Intrusion Detection in VANET Research Topics

Intrusion Detection in VANET research topic is now one of the trending topics to detect the malicious or unauthorized access in vehicle communication surroundings. Here in this research we proposed an Intrusion Detection in VANET technology to overcome the existing issues:

1. Define Intrusion Detection in VANET.

In the beginning of the research we first see the definition for our proposed research Intrusion detection in VANET technology. In this the intrusion detection comprises examining and controlling the network traffic to find any unwanted or cruel activities over the vehicular communication surroundings.

2. What is Intrusion Detection in VANET?

Next we look for the in-depth explanation for this proposed strategy, in VANET the intrusion detection defines the procedure of finding and modifying the unwanted or cruel activities over the vehicular surrounding network to make sure the protection and dependability of communication among vehicles.

3. Where Intrusion Detection in VANET used?

Succeeding the in-depth explanation we converse where to utilize this proposed strategy. It is mainly employed in the framework of making sure protection and consistency of vehicular communication. It assists to identify and prevent the cruel activities, the unwanted access and the cyber-attacks over the VANET environment.

4. Why Intrusion Detection in VANET technology proposed? , previous technology issues

Our research is to tackle the protection susceptibilities which is important in the vehicular communication networks the Intrusion detection in VANET technology is proposed. Some of the existing technologies are absent in sufficient actions to identify and modify the cyber risks, leaving VANETs vulnerable to the hazards like denial-of-service and message spoofing.

5. Algorithms / protocols

Here we provide some of the algorithms or methods that are used for this proposed technology. The methods that we utilize are Particle swarm optimization, Time-delay multipath protocol, Random forest and k means clustering.

6. Comparative study / Analysis

For our proposed research we compare various methods or techniques to obtain the best outcome when compared to the previous technologies.

• The combination of ECC-CS is used to enhance the strong message authentication, protecting the reliability of the V2V communication in the VANET surroundings.
• RF-KMC-PSO technique permits the accurate and effective identification of attacks in the V2V surroundings to make sure the protection and consistency of communication.
• The use of K-Means clustering and the HFCHBO offers the optimal cluster head selection which results in enhancing the efficient communication and resource utilization among vehicles.
• For ordering the alert messages and choosing the routes with the minimum round trip time to enhance the road safety and efficiency in the dynamic vehicle to the vehicle environment and optimize the packet delivery.
• Combining PCP-CBT that enhances the security of vehicle position and to enable the detectable and unspecified data sharing between the vehicles which adjusts the cooperation and combined intelligence and it safeguards the sensitive information.

7. Simulation results / Parameters

In this research we propose an Intrusion Detection in VANET and this can be compared with some of the existing technology methods to get the accurate results. The performance metrics that we compared are Routing overhead (%), Computational cost (ms) and Accuracy (%) with Number of vehicle nodes, and then the Total trust score with time (min) and Location privacy level with time (s).

8. Dataset LINKS / Important URL

We provide some important links that are useful when we have to interpret the concept or details about our proposed research on the basis of intrusion detection in VANET technology.

• https://www.sciencedirect.com/science/article/pii/S2214209621000048
• https://link.springer.com/article/10.1007/s11227-023-05455-1
• https://www.mdpi.com/1424-8220/23/21/8788

9. Intrusion detection in VANET Applications

Now we see the applications that will be used in our proposed technology. The intrusion detection in VANET application contains executing the security methods to identify and avoid the cyber-attacks, unwanted access and cruel activities over the vehicular communication networks. These actions make sure the consistency and unity of the interaction among the vehicles, improving the whole protection on the roads.

10. Topology for Intrusion detection in VANET

The topology for intrusion detection in VANET generally contains distributed network architecture, employing an integration of decentralized and centralized identification mechanisms. This strategy makes sure the timely response and comprehensive coverage to the possible protection dangers along the vehicular communication surroundings.

11. Environment for Intrusion detection in VANET

Let’s see the environment for this proposed intrusion detection in VANET technology, it incorporates the dynamic and uncertain nature of vehicular communication networks, which contains the factors like mobility patterns, communication interference and varying traffic densities. The effective IDS will adjust these environmental difficulties to precisely find and lessen the protection risks.

12. Simulation tools

The intrusion detection in the VANET environment incorporates the following software requirements for this research. This environment is developed by using the tools NS3. The environment is proposed by implementing the C++ or python programming language and is developed by the environment Ubuntu 16.04 LTS.

13. Results

Intrusion Detection in VANET technology is utilized to detect unauthorized or any criminal activities in the computer network. We proposed this to our research by overcoming the existing technology issues and offering a best outcome for this research. This research can be implemented by using the language C++ or Python and is operated by the system OS Ubuntu 16.04 LTS.

Intrusion detection in VANET Research Ideas:

The following are the research topics that are related to the intrusion detection in VANET technology. These research topics are useful when the queries to be arise during this research:

1. Machine Learning-Based Intrusion Detection System for Big Data Analytics in VANET
2. Intrusion Detection Using Soft Computing Techniques in VANETs
3. Sum up Work on Intrusion Detection System in Vehicular Ad-hoc Networks
4. NIDS: Random Forest Based Novel Network Intrusion Detection System for Enhanced Cybersecurity in VANET’s
5. Intrusion Detection in VANETs and ACVs using Deep Learning
6. Intrusion Detection System for SDN based VANETs Using A Deep Belief Network, Decision Tree, and ToN -IoT Dataset
7. Collaborative Intrusion Detection for VANETs: A Deep Learning-Based Distributed SDN Approach
8. VANETs based Intrusion Detection System for False Message Identification
9. LSTM-Based Intrusion Detection System for VANETs: A Time Series Classification Approach to False Message Detection
10. Intrusion Detection in VANETs
11. Evaluation of VANET Datasets in Context of an Intrusion Detection System
12. Intelligent Hierarchical Intrusion Detection System for VANETs
13. Multibranch Reconstruction Error (MbRE) Intrusion Detection Architecture for Intelligent Edge-Based Policing in Vehicular Ad-Hoc Networks
14. An Analytical Study on Intrusion Detection System in Integrated Vehicular Ad-Hoc Network Attacks
15. Machine Learning Technique of Intrusion Detection System for Vehicular Ad Hoc Networks : An Analysis
16. Intrusion Detection Systems Based on Stacking Ensemble Learning in VANET
17. Privacy-Preserving Attribute-Based Access Control Scheme with Intrusion Detection and Policy Hiding for Data Sharing in VANET
18. Intrusion Detection System-Based Security Mechanism for Vehicular Ad-Hoc Networks for Industrial IoT
19. Intrusion Detection System Using Machine Learning for Vehicular Ad Hoc Networks Based on ToN-IoT Dataset
20. Securing VANETs: Multi-Objective Intrusion Detection With Variational Autoencoders
21. NIDS-VSB: Network Intrusion Detection System for VANET using Spark-Based Big Data Optimization and Transfer Learning
22. An authentication approach in SDN-VANET architecture with Rider-Sea Lion optimized neural network for intrusion detection
23. A Hybrid Data-driven Model for Intrusion Detection in VANET
24. IDS-XGbFS: a smart intrusion detection system using XGboostwith recent feature selection for VANET safety
25. A hybrid machine learning model for intrusion detection in VANET
26. Enhancing intrusion detection using coati optimization algorithm with deep learning on vehicular Adhoc networks
27. Machine Learning-driven optimization for SVM-based intrusion detection system in vehicular ad hoc networks
28. IDS-XGbFS: a smart intrusion detection system using XGboostwith recent feature selection for VANET safety
29. A Proposed Machine Learning Model for Intrusion Detection in VANET
30. A hybrid machine learning model for intrusion detection in VANET
31. A Machine Learning Framework for Intrusion Detection in VANET Communications
32. Enhancing intrusion detection using coati optimization algorithm with deep learning on vehicular Adhoc networks
33. Machine Learning-driven optimization for SVM-based intrusion detection system in vehicular ad hoc networks
34. Intrusion Detection for Vehicular Ad Hoc Network Based on Deep Belief Network
35. RSU-Based Online Intrusion Detection and Mitigation for VANET
36. Recent Advances in Machine-Learning Driven Intrusion Detection in Transportation: Survey
37. An authentication approach in SDN-VANET architecture with Rider-Sea Lion optimized neural network for intrusion detection
38. Machine Learning-Driven Optimization for Intrusion Detection in Smart Vehicular Networks
39. Collaborative Intrusion Detection System for SDVN: A Fairness Federated Deep Learning Approach
40. Intrusion Detection in the Automotive Domain: A Comprehensive Review
41. Performance Evaluation of Intrusion Detection System Using Gradient Boost
42. Enhanced Intrusion Detection System Based on AutoEncoder Network and Support Vector Machine
43. An Intrusion Detection System Using the XGBoost Algorithm for SDVN
44. Intrusion Detection Technology of Internet of Vehicles Based on Deep Learning
45. CascadMLIDS: A Cascaded Machine Learning Framework for Intrusion Detection System in VANET
46. Flow-based intrusion detection system in Vehicular Ad hoc Network using context-aware feature extraction
47. Vehicular Network Intrusion Detection Using a Cascaded Deep Learning Approach with Multi-Variant Metaheuristic
48. Artificial Intelligence-based intrusion detection system for V2V communication in vehicular adhoc networks
49. Intrusion detection game for ubiquitous security in vehicular networks: A signaling game based approach
50. Demystifying In-Vehicle Intrusion Detection Systems: A Survey of Surveys and a Meta-Taxonomy