Reliable Detection of Location Spoofing and Variation Attacks

Kim, Chiho; Chang, Sang‐Yoon; Lee, Dongeun; Kim, Jonghyun; Park, Kyungmin; Kim, Jinoh

doi:10.1109/access.2023.3241236

Cited by 17 publications

(13 citation statements)

References 37 publications

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“…Type-16 BCM RF-FV2 1.0000 0.9990 0.9999 0.9970 0.9989 0.9986 [30] 0.9564 0.7543 0.9116 0.9177 0.8403 0.8838 [31] 0.9680 0.7795 0.9713 0.9704 0.8403 0.9082 [32] -0.9501 ---- [33] 0.8566 0.8680 0.8466 0.8410 0.8663 - [34] -----0.7990 [35] -----0.9910…”

Section: Testing Resultsmentioning

confidence: 99%

“…Kin et al [35] trained different models using Extreme Gradient Boosting (XGB), Multi-layer Perceptron (MLP), KNN, SVM, and RF algorithms to evaluate the performance of two different feature sets, named (Basic and Ext). In the "Basic" feature set, all the features in two consecutive BSMs -from the same senderare combined in one feature set for each message.…”

Section: Ml-based Techniquesmentioning

confidence: 99%

“…The implementation process has been accomplished in five phases, as summarized in Figure 2. Firstly, the dataset was pre- The difference between the total and estimated distances using average speeds The difference between the estimated and average velocities The magnitude difference between the estimated and average velocities [31] EB, EV, SVM, NB, RF, and KNN BCS and BCM LP MP The difference between the total and estimated distances using average speeds The difference between the estimated and average velocities The magnitude difference between the estimated and average velocities [32] KNN and RF BCS (Type-2) Sender-receiver distance RSSI Sender's claimed location The variance in claimed location between the latest and previous messages from the same sender Sender-receiver angle Calculated distance using RSSI [33] Ensemble learning, KNN, and RF BCS The variance in claimed location between the latest and previous messages from the same sender The variance in the location of the receiver between the latest and previous messages Sender-receiver angle Calculated distance using RSSI The difference between the calculated distance using RSSI and the distance using the message's claimed location [34] Federated ML BCS and BCM The difference between the total and estimated distances using average velocities The difference between the estimated and average velocities The magnitude difference between the estimated and average velocities [35] KNN, SVM, RF, XGB, and MLP BCM All the features in the latest and previous messages MP The variance in claimed location between the latest and previous messages from the same sender The time variance between the latest and previous messages The Euclidean distance between the latest and previous messages pared to be compatible with ML training. A new derived feature based on the received message's RSSI was introduced in the next phase.…”

Section: Proposed Modelmentioning

confidence: 99%

See 2 more Smart Citations

Using the received signal strength indicator and machine learning to enhance the security of vehicular ad-hoc networks

Abdelkreem,

Hussein,

Tammam

2024

Preprint

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A vehicular Ad-hoc Network (VANET) is a wireless network established between vehicles and surrounding infrastructure to enable information exchange between them. Consequently, many applications that can enhance passengers' security assurance and traffic flow are built upon this information. However, malicious nodes can manipulate the exchanged data to attack other nodes and disrupt the network’s normal behavior. For example, if an attacker broadcasts a falsified vehicle’s location, the functionality of applications that rely on accurate location sharing will be confused, and deadly accidents may occur. Thus, many studies have been conducted to implement Misbehavior Detection Schemes (MDSs) to identify position spoofing attacks in VANET. However, the performance results of these MDSs are limited in detecting some attack types, and most of them rely on application layer features that the attacker can alter before transmission to evade the detection model. So, this paper proposes an MDS for position falsification attack detection that employs a physical-layer-based feature called RSSIConf. The RSSIConf feature relies on the Received Signal Strength Indicator of received messages and its confidence intervals at different confidence levels and distances. The evaluations show that the proposed model outperforms existing approaches, where the improvements range between 0.76% to 13.26% in accuracy and 0.74% to 12.71% in F1-Score, depending on the detected attack type.

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Section: Testing Resultsmentioning

confidence: 99%

Section: Ml-based Techniquesmentioning

confidence: 99%

Section: Proposed Modelmentioning

confidence: 99%

See 1 more Smart Citation

Using the received signal strength indicator and machine learning to enhance the security of vehicular ad-hoc networks

Abdelkreem,

Hussein,

Tammam

2024

Preprint

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show abstract

“…A new cognitive closed‐loop system was developed in Benlloch‐Caballero et al 34 to provide distributed dual‐layer self‐protection capabilities. Comparative analysis of federated deep learning approaches was introduced in Kim et al 35 for higher accuracy in detecting attacks. The data‐driven methodology was presented in Ferrag et al 36 for discovering location spoofing attacks.…”

Section: Related Workmentioning

confidence: 99%

Artificial intelligence enabled Luong Attention and Hosmer Lemeshow Regression Window‐based attack detection in 6G

Bhuvaneshwari¹,

Balusamy

Dhanaraj

et al. 2023

Int J Communication

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SummaryRegardless of the developments of networking and communication technologies, security is without exception a predominant feature to ensure network reliability. The future sixth‐generation (6G) network is anticipated to be carried out with artificial intelligence (AI) powered communication via machine learning (ML), post‐quantum cryptography, and so on. AI‐powered communication has been in recent years utilized in enhancing network traffic performance with respect to resource management, optimal frequency spectrum design, security, and latency. The studies of modern wireless communications and anticipated features of 6G networks revealed a prerequisite for designing a trustworthy attack detection mechanism. In this work, a method called, Luong Attention and Hosmer Lemeshow Regression Window‐based (LA‐HLRW) attack detection in 6G is proposed. Initially, with the raw Botnet Attack dataset obtained as input, preprocessing is performed to normalize network traffic features. Next, the dimensionality of network traffic feature of large‐scale network traffic data is reduced using the Luong Attention integrated with Long Short Term Memory (LSTM)‐based Feature extraction model. Finally, with the objective of classifying network traffic samples for attack detection in 6G, we analyze the low dimensional network traffic feature set produced by Luong Attention integrated with LSTM using the Hosmer Lemeshow Logistic Regression Window‐based Attack Detection model. Extensive experiments are performed with the Botnet Attack dataset to validate the efficiency of the proposed LA‐HLRW method by using different parameters such as attack detection accuracy, attack detection time, precision, and recall. The overall analysis of proposed LA‐HLRW results significantly reduced the attack detection time by 24%, and additionally improved attack detection accuracy, precision, and recall by 5%, 5%, and 6% as compared to existing attack detection methods respectively.

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“…The method can detect simple spoofing attacks, but a complex spoofing attack, such as a turn-by-turn attack or a wrong-turn attack, cannot be detected. Dasgupta et al proposed a data-driven approach to reliably detect network attacks generated by location spoofing [77].…”

Section: B: For Location Spoofing Attackmentioning

confidence: 99%

A Survey on Vehicular Cloud Network Security

Deng,

Liu

et al. 2023

IEEE Access

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The novel Vehicular Cloud Network (VCN) is a close combination of Vehicular Ad hoc Network, Cloud Computing, and Edge Computing. However, VCN is facing the enormous challenge of security and privacy before it is generalized. To address these issues, VCN communication characteristics and security requirements are summarized first. Secondly, the paper conducts an in-depth analysis of the existing security problems and solutions in VCN based on several attack models, including routing, data, identity, DoS, and hostile attacks. And we summarize the security solutions into two categories: security defense measures based on identity authentication and access control, and security protection measures based on traffic detection and anomaly detection. Thirdly, this paper proposes a reliable VCN security architecture consisting of three types of clouds: vehicular, edge, and central clouds. Moreover, the VCN architecture combines a Vehicle Intrusion Detection and Prevention System with a Vehicular Cloud Security Management Platform to ensure the security of VCN. Last but not least, some open issues worth investigating are discussed.

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Reliable Detection of Location Spoofing and Variation Attacks

Cited by 17 publications

References 37 publications

Using the received signal strength indicator and machine learning to enhance the security of vehicular ad-hoc networks

Using the received signal strength indicator and machine learning to enhance the security of vehicular ad-hoc networks

Artificial intelligence enabled Luong Attention and Hosmer Lemeshow Regression Window‐based attack detection in 6G

A Survey on Vehicular Cloud Network Security

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