Malware Detection in Self-Driving Vehicles Using Machine Learning Algorithms
The recent trend for vehicles to be connected to unspecified devices, vehicles, and infrastructure increases the potential for external threats to vehicle cybersecurity. Thus, intrusion detection is a key network security function in vehicles with open connectivity, such as self-driving and connecte...
Main Authors: | Seunghyun Park, Jin-Young Choi |
---|---|
Format: | Article |
Language: | English |
Published: |
Hindawi-Wiley
2020-01-01
|
Series: | Journal of Advanced Transportation |
Online Access: | http://dx.doi.org/10.1155/2020/3035741 |
Similar Items
-
Hierarchical Anomaly Detection Model for In-Vehicle Networks Using Machine Learning Algorithms
by: Seunghyun Park, et al.
Published: (2020-07-01) -
BENCHMARKING MACHINE LEARNING ALGORITHMS FOR ANDROID MALWARE DETECTION
by: Somayyeh Fallah, et al.
Published: (2019-12-01) -
Metamorphic malware detection using machine learning
by: Ahmed Ali, Mohammed Hasan Ali
Published: (2020) -
Detecting Metamorphic Malware based on Machine Learning
by: Chen-Han Dai, et al.
Published: (2019) -
Android Malware Detection Based on Machine Learning
by: Chun-Hsuan Chang, et al.
Published: (2016)