Anomaly Detection in Automobile Control Network Data with Long Short-Term Memory Networks
By Adrian Taylor, Sylvain P. Leblanc, Nathalie Japkowicz. International Conference on Data Science and Advanced Analytics, 2016
323 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Networks and Communications, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/cb75fd79921fe0a5ee753fe1001c9039130b8a1d
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Izhak Golan
- Ran El-Yaniv
- Nada Sahlab
- Madhusanka Liyanage
- Quoc-Viet Pham
- Valentino Constantinou
- Benjamin Maschler
- Kyle Hundman
In Computer Networks and Communications
- Ran El-Yaniv
- Madhusanka Liyanage
- Valentino Constantinou
- Kyle Hundman
- Ian Colwell
- Helge Janicke
- Minghui Zhu
- Pardeep Kumar
In Electrical and Electronic Engineering
- Ran El-Yaniv
- Madhusanka Liyanage
- Quoc-Viet Pham
- Helge Janicke
- Minghui Zhu
- Mohamed Amine Ferrag
- Leandros A. Maglaras
- Sotiris K. Moschoyiannis
Citations in the corpus
- RoboADS: Anomaly Detection Against Sensor and Actuator Misbehaviors in Mobile Robots (2018, background): “They exploit the correlation between sensing data and detect anomaly when a discrepancy is discovered [34]–[36].”
- A Survey of Intrusion Detection for In-Vehicle Networks (2020, background): “neural network (RNN) to detect attacks with low false alarm rates [66].”
- Survey on 6G Frontiers: Trends, Applications, Requirements, Technologies and Future Research (2021, background): “Other factors such as network congestion control [126], [127], load balancing [128], network security [129], virtual resource”
- Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study (2020, background): “(2016) [32] Attacks against vehicles ROC curve 81”
- Deep Anomaly Detection Using Geometric Transformations (2018, background): “As many related works [25, 28, 14], in this paper we also focus only on learning the scoring function nS(x), and completely ignore the constrained binary decision problem.”
- Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding (2018, methodology): “These advantages have motivated the use of LSTM networks in several recent anomaly detection tasks [5, 10, 30, 31, 33, 44], where LSTM models are fit on nominal data and model predictions are compared to actual data stream values using a set of detection rules in order to detect anomalies [5, 30, 31”
- A Survey on Anomaly Detection for Technical Systems using LSTM Networks (2021, methodology): “In [22], a deep LSTM network is used as predictor of the regular bus communication behavior in vehicles.”