Measurement of IEEE 802.11p Performance for Basic Safety Messages in Vehicular Communications

Rajput, Nitin Singh

doi:10.1109/ants.2018.8710108

Cited by 16 publications

(9 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this article, CIC-IDS 2017 [48] and i-VANET [49] datasets are used to validate the proposed VANET system using machine and deep learning algorithms. The CIC-IDS 2017 dataset consists of real-world traffic with attacks and nonattack data from various vehicle nodes from the VANET system.…”

Section: Resultsmentioning

confidence: 99%

Intelligent Intrusion Detection System for VANET Using Machine Learning and Deep Learning Approaches

Karthiga

Durairaj²,

Nawaz

et al. 2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Detecting the attacks in Vehicular Ad hoc Network (VANET) system is very important to provide more secure and reliable communication between all vehicles in the system. In this article, an effective Intelligent Intrusion Detection System (IDS) is proposed using machine learning and deep learning approaches such as Adaptive Neuro Fuzzy Inference System (ANFIS) and Convolutional Neural Networks (CNN), respectively. The existing methods focus on detecting only the known attacks in VANET environment. This limitation is overcome by proposing the Intelligent IDS system using soft computing techniques. The proposed method consists of Known IDS (KIDS) and Unknown IDS (UIDS) modules, which detect both known attacks and unknown attacks. The KIDS module uses ANFIS classification module to detect the known malicious attacks, whereas the UIDS module uses a deep learning algorithm to detect the unknown attacks in VANET. Modified LeeNET (MLNET) architecture is proposed in this article to identify the type of unknown attacks. In this work, DoS attacks, Botnet attacks, PortScan attacks, and Brute Force attacks are detected using this hybrid learning algorithm. The proposed system obtains 96.9% of Pr, 98.3% of Se, 98.7% of Sp, and 98.6% of Acc and consumed 1.75 s for detecting the DoS attack on i-VANET dataset. The proposed system obtains 98.1% of Pr, 98.9% of Se, 98.1% of Sp, and 98.1% of Acc and consumed 0.95 s for detecting the Botnet attack. The proposed system obtains 98.7% of Pr, 99.1% of Se, 98.9% of Sp, and 99.2% of Acc and consumed 1.38 s for detecting the PortScan attack. The proposed system obtains 99.1of Pr, 97.8% of Se, 98.7% of Sp, and 98.5% of Acc and consumed 1.29 s for detecting the Brute Force attack. The developed methodology is tested on the real-time CIC-IDS 2017 dataset, and the experimental results are compared with other state-of-the-art methods.

show abstract

Section: Resultsmentioning

confidence: 99%

Intelligent Intrusion Detection System for VANET Using Machine Learning and Deep Learning Approaches

Karthiga

Durairaj²,

Nawaz

et al. 2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…Therefore, any latency below 100 ms has no impact on the computed control commands of our model. For a close range (<35 m between the vehicles), Reference [48] measured an average latency of less than 50 ms. Based on these measurements, we assume that the latency, including all delays, is always below 100 ms. In case a message is not received because of wireless dropout, the following vehicle could switch to the leader mode at any time.…”

Section: Discussionmentioning

confidence: 99%

Multi-Task End-to-End Self-Driving Architecture for CAV Platoons

Huch

Ongel²,

Betz

et al. 2021

Sensors

View full text Add to dashboard Cite

Connected and autonomous vehicles (CAVs) could reduce emissions, increase road safety, and enhance ride comfort. Multiple CAVs can form a CAV platoon with a close inter-vehicle distance, which can further improve energy efficiency, save space, and reduce travel time. To date, there have been few detailed studies of self-driving algorithms for CAV platoons in urban areas. In this paper, we therefore propose a self-driving architecture combining the sensing, planning, and control for CAV platoons in an end-to-end fashion. Our multi-task model can switch between two tasks to drive either the leading or following vehicle in the platoon. The architecture is based on an end-to-end deep learning approach and predicts the control commands, i.e., steering and throttle/brake, with a single neural network. The inputs for this network are images from a front-facing camera, enhanced by information transmitted via vehicle-to-vehicle (V2V) communication. The model is trained with data captured in a simulated urban environment with dynamic traffic. We compare our approach with different concepts used in the state-of-the-art end-to-end self-driving research, such as the implementation of recurrent neural networks or transfer learning. Experiments in the simulation were conducted to test the model in different urban environments. A CAV platoon consisting of two vehicles, each controlled by an instance of the network, completed on average 67% of the predefined point-to-point routes in the training environment and 40% in a never-seen-before environment. Using V2V communication, our approach eliminates casual confusion for the following vehicle, which is a known limitation of end-to-end self-driving.

show abstract

“…To develop and evaluate detection functions against location spoofing attacks, we employ the VeReMi dataset, which includes a collection of data instances with original and spoofed coordinate information [14], [15]. In VANET, each vehicle broadcasts its information in the form of Basic Safety Messages (BSMs), which occur at regular intervals (e.g., 10 times per second) [21]. One of the critical concerns in VANET is that malicious agents may inject falsified information through BSMs, which can adversely affect other agents.…”

Section: B Dataset Descriptionmentioning

confidence: 99%

Reliable Detection of Location Spoofing and Variation Attacks

et al. 2023

View full text Add to dashboard Cite

Location spoofing is a critical attack in mobile communications. While several previous studies investigated the detection of location spoofing attacks, they are limited in their performance and lack the consideration of emerging attack variations. In this paper, we present a data-driven methodology for the reliable detection of location spoofing and its variations. To enhance the performance, we introduce and utilize a new set of features, which is differential in nature and enables the checking of the mobility constraints and inconsistency. Our comparison study with the previous research shows that the presented scheme using the new features significantly improves the accuracy and reliability of the detection against location spoofing attacks. To take the possibility of attack variations into account, we establish a set of scenarios manipulating coordinate data to create attack variants. Our experimental results confirm the feasibility and effectiveness of the new features for identifying diverse types of spoofing attacks and their variations, greatly improving the detection performance by up to 99.1% accuracy. Additionally, we present a profiling-based detection approach (building the detector referring only to legitimate coordinate data), to further extend resilience to previously unseen attacks as a means to zero-day detection. The evaluation result shows the potential of the profiling-based detector with comparable or even better performance than the supervised learning methods (requiring both legitimate and falsified data to construct the detector).

show abstract

Measurement of IEEE 802.11p Performance for Basic Safety Messages in Vehicular Communications

Cited by 16 publications

References 5 publications

Intelligent Intrusion Detection System for VANET Using Machine Learning and Deep Learning Approaches

Intelligent Intrusion Detection System for VANET Using Machine Learning and Deep Learning Approaches

Multi-Task End-to-End Self-Driving Architecture for CAV Platoons

Reliable Detection of Location Spoofing and Variation Attacks

Contact Info

Product

Resources

About