A survey on machine learning-based misbehavior detection systems for 5g and beyond vehicular networks
A Boualouache, T Engel - IEEE Communications Surveys & …, 2023 - ieeexplore.ieee.org
Advances in Vehicle-to-Everything (V2X) technology and onboard sensors have significantly
accelerated deploying Connected and Automated Vehicles (CAVs). Integrating V2X with 5G …
accelerated deploying Connected and Automated Vehicles (CAVs). Integrating V2X with 5G …
Demystifying in-vehicle Intrusion Detection Systems: A survey of surveys and a meta-taxonomy
Breaches in the cyberspace due to cyber-physical attacks can harm the physical space, and
any type of vehicle is an alluring target for wrongdoers for an assortment of reasons …
any type of vehicle is an alluring target for wrongdoers for an assortment of reasons …
HDL-IDS: A hybrid deep learning architecture for intrusion detection in the internet of vehicles
Internet of Vehicles (IoV) is an application of the Internet of Things (IoT) network that
connects smart vehicles to the internet, and vehicles with each other. With the emergence of …
connects smart vehicles to the internet, and vehicles with each other. With the emergence of …
An enhanced multi-stage deep learning framework for detecting malicious activities from autonomous vehicles
IA Khan, N Moustafa, D Pi, W Haider… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Intelligent Transportation Systems (ITS), particularly Autonomous Vehicles (AVs), are
susceptible to safety and security concerns that impend people's lives. Nothing like manually …
susceptible to safety and security concerns that impend people's lives. Nothing like manually …
Pwpae: An ensemble framework for concept drift adaptation in iot data streams
As the number of Internet of Things (IoT) devices and systems have surged, IoT data
analytics techniques have been developed to detect malicious cyber-attacks and secure IoT …
analytics techniques have been developed to detect malicious cyber-attacks and secure IoT …
BoostedEnML: Efficient technique for detecting cyberattacks in IoT systems using boosted ensemble machine learning
Following the recent advances in wireless communication leading to increased Internet of
Things (IoT) systems, many security threats are currently ravaging IoT systems, causing …
Things (IoT) systems, many security threats are currently ravaging IoT systems, causing …
[HTML][HTML] An edge based hybrid intrusion detection framework for mobile edge computing
Abstract The Mobile Edge Computing (MEC) model attracts more users to its services due to
its characteristics and rapid delivery approach. This network architecture capability enables …
its characteristics and rapid delivery approach. This network architecture capability enables …
Semi-supervised federated learning based intrusion detection method for internet of things
Federated learning (FL) has become an increasingly popular solution for intrusion detection
to avoid data privacy leakage in Internet of Things (IoT) edge devices. Existing FL-based …
to avoid data privacy leakage in Internet of Things (IoT) edge devices. Existing FL-based …
IoT data analytics in dynamic environments: From an automated machine learning perspective
With the wide spread of sensors and smart devices in recent years, the data generation
speed of the Internet of Things (IoT) systems has increased dramatically. In IoT systems …
speed of the Internet of Things (IoT) systems has increased dramatically. In IoT systems …
TCAN-IDS: intrusion detection system for internet of vehicle using temporal convolutional attention network
P Cheng, K Xu, S Li, M Han - Symmetry, 2022 - mdpi.com
Intrusion detection systems based on recurrent neural network (RNN) have been considered
as one of the effective methods to detect time-series data of in-vehicle networks. However …
as one of the effective methods to detect time-series data of in-vehicle networks. However …