Summary of Machine Learning-based Malicious Vehicle Detection For Security Threats and Attacks in Vehicle Ad-hoc Network (vanet) Communications, by Thanh Nguyen Canh and Xiem Hoangvan
Machine Learning-Based Malicious Vehicle Detection for Security Threats and Attacks in Vehicle Ad-hoc Network (VANET) Communications
by Thanh Nguyen Canh, Xiem HoangVan
First submitted to arxiv on: 16 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR); Networking and Internet Architecture (cs.NI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research proposes a machine learning-based solution to detect blackhole attacks in Vehicle Ad-hoc Networks (VANET). With growing concerns about VANET security, this study creates a comprehensive dataset of normal and malicious traffic flows. The authors identify key features to differentiate between benign and malicious nodes, and evaluate six machine learning algorithms: Gradient Boosting, Random Forest, Support Vector Machines, k-Nearest Neighbors, Gaussian Naive Bayes, and Logistic Regression. Experimental results show the effectiveness of these algorithms in distinguishing normal from malicious nodes. This work highlights the potential for machine learning to enhance VANET security by detecting and mitigating blackhole attacks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper finds a way to make Vehicle Ad-hoc Networks safer. It does this by creating a special set of data that includes both good and bad traffic flows. The researchers then look at what makes these different, and use those differences to create ways to spot the bad traffic. They test different machines learning tools to see which ones work best. The results show that some of these tools are really good at telling apart good and bad traffic. This is important because Vehicle Ad-hoc Networks need to be protected from people who might try to make them stop working or steal information. |
Keywords
* Artificial intelligence * Boosting * Logistic regression * Machine learning * Naive bayes * Random forest
