@article {2023, title = {Efficient Unmanned Aerial Systems Navigation With Collision Avoidance in Dense Urban Environments}, journal = {IEEE Transactions on Intelligent Transportation Systems (T-ITS)}, volume = {24}, year = {2023}, month = {August}, abstract = {

Unmanned Aerial Systems (UAS) are an emerging type of airborne traffic under active research expected to carry cargo and passengers in the future over dense population centers. One challenge is identifying algorithms which can compactly represent and navigate the available airspace while avoiding conflict with buildings and other UAS. In this paper, we explore a decentralized method coupling a medial axis graph for global navigation through the city with local collision avoidance of buildings and other UAS to obtain collision-free efficient navigation through an urban environment. We study trade-offs of using Optimal Reciprocal Collision Avoidance (ORCA), Rapidly-exploring Random Trees (RRT and RRT*), and Fast Markov Decision Process (FastMDP) as the collision avoidance algorithms. We examine low-altitude UAS navigating through a portion of New York City dense with skyscrapers to study the effectiveness of the algorithms in a challenging environment. We show that ORCA, RRT, RRT*, and FastMDP all are fairly efficient for 2D problems, but as the problem becomes more realistic (3D, constrained motion, aware of other UAS), FastMDP provides the best overall performance among the four algorithms studied.

}, author = {Joshua Bertram and Joseph Zambreno and Peng Wei} } @article {2021, title = {A Fast Markov Decision Process based Algorithm for Collision Avoidance in Urban Air Mobility}, journal = {IEEE Transactions on Intelligent Transportation Systems (T-ITS)}, volume = {23}, year = {2022}, month = {September}, abstract = {
Multiple aircraft collision avoidance is a challenging problem due to a stochastic environment and uncertainty in the intent of other aircraft. Traditionally a layered approach to collision avoidance has been employed using a centralized air traffic control system, established rules of the road, separation assurance, and last minute pairwise collision avoidance. With the advent of Urban Air Mobility (air taxis), the expected increase in traffic density in urban environments, short time scales, and small distances between aircraft favor decentralized decision making on-board the aircraft. In this paper, we present a Markov Decision Process (MDP) based method, named FastMDP, which can solve a certain subclass of MDPs quickly, and demonstrate using the algorithm online to safely maintain separation and avoid collisions with multiple aircraft (1-on-n) while remaining computationally efficient. We compare the FastMDP algorithm\’s performance against two online collision avoidance algorithms that have been shown to be both efficient and scale to large numbers of aircraft: Optimal Reciprocal Collision Avoidance (ORCA) and Monte Carlo Tree Search (MCTS). Our simulation results show that under the assumption that aircraft do not have perfect knowledge of other aircraft intent FastMDP outperforms ORCA and MCTS in collision avoidance behavior in terms of loss of separation and near mid-air collisions while being more computationally efficient. We further show that in our simulation FastMDP behaves nearly as well as MCTS with perfect knowledge of other aircraft intent. Our results show that FastMDP is a promising algorithm for collision avoidance that is also computationally efficient.
}, author = {Joshua Bertram and Peng Wei and Joseph Zambreno} } @article {EzeOlu20A, title = {Reverse Engineering Controller Area Network Messages using Unsupervised Machine Learning}, journal = {IEEE Consumer Electronics Magazine}, volume = {11}, year = {2022}, month = {January}, abstract = {

The smart city landscape is rife with opportunities for mobility and economic optimization, but also presents many security concerns spanning the range of components and systems in the smart ecosystem. One key enabler for this ecosystem is smart transportation and transit, which is foundationally built upon connected vehicles. Ensuring vehicular security, while necessary to guarantee passenger and pedestrian safety, is itself challenging due to the broad attack surfaces of modern automotive systems. A single car contains dozens to hundreds of small embedded computing devices known as electronic control units (ECUs) executing 100s of millions of lines of code; the inherent complexity of this tightly-integrated cyber-physical system (CPS) is one of the key problems that frustrates effective security. We describe an approach to help reduce the complexity of security analyses by leveraging unsupervised machine learning to learn clusters of messages passed between ECUs that correlate with changes in the CPS state of a vehicle as it moves through the world. Our approach can help to improve the security of vehicles in a smart city, and can leverage smart city infrastructure to further enrich and refine the quality of the machine learning output.

}, author = {Uchenna Ezeobi and Habeeb Olufowobi and Clinton Young and Joseph Zambreno and Gedare Bloom} } @article {QasDen20A, title = {Benchmarking Vision Kernels and Neural Network Inference Accelerators on Embedded Platforms}, journal = {Journal of Systems Architecture}, volume = {113}, year = {2021}, month = {February}, abstract = {

Developing efficient embedded vision applications requires exploring various algorithmic optimization trade-offs and a broad spectrum of hardware architecture choices. This makes navigating the solution space and finding the design points with optimal performance trade-offs a challenge for developers. To help provide a fair baseline comparison, we conducted comprehensive benchmarks of accuracy, run-time, and energy efficiency of a wide range of vision kernels and neural networks on multiple embedded platforms: ARM57 CPU, Nvidia Jetson TX2 GPU and Xilinx ZCU102 FPGA. Each platform utilizes their optimized libraries for vision kernels (OpenCV, VisionWorks and xfOpenCV) and neural networks (OpenCV DNN, TensorRT and Xilinx DPU). For vision kernels, our results show that the GPU achieves an energy/frame reduction ratio of 1.1\–3.2x compared to the others for simple kernels. However, for more complicated kernels and complete vision pipelines, the FPGA outperforms the others with energy/frame reduction ratios of 1.2\–22.3x. For neural networks [Inception-v2 and ResNet-50, ResNet-18, Mobilenet-v2 and SqueezeNet], it shows that the FPGA achieves a speed up of [2.5, 2.1, 2.6, 2.9 and 2.5]x and an EDP reduction ratio of [1.5, 1.1, 1.4, 2.4 and 1.7]x compared to the GPU FP16 implementations, respectively.

}, author = {Murad Qasaimeh and Kristof Denolf and Alireza Khodamoradi and Michaela Blott and Jack Lo and Lisa Halder and Kees Vissers and Joseph Zambreno and Phillip Jones} } @conference {BerZam21A, title = {Scalable FastMDP for Pre-departure Airspace Reservation and Strategic De-conflict}, booktitle = {Proceedings of the AIAA SciTech Forum}, year = {2021}, month = {January}, abstract = {

Pre-departure flight plan scheduling for Urban Air Mobility (UAM) and cargo delivery drones will require on-demand scheduling of large numbers of aircraft. We demonstrate an algorithm known as FastMDP-GPU that performs first-come-first-served pre-departure flight plan scheduling where conflict free flight plans are generated on demand. We demonstrate a parallelized implementation of the algorithm on a Graphics Processor Unit (GPU) and show the level of performance and scaling that can be achieved. Our results show that on commodity GPU hardware we can perform flight plan scheduling against 2000-3000 known flight plans and with server-class hardware the performance can be higher. Our results show promise for implementing a large scale UAM scheduler capable of performing on-demand flight scheduling that would be suitable for both centralized or distributed flight planning systems.

}, author = {Joshua Bertram and Joseph Zambreno and Peng Wei} } @article {OluYou19A, title = {SAIDuCANT: Specification-based Automotive Intrusion Detection using Controller Area Network (CAN) Timing}, journal = {IEEE Transactions on Vehicular Technology}, volume = {69}, year = {2020}, month = {February}, abstract = {The proliferation of embedded devices in modern vehicles has opened the traditionally-closed vehicular system to the risk of cybersecurity attacks through physical and remote access to the in-vehicle network such as the controller area network (CAN). The CAN bus does not implement a security protocol that can protect the vehicle against the increasing cyber and physical attacks. To address this risk, we introduce a novel algorithm to extract the real-time model parameters of the CAN bus and develop SAIDuCANT, a specification-based intrusion detection system (IDS) using anomaly-based supervised learning with the real-time model as input.We evaluate the effectiveness of SAIDuCANT with real CAN logs collected from two passenger cars and on an open source CAN dataset collected from real-world scenarios. Experimental results show that SAIDuCANT can effectively detect data injection attacks with low false positive rates and outperforms other detection approaches using the timing features of CAN messages.}, author = {Habeeb Olufowobi and Clinton Young and Joseph Zambreno and Gedare Bloom} } @conference {OluEze19A, title = {Anomaly Detection Approach Using Adaptive Cumulative Sum Algorithm for Controller Area Network}, booktitle = {Proceedings of the ACM Workshop on Automotive Cybersecurity (AutoSec)}, year = {2019}, month = {March}, abstract = {The modern vehicle has transformed from a purely mechanical system to a system that embeds several electronic devices. These devices communicate through the in-vehicle network for enhanced safety and comfort but are vulnerable to cyber-physical risks and attacks. A well-known technique of detecting these attacks and unusual events is by using intrusion detection systems. Anomalies in the network occur at unknown points and produce abrupt changes in the statistical features of the message stream. In this paper, we propose an anomaly-based intrusion detection approach using the cumulative sum (CUSUM) change-point detection algorithm to detect data injection attacks on the controller area network (CAN) bus. We leverage the parameters required for the change-point algorithm to reduce false alarm rate and detection delay. Using real dataset generated from a car in normal operation, we evaluate our detection approach on three different kinds of attack scenarios.}, author = {Habeeb Olufowobi and Uchenna Ezeobi and Eric Muhati and Gaylon Robinson and Clinton Young and Joseph Zambreno and Gedare Bloom} } @conference {YouOlu19A, title = {Automotive Intrusion Detection Based on Constant CAN Message Frequencies Across Vehicle Driving Modes}, booktitle = {Proceedings of the ACM Workshop on Automotive Cybersecurity (AutoSec)}, year = {2019}, month = {March}, abstract = {The modern automobile relies on numerous electronic control units communicating over the de facto standard of the controller area network (CAN) bus. This communication network was not developed with cybersecurity in mind. Many methods based on constant time intervals between messages have been proposed to address this lack of security issue with the CAN bus. However, these existing methods may struggle to handle variable time intervals between messages during transitions of vehicle driving modes. This paper proposes a simple and cost-effective method to ensure the security of the CAN bus that is based on constant message frequencies across vehicle driving modes. This proposed method does not require any modifications on the existing CAN bus and it is designed with the intent for efficient execution in platforms with very limited computational resources. Test results with the proposed method against two different vehicles and a frequency domain analysis are also presented in the paper.}, author = {Clinton Young and Habeeb Olufowobi and Gedare Bloom and Joseph Zambreno} } @article {YouOlu19B, title = {Survey of Automotive Controller Area Network Intrusion Detection Systems}, journal = {IEEE Design and Test}, volume = {36}, year = {2019}, month = {December}, abstract = {Control Area Network (CAN) is one of the most popular targets for malicious attacks and exploitations in modern automotive systems. The goal of intrusion detection systems (IDS) is to identify and mitigate security attacks; consequently, they are of paramount importance to automotive security. This article surveys the state of the art in IDS, with special emphasis on techniques for detecting attacks on CAN modules.}, author = {Clinton Young and Habeeb Olufowobi and Gedare Bloom and Joseph Zambreno} } @conference {OluBlo18A, title = {Work-in-Progress: Real-Time Modeling for Intrusion Detection in Automotive Controller Area Network}, booktitle = {Proceedings of the IEEE Real-Time Systems Symposium (RTSS)}, year = {2018}, month = {December}, abstract = {Security of vehicular networks has often been an afterthought since they are designed traditionally to be a closed system. An attack could lead to catastrophic effect which may include loss of human life or severe injury to the driver and passengers of the vehicle. In this paper, we propose a novel algorithm to extract the real-time model of the controller area network (CAN) and develop a specification-based intrusion detection system (IDS) using anomaly-based supervised learning with the real-time model as input. We evaluate IDS performance with real CAN logs collected from a sedan car.}, author = {Habeeb Olufowobi and Gedare Bloom and Clinton Young and Joseph Zambreno} } @conference {YouZam16A, title = {Towards a Fail-Operational Intrusion Detection System for In-Vehicle Networks}, booktitle = {Proceedings of the Workshop on Security and Dependability of Critical Embedded Real-Time Systems (CERTS)}, year = {2016}, month = {November}, abstract = {The landscape of automotive in-vehicle networks is changing driven by the vast options for infotainment features and progress toward fully-autonomous vehicles. However, the security of automotive networks is lagging behind feature-driven technologies, and new vulnerabilities are constantly being discovered. In this paper, we introduce a road map towards a security solution for in-vehicle networks that can detect anomalous and failed states of the network and adaptively respond in real-time to maintain a fail-operational system.}, author = {Clinton Young and Joseph Zambreno and Gedare Bloom} } @article {BauSte10A, title = {A Case Study in Hardware Trojan Design and Implementation}, journal = {International Journal of Information Security (IJIS)}, volume = {10}, number = {1}, year = {2011}, pages = {1-14}, abstract = {As integrated circuits (ICs) continue to have an overwhelming presence in our digital information-dominated world, having trust in their manufacture and distribution mechanisms is crucial. However, with ever-shrinking transistor technologies, the cost of new fabrication facilities is becoming prohibitive, pushing industry to make greater use of potentially less reliable foreign sources for their IC supply. The 2008 Computer Security Awareness Week (CSAW) Embedded Systems Challenge at the Polytechnic Institute of NYU highlighted some of the vulnerabilities of the IC supply chain in the form of a hardware hacking challenge. This paper explores the design and implementation of our winning entry.}, author = {Alex Baumgarten and Michael Steffen and Matthew Clausman and Joseph Zambreno} } @article {BauTya10A, title = {Preventing Integrated Circuit Piracy using Reconfigurable Logic Barriers}, journal = {IEEE Design and Test of Computers}, volume = {27}, number = {1}, year = {2010}, month = {January}, pages = {66-75}, abstract = {Hardware metering to prevent IC piracy is a challenging and important problem. The authors propose a combinational locking scheme based on intelligent placement of the barriers throughout the design in which the objective is to maximize the effectiveness of the barriers and to minimize the overhead.}, author = {Alex Baumgarten and Akhilesh Tyagi and Joseph Zambreno} } @conference {SatBau09A, title = {Architectural Support for Automated Software Attack Detection, Recovery, and Prevention}, booktitle = {Proceedings of the International Conference on Embedded and Ubiquitous Computing (EUC)}, year = {2009}, month = {August}, abstract = {Attacks on software systems are an increasingly serious problem from an economic and security standpoint. Many techniques have been proposed ranging from simple compiler modifications to full-scale re-engineering of computer systems architecture aimed at attack detection. Traditional techniques ignore the arguably more important problem of graceful recovery. Without recovery, even a successful attack detection can become an effective Denial-of-Service. We propose an architectural approach to attack detection and recovery called rollback and huddle that monitors a program{\textquoteright}s execution with a lightweight attack-detection module while continuously checkpointing the system state. In the case of an attack, the program state is rolled back to a time before the attack occurred and an additional module is loaded to identify the source of the attack, repair the original vulnerability, and prevent future attacks. The simple hardware modules work alongside a standard computer architecture and aid in attack detection, checkpoint creation, and attack recovery. Experimental results show minimal runtime overhead and resource utilization.}, author = {Jesse Sathre and Alex Baumgarten and Joseph Zambreno} }