TDOA-based Sybil attack detection scheme for wireless sensor networks

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.

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

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.

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.

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.

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.

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

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.

A Wireless Sensor Network (WSN) is a wireless network consisting of small sensor nodes. Sensors are used to monitor physical or environmental conditions. WSNs are particularly used in military and civilian applications. As WSNs are generally deployed in an unattended area, they are prone to various types of attacks. One of the harmful attacks is Sybil attack in which a node illegitimately claims multiple identities. In this scenario, legitimate node shares data to the malicious node and the data are lost. So that it becomes necessary to secure the network from this type of attack. This paper aims to study, discuss and analyze the various techniques for detection of Sybil attack in Wireless Sensor Networks. Also various protocols affected by Sybil attack are studied and analyzed.

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.

Sybil attacks are the major problems occurred in the distributed systems without trusted identities. It occur when the one-to-one relationship between a node and its identity is violated. This is occurred by an attacker creates multiple pseudonymous identities and pretends to be multiple, distinct nodes in the system. Intent search is another important function in social network. It mostly depends on surrounding text features which leads to noisy search result. In this paper a new Sybil defence mechanism is introduced that is Sybil protector, in order to defend against attacks in social networks. It is efficient and scalable to large social network. Network topologies are used by the Sybil protector to defend against Sybil attacks in social networks. It can effectively identify the Sybil nodes and detect the Sybil community around a Sybil node. And also limits the number of attack edges in online social networks. Here also included a novel internet image search which only requires one-click user feedback. For that expanded keywords are used to create positive example images and to include more relevant images it enlarge the image pool. It consist of four steps.1) Adaptive similarity.2) Keyword expansion. 3) Visual query expansion. Intent image search is very effective because it has extremely simple user interface. IndexTerms: Sybil attack, Sybil identity, social networks, attack edge, keyword expansion.

Protecting Routing Data in WSNs with use of IOTA Tangle

Decentralized distributed systems such as peer-to-peer systems are particularly vulnerable to sybil attacks, where a malicious user pretends to have multiple identities (called sybil nodes). Without a trusted central authority, defending against sybil attacks is quite challenging. Among the small number of decentralized approaches, our recent SybilGuard protocol [H. Yu et al., 2006] leverages a key insight on social networks to bound the number of sybil nodes accepted. Although its direction is promising, SybilGuard can allow a large number of sybil nodes to be accepted. Furthermore, SybilGuard assumes that social networks are fast mixing, which has never been confirmed in the real world. This paper presents the novel SybilLimit protocol that leverages the same insight as SybilGuard but offers dramatically improved and near-optimal guarantees. The number of sybil nodes accepted is reduced by a factor of ominus(radicn), or around 200 times in our experiments for a million-node system. We further prove that SybilLimit's guarantee is at most a log n factor away from optimal, when considering approaches based on fast-mixing social networks. Finally, based on three large-scale real-world social networks, we provide the first evidence that real-world social networks are indeed fast mixing. This validates the fundamental assumption behind SybilLimit's and SybilGuard's approach.

In traffic safety related application of Vehicular Ad-hoc Networks (VANETs), security is a great important issue. Sybil attack is a particular kind of attack where the attacker illegitimately claims multiple identities. In the past years, several approaches have been proposed for solving this problem. They are categorized into PKI-based, infrastructure-based, observer-based, and resource-test-based schemes. In this paper, previous protocols are analyzed, and a novel scheme to detect the Sybil nodes in VANETs is presented, mitigating the effect of a Sybil attack. The proposed Sybil nodes detection scheme, Traffic Flow Theory Aided Physical Measurement-Based Sybil Nodes Detection Mechanism in VANETs (PMSD), takes advantage of unmodifiable physical measurements of the beacon messages instead of key-based materials, which dose not only solve the Sybil attack problem, but also reduces the overhead for the detection. The proposed scheme does not require fixed infrastructure, which makes it easy to implement. To increase the detection accuracy, traffic flow theory and safety guard distance is introduced into the scheme. The simulation results show a 97% detection rate of Sybil nodes, with only about a 2% error rate.

Sybil attack refers to the situation when a malicious node falsely claims to have numerous identities and is known to be one of the security threats to the Internet of Things (IoT). Due to recent increase usage of unmanned aerial vehicles (UAVs) in various applications, Sybil attack has been identified as a threat to the flying ad hoc network (FANET) paradigm and its integration with the IoT to form the Internet of Flying Things (IoFT). In this paper, we propose an intelligent Sybil attack detection approach for FANETs-based IoFT using physical layer characteristics of the radio signals emitted from the UAVs as detected by two ground nodes. A supervised machine learning approach is employed and experimented with several different classifiers available in the Weka workbench platform. The experiment was carried out based on two features of the radio signals, namely, the received signal strength difference (RSSD) and the time difference of arrival (TDoA). Simulation results revealed that the proposed scheme can achieve a high correct classification accuracy of above 91% on average, even for smart malicious nodes with power control capability operating at power levels not directly trained. In addition to its high performance, the proposed scheme is also less susceptible to various attacks commonly carried out on the upper layers, such as data spoofing, due to the use of only intrinsically generated physical layer data. Furthermore, no additional communications overheads of the UAV nodes are required for the functionality of this scheme.