Ankur Vora

Bio: Ankur Vora is an academic researcher from Binghamton University. The author has contributed to research in topics: Motion estimation & Block-matching algorithm. The author has an hindex of 3, co-authored 8 publications receiving 30 citations. Previous affiliations of Ankur Vora include VIT University.

Detecting and Mitigating Spoofing Attack Against an Automotive Radar

Abstract: Applying cyber security mechanisms, such as cryptography, trusted computing, and network intrusion detection, is insufficient to secure cyber-physical systems (CPS) such as smart vehicles, because most sensors (and actuators) are designed without security considerations and remain vulnerable to sensor spoofing attacks in the analog domain. To address this problem, we present a new approach to detect and handle sensor spoofing attack against automotive radars—a key component for assisted and autonomous driving—by extending multiple beamforming in an automotive multi-input multi-output (MIMO) radar. In a simulation study for adaptive cruise control based on the car following model, our approach significantly outperforms three state-of-the-art baselines in terms of attack detection and ranging accuracy.

Effective 5G Wireless Downlink Scheduling and Resource Allocation in Cyber-Physical Systems

Abstract: In emerging Cyber-Physical Systems (CPS), the demand for higher communication performance and enhanced wireless connectivity is increasing fast. To address the issue, in our recent work, we proposed a dynamic programming algorithm with polynomial time complexity for effective cross-layer downlink Scheduling and Resource Allocation (SRA) considering the channel and queue state, while supporting fairness. In this paper, we extend the SRA algorithm to consider 5G use-cases, namely enhanced Machine Type Communication (eMTC), Ultra-Reliable Low Latency Communication (URLLC) and enhanced Mobile BroadBand (eMBB). In a simulation study, we evaluate the performance of our SRA algorithm in comparison to an advanced greedy cross-layer algorithm for eMTC, URLLC and LTE (long-term evolution). For eMTC and URLLC, our SRA method outperforms the greedy approach by up to 17.24%, 18.1%, 2.5% and 1.5% in terms of average goodput, correlation impact, goodput fairness and delay fairness, respectively. In the case of LTE, our approach outperforms the greedy method by 60%, 2.6% and 1.6% in terms of goodput, goodput fairness and delay fairness compared with tested baseline.

CSI Classification for 5G via Deep Learning

Abstract: 5G communication requires continuous exchanges of channel state information (CSI) between the base station and user equipment (UE) to adjust the physical layer parameters. CSI classification in a noisy environment is challenging, since CSI can get corrupted. To address this problem, we apply a convolutional neural network (CNN) to classify several key CSI parameters. In a simulation study, our CNN method classifies the CSI parameters with accuracy ranging between 84-98\%, which is approximately 24-38\% higher than the 3GPP recommendations for UEs.

Throughput Enhancement via Multi-Armed Bandit in Heterogeneous 5G Networks

Abstract: In heterogeneous networks, a user equipment (UE) can directly communicate with the macro base station (BS) or a small, low-power pico or femto BS. Alternatively, it can indirectly communicate with the macro BS through one or more intermediate device (UE) or a relay-station that uses the over-the-air backhaul to the macro BS. Due to the highly dynamic and uncertain nature of wireless communication, it is essential for a UE to choose an optimal communication mode and a neighbor to which it connects, e.g., a macro/small BS in the direct communication mode or a nearby relay/device in the indirect communication mode. In this paper, we apply an effective reinforcement learning method, called multi-armed bandit (MAB), to shed light on this problem. Especially, we apply MAB supported by the Thompson sampling theorem to pick an optimal arm—a neighbor that determines the communication mode and resulting performance, while effectively dealing with the exploration-exploitation dilemma in MAB. In a simulation study undertaken in Matlab, we compare the performance of the proposed approach to several baselines representing the current state of the art. Our approach enhances the throughput normalized to the optimal throughput by approximately 8-97% compared to several baselines representing the state of the art. Further, it improves the throughput by up to 15% compared to the best performing baseline [1], [2].

Index Modulation with PAPR and Beamforming for 5G MIMO-OFDM

Abstract: Although key techniques for next-generation wireless communication have been explored separately, relatively little work has been done to investigate their potential cooperation for performance optimization. To address this problem, we propose a holistic framework for robust 5G communication based on multiple-input-multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM). More specifically, we design a new framework that supports: 1) index modulation based on OFDM (OFDM–M) [1]; 2) sub-band beamforming and channel estimation to achieve massive path gains by exploiting multiple antenna arrays [2]; and 3) sub-band pre-distortion for peak-to-average-power-ratio (PAPR) reduction [3] to significantly decrease the PAPR and communication errors in OFDM-IM by supporting a linear behavior of the power amplifier in the modem. The performance of the proposed framework is evaluated against the state-of-the-art QPSK, OFDM-IM [1] and QPSK-spatiotemporal QPSK-ST [2] schemes. The results show that our framework reduces the bit error rate (BER), mean square error (MSE) and PAPR compared to the baselines by approximately 6–13dB, 8–13dB, and 50%, respectively.

A Survey on the Current Security Landscape of Intelligent Transportation Systems

Abstract: With the proliferation of embedded technologies and wireless capabilities, today’s vehicles are no longer isolated mechanical machines. They become part of a hyper-connected system -Intelligent Transportation Systems (ITS)- that has the potential to support multiple levels of autonomy and intelligence improving considerably the safety, efficiency, and sustainability of transportation networks. However, this raises new security issues that make the whole system prone to cybersecurity attacks that threaten both the safety and privacy of all road-users. This article gives a short background tutorial on the main security issues and the different attacks that hinder Intelligent Transport Systems. To enable secure and safe ITS applications, this article provides a comprehensive analysis of existing solutions and highlights their strengths and limitations. Finally, this survey presents key challenges in the field, and discusses recent trends that must be factored in by researchers, implementers, and car manufactures to improve the security of ITS.

Cybersecurity Attacks in Vehicular Sensors

Abstract: Today’s modern vehicles contain anywhere from sixty to one-hundred sensors and exhibit the characteristics of Cyber-Physical-Systems (CPS) There is a high degree of coupling, cohesiveness, and interactions among vehicle’s CPS components (eg, sensors, devices, systems, systems-of-systems) across sensing, communication, and control layers Cyber-attacks in the sensing or communication layers can compromise the security of the control layer This paper provides a detailed review of potential cyber threats related to the sensing layer Notably, the focus is mainly towards two categories of sensors: vehicle dynamics sensors (eg, Tire Pressure Monitoring Systems (TPMS), magnetic encoders, and inertial sensors) and environment sensors (eg, Light Detection and Ranging (LiDAR), ultrasonic, camera, Radio Detection and Ranging (Radar) systems, and Global Positioning System (GPS) units) The paper also offers perspectives through existing countermeasures from literature and stresses the need for data-driven cybersecurity solutions

Wireless Communication, Sensing, and REM: A Security Perspective

Abstract: The diverse requirements of next-generation communication systems necessitate awareness, flexibility, and intelligence as essential building blocks of future wireless networks. The awareness can be obtained from the radio signals in the environment using wireless sensing and radio environment mapping (REM) methods. This is, however, accompanied by threats such as eavesdropping, manipulation, and disruption posed by malicious attackers. To this end, this work analyzes the wireless sensing and radio environment awareness mechanisms, highlighting their vulnerabilities and provides solutions for mitigating them. As an example, the different threats to REM and its consequences in a vehicular communication scenario are described. Furthermore, the use of REM for securing communications is discussed and future directions regarding sensing/REM security are highlighted.

Neighbor Discovery and Selection in Millimeter Wave D2D Networks Using Stochastic MAB

Abstract: The propagation characteristics of millimeter-wave (mmWaves), encourages its use in the device to device (D2D) communications for fifth-generation (5G) and future beyond 5G (B5G) networks. However, due to the use of beamforming training (BT), there is a tradeoff between exploring neighbor devices for best device selection and the required overhead. In this letter, using a tool of machine learning, joint neighbor discovery and selection (NDS) in mmWave D2D networks is formulated as a stochastic budget-constraint multi-armed bandit (MAB) problem. Hence, a modified Thomson sampling (TS) and variants of upper confidence bound (UCB) based algorithms are proposed to address the topic while considering the residual energies of the surrounding devices. Simulation analysis demonstrates the effectiveness of the proposed techniques over the conventional approaches concerning average throughput, energy efficiency, and network lifetime.