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5 pages, 160 KiB

Open AccessEditorial

In-Vehicle Networking/Autonomous Vehicle Security for Internet of Things/Vehicles

by Taeshik Shon

Electronics 2021, 10(6), 637; https://doi.org/10.3390/electronics10060637 - 10 Mar 2021

Cited by 13 | Viewed by 2838

Abstract

In recent years, vehicles have become one of the most common examples in the area of ICT convergence applications and services [...] Full article

(This article belongs to the Special Issue In-Vehicle Networking/Autonomous Vehicle Security for Internet of Things/Vehicles)

Research

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15 pages, 1352 KiB

Open AccessArticle

Packet Preprocessing in CNN-Based Network Intrusion Detection System

by Wooyeon Jo, Sungjin Kim, Changhoon Lee and Taeshik Shon

Electronics 2020, 9(7), 1151; https://doi.org/10.3390/electronics9071151 - 16 Jul 2020

Cited by 45 | Viewed by 5746

Abstract

The proliferation of various connected platforms, including Internet of things, industrial control systems (ICSs), connected cars, and in-vehicle networks, has resulted in the simultaneous use of multiple protocols and devices. Chaotic situations caused by the usage of different protocols and various types of [...] Read more.

The proliferation of various connected platforms, including Internet of things, industrial control systems (ICSs), connected cars, and in-vehicle networks, has resulted in the simultaneous use of multiple protocols and devices. Chaotic situations caused by the usage of different protocols and various types of devices, such as heterogeneous networks, implemented differently by vendors renders the adoption of a flexible security solution difficult, such as recent deep learning-based intrusion detection system (IDS) studies. These studies optimized the deep learning model for their environment to improve performance, but the basic principle of the deep learning model used was not changed, so this can be called a next-generation IDS with a model that has little or no requirements. Some studies proposed IDS based on unsupervised learning technology that does not require labeled data. However, not using available assets, such as network packet data, is a waste of resources. If the security solution considers the role and importance of the devices constituting the network and the security area of the protocol standard by experts, the assets can be well used, but it will no longer be flexible. Most deep learning model-based IDS studies used recurrent neural network (RNN), which is a supervised learning model, because the characteristics of the RNN model, especially when the long-short term memory (LSTM) is incorporated, are better configured to reflect the flow of the packet data stream over time, and thus perform better than other supervised learning models such as convolutional neural network (CNN). However, if the input data induce the CNN’s kernel to sufficiently reflect the network characteristics through proper preprocessing, it could perform better than other deep learning models in the network IDS. Hence, we propose the first preprocessing method, called “direct”, for network IDS that can use the characteristics of the kernel by using the minimum protocol information, field size, and offset. In addition to direct, we propose two more preprocessing techniques called “weighted” and “compressed”. Each requires additional network information; therefore, direct conversion was compared with related studies. Including direct, the proposed preprocessing methods are based on field-to-pixel philosophy, which can reflect the advantages of CNN by extracting the convolutional features of each pixel. Direct is the most intuitive method of applying field-to-pixel conversion to reflect an image’s convolutional characteristics in the CNN. Weighted and compressed are conversion methods used to evaluate the direct method. Consequently, the IDS constructed using a CNN with the proposed direct preprocessing method demonstrated meaningful performance in the NSL-KDD dataset. Full article

(This article belongs to the Special Issue In-Vehicle Networking/Autonomous Vehicle Security for Internet of Things/Vehicles)

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22 pages, 580 KiB

Open AccessFeature PaperArticle

An Anonymous Device to Device Authentication Protocol Using ECC and Self Certified Public Keys Usable in Internet of Things Based Autonomous Devices

by Bander A. Alzahrani, Shehzad Ashraf Chaudhry, Ahmed Barnawi, Abdullah Al-Barakati and Taeshik Shon

Electronics 2020, 9(3), 520; https://doi.org/10.3390/electronics9030520 - 21 Mar 2020

Cited by 21 | Viewed by 4765

Abstract

Two party authentication schemes can be good candidates for deployment in Internet of Things (IoT)-based systems, especially in systems involving fast moving vehicles. Internet of Vehicles (IoV) requires fast and secure device-to-device communication without interference of any third party during communication, and this [...] Read more.

Two party authentication schemes can be good candidates for deployment in Internet of Things (IoT)-based systems, especially in systems involving fast moving vehicles. Internet of Vehicles (IoV) requires fast and secure device-to-device communication without interference of any third party during communication, and this task can be carried out after registration of vehicles with a trusted certificate issuing party. Recently, several authentication protocols were proposed to enable key agreement in two party settings. In this study, we analyze two recent protocols and show that both protocols are insecure against key compromise impersonation attack (KCIA) as well as both lack of user anonymity. Therefore, this paper proposes an improved protocol that does not only resist KCIA and related attacks, but also offers comparable computation and communication. The security of proposed protocol is tested under formal model as well as using well known Burrows–Abadi–Needham (BAN) logic along with a discussion on security features. While resisting the KCIA and related attacks, proposed protocol also provides comparable trade-of between security features and efficiency and completes a round of key agreement in just

13.42

ms, which makes it a promising candidate to be deployed in IoT environments. Full article

(This article belongs to the Special Issue In-Vehicle Networking/Autonomous Vehicle Security for Internet of Things/Vehicles)

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14 pages, 562 KiB

Open AccessFeature PaperArticle

Sensitive, Linear, Robust Current-To-Time Converter Circuit for Vehicle Automation Application

by Nandakishor Yadav, Youngbae Kim, Mahmoud Alashi and Kyuwon Ken Choi

Electronics 2020, 9(3), 490; https://doi.org/10.3390/electronics9030490 - 16 Mar 2020

Cited by 3 | Viewed by 2791

Abstract

Voltage-to-time and current-to-time converters have been used in many recent works as a voltage-to-digital converter for artificial intelligence applications. In general, most of the previous designs use the current-starved technique or a capacitor-based delay unit, which is non-linear, expensive, and requires a large [...] Read more.

Voltage-to-time and current-to-time converters have been used in many recent works as a voltage-to-digital converter for artificial intelligence applications. In general, most of the previous designs use the current-starved technique or a capacitor-based delay unit, which is non-linear, expensive, and requires a large area. In this paper, we propose a highly linear current-to-digital converter. An optimization method is also proposed to generate the optimal converter design containing the smallest number of PMOS and sensitive circuits such as a differential amplifier. This enabled our design to be more stable and robust toward negative bias temperature instability (NBTI) and process variation. The proposed converter circuit implements the point-wise conversion from current-to-time, and it can be used directly for a variety of applications, such as analog-to-digital converters (ADC), used in built-in computational random access (C-RAM) memory. The conversion gain of the proposed circuit is 3.86 ms/A, which is 52 times greater than the conversion gains of state-of-the-art designs. Further, various time-to-digital converter (TDC) circuits are reviewed for the proposed current-to-time converter, and we recommend one circuit for a complete ADC design. Full article

(This article belongs to the Special Issue In-Vehicle Networking/Autonomous Vehicle Security for Internet of Things/Vehicles)

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14 pages, 4996 KiB

Open AccessFeature PaperArticle

Low-Power RTL Code Generation for Advanced CNN Algorithms toward Object Detection in Autonomous Vehicles

by Youngbae Kim, Heekyung Kim, Nandakishor Yadav, Shuai Li and Kyuwon Ken Choi

Electronics 2020, 9(3), 478; https://doi.org/10.3390/electronics9030478 - 14 Mar 2020

Cited by 15 | Viewed by 7421

Abstract

In the implementation process of a convolution neural network (CNN)-based object detection system, the primary issues are power dissipation and limited throughput. Even though we utilize ultra-low power dissipation devices, the dynamic power dissipation issue will be difficult to resolve. During the operation [...] Read more.

In the implementation process of a convolution neural network (CNN)-based object detection system, the primary issues are power dissipation and limited throughput. Even though we utilize ultra-low power dissipation devices, the dynamic power dissipation issue will be difficult to resolve. During the operation of the CNN algorithm, there are several factors such as the heating problem generated from the massive computational complexity, the bottleneck generated in data transformation and by the limited bandwidth, and the power dissipation generated from redundant data access. This article proposes the low-power techniques, applies them to the CNN accelerator on the FPGA and ASIC design flow, and evaluates them on the Xilinx ZCU-102 FPGA SoC hardware platform and 45 nm technology for ASIC, respectively. Our proposed low-power techniques are applied at the register-transfer-level (RT-level), targeting FPGA and ASIC. In this article, we achieve up to a 53.21% power reduction in the ASIC implementation and saved 32.72% of the dynamic power dissipation in the FPGA implementation. This shows that our RTL low-power schemes have a powerful possibility of dynamic power reduction when applied to the FPGA design flow and ASIC design flow for the implementation of the CNN-based object detection system. Full article

(This article belongs to the Special Issue In-Vehicle Networking/Autonomous Vehicle Security for Internet of Things/Vehicles)

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15 pages, 1038 KiB

Open AccessArticle

Design and Development of BTI Model and 3D InGaAs HEMT-Based SRAM for Reliable and Secure Internet of Things Application

by Nandakishor Yadav, Mahmoud Alashi and Kyuwon Ken Choi

Electronics 2020, 9(3), 469; https://doi.org/10.3390/electronics9030469 - 11 Mar 2020

Cited by 2 | Viewed by 2971

Abstract

It is broadly accepted that the silicon-based CMOS has touched its scaling limits and alternative substrate materials are needed for future technology nodes. An Indium-Gallium-Arsenide (

I n G a A s

)-based device is well situated for further technology nodes. This material [...] Read more.

It is broadly accepted that the silicon-based CMOS has touched its scaling limits and alternative substrate materials are needed for future technology nodes. An Indium-Gallium-Arsenide (

I n G a A s

)-based device is well situated for further technology nodes. This material also has better mobility of the electrons and holes for the high performance and real-time system design. The improved mobility helps to increase the operating frequency of the device which is useful for Internet of Things (IoT) applications. However,

I n G a A s

-based High Electron Mobility Transistors (HEMT) limits the reliability of the device due to the presence of dangling bonds at the channel–gate insulator interfaces. Weak dangling-bonds get broken under electric stress, and positive hydrogen atoms are trapped into the oxide. This charge trapping depends on the material parameters and device geometry. In this paper, the existing Bias-Temperature-Instability (BTI) model is modified based on the material parameters and device geometry. Charge trapping and annealing constants are the most critical BTI model parameters that are modeled and evaluated based on different HEMT material parameters. The proposed model was compared to experimental and TCAD simulation results. The proposed model has been used for lifetime prediction of the InGaAs HEMT-based Static Random-Access Memory (SRAM) cell because it is used to store and process the information in the IoT applications. Full article

(This article belongs to the Special Issue In-Vehicle Networking/Autonomous Vehicle Security for Internet of Things/Vehicles)

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14 pages, 3243 KiB

Open AccessArticle

Optimized Node Clustering in VANETs by Using Meta-Heuristic Algorithms

by Waleed Ahsan, Muhammad Fahad Khan, Farhan Aadil, Muazzam Maqsood, Staish Ashraf, Yunyoung Nam and Seungmin Rho

Electronics 2020, 9(3), 394; https://doi.org/10.3390/electronics9030394 - 27 Feb 2020

Cited by 58 | Viewed by 5251

Abstract

In a vehicular ad-hoc network (VANET), the vehicles are the nodes, and these nodes communicate with each other. On the road, vehicles are continuously in motion, and it causes a dynamic change in the network topology. It is more challenging when there is [...] Read more.

In a vehicular ad-hoc network (VANET), the vehicles are the nodes, and these nodes communicate with each other. On the road, vehicles are continuously in motion, and it causes a dynamic change in the network topology. It is more challenging when there is a higher node density. These conditions create many difficulties for network scalability and optimal route-finding in VANETs. Clustering protocols are being used frequently to solve such type of problems. In this paper, we proposed the grasshoppers’ optimization-based node clustering algorithm for VANETs (GOA) for optimal cluster head selection. The proposed algorithm reduced network overhead in unpredictable node density scenarios. To do so, different experiments were performed for comparative analysis of GOA with other state-of-the-art techniques like dragonfly algorithm, grey wolf optimizer (GWO), and ant colony optimization (ACO). Plentiful parameters, such as the number of clusters, network area, node density, and transmission range, were used in various experiments. The outcome of these results indicated that GOA outperformed existing methodologies. Lastly, the application of GOA in the flying ad-hoc network (FANET) domain was also proposed for next-generation networks. Full article

(This article belongs to the Special Issue In-Vehicle Networking/Autonomous Vehicle Security for Internet of Things/Vehicles)

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8 pages, 1054 KiB

Open AccessFeature PaperArticle

Design of a Voltage to Time Converter with High Conversion Gain for Reliable and Secure Autonomous Vehicles

by Nandakishor Yadav, Youngbae Kim, Mahmoud Alashi and Kyuwon Ken Choi

Electronics 2020, 9(3), 384; https://doi.org/10.3390/electronics9030384 - 26 Feb 2020

Cited by 6 | Viewed by 4476

Abstract

Automation of vehicles requires a secure, reliable, and real-time on-chip system. These systems also require very high-speed and compact on-chip analog to digital converters (ADC). The conventional ADC cannot fulfill this requirement. In this paper, we proposed a Darlington pair- and source biasing-based [...] Read more.

Automation of vehicles requires a secure, reliable, and real-time on-chip system. These systems also require very high-speed and compact on-chip analog to digital converters (ADC). The conventional ADC cannot fulfill this requirement. In this paper, we proposed a Darlington pair- and source biasing-based high speed, secure, and reliable voltage to time converter (VTC). It is a compact, high-speed design and gives high conversion gain. The source biasing also helps to increase the input voltage range. The conversion gain of the proposed circuit is 101.43ns/v, which is 52 times greater than the gain achieved by state-of-the-art design. It also shows less effect of process variation and bias temperature instability. Full article

(This article belongs to the Special Issue In-Vehicle Networking/Autonomous Vehicle Security for Internet of Things/Vehicles)

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17 pages, 1809 KiB

Open AccessFeature PaperArticle

A Reactive and On-Chip Sensor Circuit for NBTI and PBTI Resilient SRAM Design

by Nandakishor Yadav, Youngbae Kim, Mahmoud Alashi and Kyuwon Ken Choi

Electronics 2020, 9(2), 326; https://doi.org/10.3390/electronics9020326 - 13 Feb 2020

Cited by 4 | Viewed by 3359

Abstract

Process Variation (PV), Bias Temperature Instability (BTI) and Time-Dependent Dielectric Breakdown (TDDB) are the critical factors that affect the reliability of semiconductor chip design. They cause the system to be unstable and increase the soft error rate. In this paper, a compact on-chip [...] Read more.

Process Variation (PV), Bias Temperature Instability (BTI) and Time-Dependent Dielectric Breakdown (TDDB) are the critical factors that affect the reliability of semiconductor chip design. They cause the system to be unstable and increase the soft error rate. In this paper, a compact on-chip degradation technique using runtime leakage current monitoring has been proposed. The proposed sensor-based adaptive technique compensates for the variation due to PV and aging using the body-bias-voltage-generator circuit. Simulation experiments for three and ten-year stress have been performed. Simulation results proved the superiority of the proposed sensor which provides 33% (up to 0.75 V) more output voltage and 98% sensitivity at 1 V supply voltage compared to the state-of-the-art sensor. The proposed technique mitigates up to 80% PV and BTI effects in SRAM compared to the state-of-the-art techniques. Full article

(This article belongs to the Special Issue In-Vehicle Networking/Autonomous Vehicle Security for Internet of Things/Vehicles)

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