# A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks

arXiv: https://arxiv.org/abs/2505.21703 · HTML: https://mlsanity.com/paper/2505.21703/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 35 references that are in the map.
- First posted on arXiv 27 May 2025.

## Builds on (most cited first, 7 of 7)

- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Deep learning for time series classification: a review](https://mlsanity.com/paper/1809.04356/) (2018)
- [PyOD: A Python Toolbox for Scalable Outlier Detection](https://mlsanity.com/paper/1901.01588/) (2019)
- [Machine Learning Based Network Vulnerability Analysis of Industrial Internet of Things](https://mlsanity.com/paper/1911.05771/) (2019)
- [Structural Attention-Based Recurrent Variational Autoencoder for Highway Vehicle Anomaly Detection](https://mlsanity.com/paper/2301.03634/) (2023)
- [Predicting machine failures from multivariate time series: an industrial case study](https://mlsanity.com/paper/2402.17804/) (2024)
- [Location Anomalies Detection for Connected and Autonomous Vehicles](https://mlsanity.com/paper/1907.00811/) (2019)

## Built on by (most cited first, 3 of 3)

- [Securing Swarms: Cross-Domain Adaptation for ROS2-based CPS Anomaly Detection](https://mlsanity.com/paper/2508.15865/) (2025)
- [Why Pool When You Can Flow? Active Learning with GFlowNets](https://mlsanity.com/paper/2509.00704/) (2025)
- [A Source Domain is All You Need: Source-Only Cross-OS Transfer Learning for APT Anomaly Detection via Semantic Alignment and Optimal Transport](https://mlsanity.com/paper/2606.10216/) (2026)

## Abstract (arXiv)

Internet of Vehicles (IoV) systems, while offering significant advancements in transportation efficiency and safety, introduce substantial security vulnerabilities due to their highly interconnected nature. These dynamic systems produce massive amounts of data between vehicles, infrastructure, and cloud services and present a highly distributed framework with a wide attack surface. In considering network-centered attacks on IoV systems, attacks such as Denial-of-Service (DoS) can prohibit the communication of essential physical traffic safety information between system elements, illustrating that the security concerns for these systems go beyond the traditional confidentiality, integrity, and availability concerns of enterprise systems. Given the complexity and volume of data generated by IoV systems, traditional security mechanisms are often inadequate for accurately detecting sophisticated and evolving cyberattacks. Here, we present an unsupervised autoencoder method trained entirely on benign network data for the purpose of unseen attack detection in IoV networks. We leverage a weighted combination of reconstruction and triplet margin loss to guide the autoencoder training and develop a diverse representation of the benign training set. We conduct extensive experiments on recent network intrusion datasets from two different application domains, industrial IoT and home IoT, that represent the modern IoV task. We show that our method performs robustly for all unseen attack types, with roughly 99% accuracy on benign data and between 97% and 100% performance on anomaly data. We extend these results to show that our model is adaptable through the use of transfer learning, achieving similarly high results while leveraging domain features from one domain to another.