiency, control overhead, and detection rate. The simulation results show that the proposed ACRM-MRT method can effectively improve the time delay, throughput, energy efficiency, control overhead, and detection rate compared to the existing methods. Topological change adaptive ad-hoc on-demand multi-path distance vector (TA-AOMDV)and ad-hoc on-demand multi-path distance vector (AOMDV) are simulated on the NS2 platform for the data rate in the range of 4–40 kbps and the number of nodes in the range of 10-100. The proposed method can reduce the service recovery time in the case of faults during service execution and can be used in real-time applications traffics since it is mostly affected by failure through the occurrence of delay and loss of packets.

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.

Accurately positioning nodes in wireless and sensor networks is important because the location of sensors is a critical input to many higher-level networking tasks. However, the localization infrastructure can be subjected to non-cryptographic attacks, such as signal attenuation and amplification, that cannot be addressed by traditional security services. We propose several attack detection schemes for wireless localization systems. We first formulate a theoretical foundation for the attack detection problem using statistical significance testing. Next, we define test metrics for two broad localization approaches: multilateration and signal strength. We then derived both mathematical models and analytic solutions for attack detection for any system that utilizes those approaches. We also studied additional test statistics that are specific to a diverse set of algorithms. Our trace-driven experimental results provide strong evidence of the effectiveness of our attack detection schemes with high detection rates and low false positive rates across both an 802.11 (WiFi) network as well as an 802.15.4 (ZigBee) network in two real office buildings. Surprisingly, we found that of the several methods we describe, all provide qualitatively similar detection rates which indicate that the different localization systems all contain similar attack detection capability.

VANET is a next generation communication technology where vehicles create an autonomous network with assistance of RSU (Road Side Units). VANET provides legitimate information to the users on the road, in order to increase the road and user’s safety. It provides useful information to the vehicles about directions, location mapping, premises, etc. The fast propagation of emergency and local warning messages to the approaching vehicles will be helpful for preventing secondary accidents. If security of the network is not guaranteed, several attacks may occur, thereby alert messages may not reach to the RSUs on time or message may get spoof due to attack on it. Major attacks on emergency messages such as timing attack, spoofing attack, DoS attack, Sybil attack, etc. would be considered to mitigate and make the communication and infrastructure more secure. One of the major emergency message attack is Sybil, which is used by selfish message propagators to claim authenticity of their message by propagating with multiple identities. By deceiving other nodes with their false support using multiple identifies, they can achieve various purpose in network like vehicle re-routing, speed shift etc. This work proposes a RSU cooperative detection mechanism involving triangulation and fake propagation by RSU to identify the Sybil attacks in the VANET. The proposed methodology secures fast communication emergency messages to prevent secondary accidents by validating the message sources, nodes will accept only the messages from RSU which are digitally signed and source validated against Sybil nodes. The performance evaluation is made with existing methodology and effectiveness of the proposed methodology results demonstrated in article. The spatial location and trajectory of attacker is learnt effectively with RSU cooperation in the proposed work.

Peer to peer and distributed systems are generally susceptible to sybil attacks. Online social networks (OSNs), due to their fat user base and open access nature are also prone to such attacks. Current state-of-art algorithms for sybil attack detection make use of the inherent social graph created among registered users of OSN service. They rely on the inherent relationship among these users. No effort is made to combine other characteristic behavior of sybil users with properties of social graph of OSNs to improve detection accuracy of sybil attacks. Sybil identities are also used as gateways for spreading spam content in OSNs [6]. The proposed approach exploits this behavior of sybil users to improve detection accuracy of existing sybil detection algorithms. In the proposed approach, content generated/published by each user is used along with the topological properties of the social graph of registered users. A machine learning model is used for assigning a fractional called trust value which denotes the amount of legitimate content generated by the user. A modification to sybil detection algorithm is proposed which makes use of the of each user to improve the accuracy of detecting a sybil identity. Real dataset from Facebook is crawled and used for analysis and experiments. Analytical results show the superiority of proposed solution. Results are compared with SybilGuard and SybilShield which shows ~14% decrease in false positive rates with very minimal effect on acceptance rate or false negative rate of the sybil detection algorithms. Also, the proposed modification does not affect the performance of existing sybil detection algorithms and can be implemented in a distributed manner.