More Like this

1. Theory and implementation of dynamic watermarking for cybersecurity of advanced transportation systems

   Ko, Woo-Hyun; Satchidanandan, Bharadwaj; and Kumar, P.R. (October 2016, 2016 IEEE Conference on Communications and Network Security (CNS))

   We consider a prototypical intelligent transportation system with a control law that is specifically designed to avoid collisions. We experimentally demonstrate that, nevertheless, an attack on a position sensor can result in collisions between vehicles. This is a consequence of the feeding of malicious sensor measurements to the controller and the collision avoidance module built into the system. This is an instance of the broader concern of cybersecurity vulnerabilities opened up by the increasing integration of critical physical infrastructures with the cyber system. We consider a solution based on “dynamic watermarking” of signals to detect and stop such attacks on cyber-physical systems. We show how dynamic watermarking can handle nonlinearities arising in vehicular models. We then experimentally demonstrate that employing this nonlinear extension indeed restores the property of collision freedom even in the presence of attacks. 

   more » « less
2. Real-Time Detection and Localization of Denial-of-Service Attacks in Heterogeneous Vehicular Networks

   https://doi.org/10.23919/DATE51398.2021.9474141  

   Dey, Manju; Patra, Moumita; Mishra, Prabhat (February 2021, Design, Automation & Test in Europe Conference & Exhibition (DATE))

   null (Ed.)

   Vehicular communication has emerged as a powerful tool for providing a safe and comfortable driving experience for users. Long Term Evolution (LTE) supports and enhances the quality of vehicular communication due to its properties such as, high data rate, spatial reuse, and low delay. However, high mobility of vehicles introduces a wide variety of security threats, including Denial-of-Service (DoS) attacks. In this paper, we propose an effective solution for real-time detection and localization of DoS attacks in an LTE-based vehicular network with mobile network components (e.g., vehicles, femto access points, etc.). We consider malicious data transmission by vehicles in two ways - using real identification (unintentional) and using fake identification. Our attack detection technique is based on data packet counter and average packet delivery ratio which helps to efficiently detect attack faster than traditional approaches. We use triangulation method for localizing the attacker, and analyze average packet delay incurred by vehicles by modelling the system as an M/M/m queue. Simulation results demonstrate that our proposed technique significantly outperforms state-of-the-art techniques. 

   more » « less
3. Denial-of-Service Attacks on C-V2X Networks

   Trkulja, Nataša; Starobinski, David; Berry, Randall (February 2021, Third International Workshop on Automotive and Autonomous Vehicle Security (AutoSec) 2021)

   null (Ed.)

   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
4. Positioning Helper Nodes to Improve Robustness of Wireless Mesh Networks to Jamming Attacks

   Feng, Jixin; Dixon, Warren E.; Shea, John M. (January 2017, IEEE Global Communications Conference)

   Wireless communication systems are susceptible to both unintentional interference and intentional jamming attacks. For mesh and ad-hoc networks, interference affects the network topology and can cause the network to partition, which may completely disrupt the applications or missions that depend on the network. Defensive techniques can be applied to try to prevent such disruptions to the network topology. Most previous research in this area is on improving network resilience by adapting the network topology when a jamming attack occurs. In this paper, we consider making a network more robust to jamming attacks before any such attack has happened. We consider a network in which the positions of most of the radios in the network are not under the control of the network operator, but the network operator can position a few “helper nodes” to add robustness against jamming. For instance, most of the nodes are radios on vehicles participating in a mission, and the helper nodes are mounted on mobile robots or UAVs. We develop techniques to determine where to position the helper nodes to maximize the robustness of the network to certain jamming attacks aimed at disrupting the network topology. Using our recent results for quickly determining how to attack a network, we use the harmony search algorithm to find helper node placements that maximize the number of jammers needed to disrupt the network 

   more » « less
5. A Grid-Based Misbehavior Detection System for Vehicular Communication Networks

   https://doi.org/10.1109/ICC51166.2024.10623059  

   Gunawardena, Chamath; Moushi, Owana Marzia; Ye, Feng; Hu, Rose Qingyang; Qian, Yi (June 2024, IEEE)

   A vehicular communication network allows vehicles on the road to be connected by wireless links, providing road safety in vehicular environments. Vehicular communication network is vulnerable to various types of attacks. Cryptographic techniques are used to prevent attacks such as message modification or vehicle impersonation. However, cryptographic techniques are not enough to protect against insider attacks where an attacking vehicle has already been authenticated in the network. Vehicular network safety services rely on periodic broadcasts of basic safety messages (BSMs) from vehicles in the network that contain important information about the vehicles such as position, speed, received signal strength (RSSI) etc. Malicious vehicles can inject false position information in a BSM to commit a position falsification attack which is one of the most dangerous insider attacks in vehicular networks. Position falsification attacks can lead to traffic jams or accidents given false position information from vehicles in the network. A misbehavior detection system (MDS) is an efficient way to detect such attacks and mitigate their impact. Existing MDSs require a large amount of features which increases the computational complexity to detect these attacks. In this paper, we propose a novel grid-based misbehavior detection system which utilizes the position information from the BSMs. Our model is tested on a publicly available dataset and is applied using five classification algorithms based on supervised learning. Our model performs multi-classification and is found to be superior compared to other existing methods that deal with position falsification attacks. 

   more » « less