This research proposes a dynamic resource allocation method for vehicle-to-everything (V2X) communications in the six generation (6G) cellular networks. Cellular V2X (C-V2X) communications empower advanced applications but at the same time bring unprecedented challenges in how to fully utilize the limited physical-layer resources, given the fact that most of the applications require both ultra low latency, high data rate and high reliability. Resource allocation plays a pivotal role to satisfy such requirements as well as guarantee quality of service (QoS). Based on this observation, a novel fuzzy-logic-assisted Q learning model (FAQ) is proposed to intelligently and dynamically allocate resources by taking advantage of the centralized allocation mode. The proposed FAQ model reuses the resources to maximize the network throughput while minimizing the interference caused by concurrent transmissions. The fuzzy-logic module expedites the learning and improves the performance of the Q-learning. A mathematical model is developed to analyze the network throughput considering the interference. To evaluate the performance, a system model for V2X communications is built for urban areas, where various V2X services are deployed in the network. Simulation results show that the proposed FAQ algorithm can significantly outperform deep reinforcement learning, Q learning and other advanced allocation strategies regarding the convergence speed and the network throughput.
more »
« less
Denial-of-Service Attacks on C-V2X Networks
Cellular Vehicle-to-Everything (C-V2X) networks are increasingly adopted by automotive original equipment manufacturers (OEMs). C-V2X, as defined in 3GPP Release 14 Mode 4, allows vehicles to self-manage the network in absence of a cellular base-station. Since C-V2X networks convey safety-critical messages, it is crucial to assess their security posture. This work contributes a novel set of Denial-of-Service (DoS) attacks on C-V2X networks operating in Mode 4. The attacks are caused by adversarial resource block selection and vary in sophistication and efficiency. In particular, we consider "oblivious" adversaries that ignore recent transmission activity on resource blocks, "smart" adversaries that do monitor activity on each resource block, and "cooperative" adversaries that work together to ensure they attack different targets. We analyze and simulate these attacks to showcase their effectiveness. Assuming a fixed number of attackers, we show that at low vehicle density, smart and cooperative attacks can significantly impact network performance, while at high vehicle density, oblivious attacks are almost as effective as the more sophisticated attacks.
more »
« less
- Award ID(s):
- 1908807
- NSF-PAR ID:
- 10300043
- Date Published:
- Journal Name:
- Third International Workshop on Automotive and Autonomous Vehicle Security (AutoSec) 2021
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
With the wider adoption of edge computing services, intelligent edge devices, and high-speed V2X communication, compute-intensive tasks for autonomous vehicles, such as object detection using camera, LiDAR, and/or radar data, can be partially offloaded to road-side edge servers. However, data privacy becomes a major concern for vehicular edge computing, as sensitive sensor data from vehicles can be observed and used by edge servers. We aim to address the privacy problem by protecting both vehicles’ sensor data and the detection results. In this paper, we present vehicle–edge cooperative deep-learning networks with privacy protection for object-detection tasks, named vePOD for short. In vePOD, we leverage the additive secret sharing theory to develop secure functions for every layer in an object-detection convolutional neural network (CNN). A vehicle’s sensor data is split and encrypted into multiple secret shares, each of which is processed on an edge server by going through the secure layers of a detection network. The detection results can only be obtained by combining the partial results from the participating edge servers. We have developed proof-of-concept detection networks with secure layers: vePOD Faster R-CNN (two-stage detection) and vePOD YOLO (single-stage detection). Experimental results on public datasets show that vePOD does not degrade the accuracy of object detection and, most importantly, it protects data privacy for vehicles. The execution of a vePOD object-detection network with secure layers is orders of magnitude faster than the existing approaches for data privacy. To the best of our knowledge, this is the first work that targets privacy protection in object-detection tasks with vehicle–edge cooperative computing.more » « less
-
More than 150 cellular networks worldwide have rolled out LTE-M (LTE-Machine Type Communication) and/or NB-IoT (Narrow Band Internet of Things) technologies to support massive IoT services such as smart metering and environmental monitoring. Such cellular IoT services share the existing cellular network architecture with non-IoT (e.g., smartphone) ones. When they are newly integrated into the cellular network, new security vulnerabilities may happen from imprudent integration. In this work, we explore the security vulnerabilities of the cellular IoT from both system-integrated and service-integrated aspects. We discover several vulnerabilities spanning cellular standard design defects, network operation slips, and IoT device implementation flaws. Threateningly, they allow an adversary to remotely identify IP addresses and phone numbers assigned to cellular IoT devices, interrupt their power saving services, and launch various attacks, including data/text spamming, battery draining, device hibernation against them. We validate these vulnerabilities over five major cellular IoT carriers in the U.S. and Taiwan using their certified cellular IoT devices. The attack evaluation result shows that the adversary can raise an IoT data bill by up to $226 with less than 120 MB spam traffic, increase an IoT text bill at a rate of $5 per second, and prevent an IoT device from entering/leaving power saving mode; moreover, cellular IoT devices may suffer from denial of IoT services. We finally propose, prototype, and evaluate recommended solutions.more » « less
-
There is an increase in usage of smaller cells or femtocells to improve performance and coverage of next-generation heterogeneous wireless networks (HetNets). However, the interference caused by femtocells to neighboring cells is a limiting performance factor in dense HetNets. This interference is being managed via distributed resource allocation methods. However, as the density of the network increases so does the complexity of such resource allocation methods. Yet, unplanned deployment of femtocells requires an adaptable and self-organizing algorithm to make HetNets viable. As such, we propose to use a machine learning approach based on Q-learning to solve the resource allocation problem in such complex networks. By defining each base station as an agent, a cellular network is modeled as a multi-agent network. Subsequently, cooperative Q-learning can be applied as an efficient approach to manage the resources of a multi-agent network. Furthermore, the proposed approach considers the quality of service (QoS) for each user and fairness in the network. In comparison with prior work, the proposed approach can bring more than a four-fold increase in the number of supported femtocells while using cooperative Q-learning to reduce resource allocation overhead.more » « less
-
null (Ed.)Recent self-propagating malware (SPM) campaigns compromised hundred of thousands of victim machines on the Internet. It is challenging to detect these attacks in their early stages, as adversaries utilize common network services, use novel techniques, and can evade existing detection mechanisms. We propose PORTFILER (PORT-Level Network Traffic ProFILER), a new machine learning system applied to network traffic for detecting SPM attacks. PORTFILER extracts port-level features from the Zeek connection logs collected at a border of a monitored network, applies anomaly detection techniques to identify suspicious events, and ranks the alerts across ports for investigation by the Security Operations Center (SOC). We propose a novel ensemble methodology for aggregating individual models in PORTFILER that increases resilience against several evasion strategies compared to standard ML baselines. We extensively evaluate PORTFILER on traffic collected from two university networks, and show that it can detect SPM attacks with different patterns, such as WannaCry and Mirai, and performs well under evasion. Ranking across ports achieves precision over 0.94 and false positive rates below 8 × 10−4 in the top 100 highly ranked alerts. When deployed on the university networks, PORTFILER detected anomalous SPM-like activity on one of the campus networks, confirmed by the university SOC as malicious. PORTFILER also detected a Mirai attack recreated on the two university networks with higher precision and recall than deep learning based autoencoder methods.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
