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11 pages, 678 KiB

Open AccessArticle

PUF-Based Post-Quantum CAN-FD Framework for Vehicular Security

by Tyler Cultice and Himanshu Thapliyal

Information 2022, 13(8), 382; https://doi.org/10.3390/info13080382 - 9 Aug 2022

Cited by 3 | Viewed by 2499

The Controller Area Network (CAN) is a bus protocol widely used in Electronic control Units (ECUs) to communicate between various subsystems in vehicles. Insecure CAN networks can allow attackers to control information between vital vehicular subsystems. As vehicles can have lifespans of multiple [...] Read more.

The Controller Area Network (CAN) is a bus protocol widely used in Electronic control Units (ECUs) to communicate between various subsystems in vehicles. Insecure CAN networks can allow attackers to control information between vital vehicular subsystems. As vehicles can have lifespans of multiple decades, post-quantum cryptosystems are essential for protecting the vehicle communication systems from quantum attacks. However, standard CAN’s efficiency and payload sizes are too small for post-quantum cryptography. The Controller Area Network Flexible Data-Rate (CAN-FD) is an updated protocol for CAN that increases transmission speeds and maximum payload size. With CAN-FD, higher security standards, such as post-quantum, can be utilized without severely impacting performance. In this paper, we propose PUF-Based Post-Quantum Cryptographic CAN-FD Framework, or PUF-PQC-CANFD. Our framework provides post-quantum security to the CAN network while transmitting and storing less information than other existing pre-quantum and post-quantum CAN frameworks. Our proposal protects against most cryptographic-based attacks while transmitting (at up to 100 ECUs) 25–94% less messages than existing pre-quantum frameworks and 99% less messages than existing post-quantum frameworks. PUF-PQC-CANFD is optimized for smaller post-quantum key sizes, storage requirements, and transmitted information to minimize the impact on resource-restricted ECUs. Full article

(This article belongs to the Special Issue Recent Advances in IoT and Cyber/Physical Security)

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20 pages, 10635 KiB

Open AccessArticle

Deep-Sleep for Stateful IoT Edge Devices

by Augusto Ciuffoletti

Information 2022, 13(3), 156; https://doi.org/10.3390/info13030156 - 17 Mar 2022

Viewed by 2364

Abstract

In an IoT (Internet of Things) system, the autonomy of battery-operated edge devices is of paramount importance. When such devices operate intermittently, reducing power consumption during standby improves such a characteristic. The deep-sleep operation mode obtains such a result: it keeps on power [...] Read more.

In an IoT (Internet of Things) system, the autonomy of battery-operated edge devices is of paramount importance. When such devices operate intermittently, reducing power consumption during standby improves such a characteristic. The deep-sleep operation mode obtains such a result: it keeps on power only the hardware needed to wake up the unit after a timeout or an external trigger. For this reason, deep sleep exhibits the issue of losing the working memory, which prevents its use with applications depending on long-lasting or stateful computations. A way to circumvent such an issue consists of saving a snapshot of the working memory on a remote repository. However, such a solution is not always convenient since it exhibits an energy footprint due to checkpoint transmission. This article analyzes the applicability of such a solution. Firstly, by comparing its energy footprint against keeping the working memory on power. The analysis follows a formal, technology-agnostic methodology based on a mathematical model for energy consumption. It yields a discriminant inequality identifying the use cases where remote checkpointing is of interest. Once justified the approach, the article proceeds by defining an architecture and a secure protocol for data transport and storage. Finally, the description of a prototype implementation provides concrete insights. Full article

(This article belongs to the Special Issue Recent Advances in IoT and Cyber/Physical Security)

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16 pages, 4080 KiB

Open AccessArticle

Asset Management Method of Industrial IoT Systems for Cyber-Security Countermeasures

by Noritaka Matsumoto, Junya Fujita, Hiromichi Endoh, Tsutomu Yamada, Kenji Sawada and Osamu Kaneko

Information 2021, 12(11), 460; https://doi.org/10.3390/info12110460 - 8 Nov 2021

Cited by 4 | Viewed by 3350

Abstract

Cyber-security countermeasures are important for IIoT (industrial Internet of things) systems in which IT (information technology) and OT (operational technology) are integrated. The appropriate asset management is the key to creating strong security systems to protect from various cyber threats. However, the timely [...] Read more.

Cyber-security countermeasures are important for IIoT (industrial Internet of things) systems in which IT (information technology) and OT (operational technology) are integrated. The appropriate asset management is the key to creating strong security systems to protect from various cyber threats. However, the timely and coherent asset management methods used for conventional IT systems are difficult to be implemented for IIoT systems. This is because these systems are composed of various network protocols, various devices, and open technologies. Besides, it is necessary to guarantee reliable and real-time control and save CPU and memory usage for legacy OT devices. In this study, therefore, (1) we model various asset configurations for IIoT systems and design a data structure based on SCAP (Security Content Automation Protocol). (2) We design the functions to automatically acquire the detailed information from edge devices by “asset configuration management agent”, which ensures a low processing load. (3) We implement the proposed asset management system to real edge devices and evaluate the functions. Our contribution is to automate the asset management method that is valid for the cyber security countermeasures in the IIoT systems. Full article

(This article belongs to the Special Issue Recent Advances in IoT and Cyber/Physical Security)

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16 pages, 1447 KiB

Open AccessArticle

The Impact of Organizational Practices on the Information Security Management Performance

by Latifa Alzahrani and Kavita Panwar Seth

Information 2021, 12(10), 398; https://doi.org/10.3390/info12100398 - 28 Sep 2021

Cited by 6 | Viewed by 4228

Abstract

Information explosion and pressures are leading organizations to invest heavily in information security to ensure that information technology decisions align with business goals and manage risks. Limited studies have been done using small- and-medium-sized enterprises (SMEs) in the manufacturing sector. Furthermore, a small [...] Read more.

Information explosion and pressures are leading organizations to invest heavily in information security to ensure that information technology decisions align with business goals and manage risks. Limited studies have been done using small- and-medium-sized enterprises (SMEs) in the manufacturing sector. Furthermore, a small number of parameters have been used in the previous studies. This research aims to examine and analyze the effect of security organizational practices on information security management performance with many parameters. A model has been developed together with hypotheses to evaluate the impact of organizational practices on information security management performance. The data is collected from 171 UK employees at manufacturing SMEs that had already implemented security policies. The structure equation model is employed via the SPSS Amos 22 tool for the evaluation of results. Our results state that security training, knowledge sharing, security education, and security visibility significantly impact information security performance. In addition, this study highlights a significant impact of both security training and knowledge sharing on trust in the organization. Business leaders and decision-makers can reference the proposed model and the corresponding study results to develop favourable tactics to achieve their goals regarding information security management. Full article

(This article belongs to the Special Issue Recent Advances in IoT and Cyber/Physical Security)

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16 pages, 693 KiB

Open AccessArticle

MNCF: Prediction Method for Reliable Blockchain Services under a BaaS Environment

by Jianlong Xu, Zicong Zhuang, Zhiyu Xia and Yuhui Li

Information 2021, 12(6), 242; https://doi.org/10.3390/info12060242 - 10 Jun 2021

Cited by 2 | Viewed by 2438

Abstract

Blockchain is an innovative distributed ledger technology that is widely used to build next-generation applications without the support of a trusted third party. With the ceaseless evolution of the service-oriented computing (SOC) paradigm, Blockchain-as-a-Service (BaaS) has emerged, which facilitates development of blockchain-based applications. [...] Read more.

Blockchain is an innovative distributed ledger technology that is widely used to build next-generation applications without the support of a trusted third party. With the ceaseless evolution of the service-oriented computing (SOC) paradigm, Blockchain-as-a-Service (BaaS) has emerged, which facilitates development of blockchain-based applications. To develop a high-quality blockchain-based system, users must select highly reliable blockchain services (peers) that offer excellent quality-of-service (QoS). Since the vast number of blockchain services leading to sparse QoS data, selecting the optimal personalized services is challenging. Hence, we improve neural collaborative filtering and propose a QoS-based blockchain service reliability prediction algorithm under BaaS, named modified neural collaborative filtering (MNCF). In this model, we combine a neural network with matrix factorization to perform collaborative filtering for the latent feature vectors of users. Furthermore, multi-task learning for sharing different parameters is introduced to improve the performance of the model. Experiments based on a large-scale real-world dataset validate its superior performance compared to baselines. Full article

(This article belongs to the Special Issue Recent Advances in IoT and Cyber/Physical Security)

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29 pages, 77707 KiB

Open AccessEditor’s ChoiceArticle

Identification of Driving Safety Profiles in Vehicle to Vehicle Communication System Based on Vehicle OBD Information

by Hussein Ali Ameen, Abd Kadir Mahamad, Sharifah Saon, Rami Qays Malik, Zahraa Hashim Kareem, Mohd Anuaruddin Bin Ahmadon and Shingo Yamaguchi

Information 2021, 12(5), 194; https://doi.org/10.3390/info12050194 - 29 Apr 2021

Cited by 4 | Viewed by 4136

Abstract

Driver behavior is a determining factor in more than 90% of road accidents. Previous research regarding the relationship between speeding behavior and crashes suggests that drivers who engage in frequent and extreme speeding behavior are overinvolved in crashes. Consequently, there is a significant [...] Read more.

Driver behavior is a determining factor in more than 90% of road accidents. Previous research regarding the relationship between speeding behavior and crashes suggests that drivers who engage in frequent and extreme speeding behavior are overinvolved in crashes. Consequently, there is a significant benefit in identifying drivers who engage in unsafe driving practices to enhance road safety. The proposed method uses continuously logged driving data to collect vehicle operation information, including vehicle speed, engine revolutions per minute (RPM), throttle position, and calculated engine load via the on-board diagnostics (OBD) interface. Then the proposed method makes use of severity stratification of acceleration to create a driving behavior classification model to determine whether the current driving behavior belongs to safe driving or not. The safe driving behavior is characterized by an acceleration value that ranges from about ±2 m/s². The risk of collision starts from ±4 m/s², which represents in this study the aggressive drivers. By measuring the in-vehicle accelerations, it is possible to categorize the driving behavior into four main classes based on real-time experiments: safe drivers, normal, aggressive, and dangerous drivers. Subsequently, the driver’s characteristics derived from the driver model are embedded into the advanced driver assistance systems. When the vehicle is in a risk situation, the system based on nRF24L01 + power amplifier/low noise amplifier PA/LNA, global positioning system GPS, and OBD-II passes a signal to the driver using a dedicated liquid-crystal display LCD and light signal. Experimental results show the correctness of the proposed driving behavior analysis method can achieve an average of 90% accuracy rate in various driving scenarios. Full article

(This article belongs to the Special Issue Recent Advances in IoT and Cyber/Physical Security)

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18 pages, 1055 KiB

Open AccessArticle

Multi-Task Learning-Based Task Scheduling Switcher for a Resource-Constrained IoT System

by Mohd Hafizuddin Bin Kamilin, Mohd Anuaruddin Bin Ahmadon and Shingo Yamaguchi

Information 2021, 12(4), 150; https://doi.org/10.3390/info12040150 - 1 Apr 2021

Cited by 2 | Viewed by 2664

Abstract

In this journal, we proposed a novel method of using multi-task learning to switch the scheduling algorithm. With multi-task learning to change the scheduling algorithm inside the scheduling framework, the scheduling framework can create a scheduler with the best task execution optimization under [...] Read more.

In this journal, we proposed a novel method of using multi-task learning to switch the scheduling algorithm. With multi-task learning to change the scheduling algorithm inside the scheduling framework, the scheduling framework can create a scheduler with the best task execution optimization under the computation deadline. With the changing number of tasks, the number of types of resources taken, and computation deadline, it is hard for a single scheduling algorithm to achieve the best scheduler optimization while avoiding the worst-case time complexity in a resource-constrained Internet of Things (IoT) system due to the trade-off in computation time and optimization in each scheduling algorithm. Furthermore, different hardware specifications affect the scheduler computation time differently, making it hard to rely on Big-O complexity as a reference. With multi-task learning to profile the scheduling algorithm behavior on the hardware used to compute the scheduler, we can identify the best scheduling algorithm. Our benchmark result shows that it can achieve an average of 93.68% of accuracy in meeting the computation deadline, along with 23.41% of average optimization. Based on the results, our method can improve the scheduling of the resource-constrained IoT system. Full article

(This article belongs to the Special Issue Recent Advances in IoT and Cyber/Physical Security)

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19 pages, 2030 KiB

Open AccessArticle

Physical Device Compatibility Support for Implementation of IoT Services with Design Once, Provide Anywhere Concept

by Mohd Anuaruddin Bin Ahmadon, Shingo Yamaguchi, Abd Kadir Mahamad and Sharifah Saon

Information 2021, 12(1), 30; https://doi.org/10.3390/info12010030 - 12 Jan 2021

Cited by 4 | Viewed by 2696

Abstract

This paper proposes a method to ensure compatibility between physical devices for implementing a service design. The method supports the relaxation of strict implementation. It allows a set of compatible devices to be implemented instead of specifying specific devices in the service design. [...] Read more.

This paper proposes a method to ensure compatibility between physical devices for implementing a service design. The method supports the relaxation of strict implementation. It allows a set of compatible devices to be implemented instead of specifying specific devices in the service design. This paper’s main contribution is the formalization of device constraints using the device’s attributes and a method to check the compatibility between devices. The method’s characteristic is that a service designer can decide the level of strictness and abstractness of the design by adjusting the compatibility rate. We show the feasibility of the proposed method to achieve the goal of “Design Once, Provide Anywhere” with an application example. We also evaluated the quality of service of the implemented IoT service in a different environment using a platform called Elgar. Full article

(This article belongs to the Special Issue Recent Advances in IoT and Cyber/Physical Security)

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23 pages, 6279 KiB

Open AccessArticle

Dimensionality Reduction for Human Activity Recognition Using Google Colab

by Sujan Ray, Khaldoon Alshouiliy and Dharma P. Agrawal

Information 2021, 12(1), 6; https://doi.org/10.3390/info12010006 - 23 Dec 2020

Cited by 10 | Viewed by 4183

Abstract

Human activity recognition (HAR) is a classification task that involves predicting the movement of a person based on sensor data. As we can see, there has been a huge growth and development of smartphones over the last 10–15 years—they could be used as [...] Read more.

Human activity recognition (HAR) is a classification task that involves predicting the movement of a person based on sensor data. As we can see, there has been a huge growth and development of smartphones over the last 10–15 years—they could be used as a medium of mobile sensing to recognize human activity. Nowadays, deep learning methods are in a great demand and we could use those methods to recognize human activity. A great way is to build a convolutional neural network (CNN). HAR using Smartphone dataset has been widely used by researchers to develop machine learning models to recognize human activity. The dataset has two parts: training and testing. In this paper, we propose a hybrid approach to analyze and recognize human activity on the same dataset using deep learning method on cloud-based platform. We have applied principal component analysis on the dataset to get the most important features. Next, we have executed the experiment for all the features as well as the top 48, 92, 138, and 164 features. We have run all the experiments on Google Colab. In the experiment, for the evaluation of our proposed methodology, datasets are split into two different ratios such as 70–10–20% and 80–10–10% for training, validation, and testing, respectively. We have set the performance of CNN (70% training–10% validation–20% testing) with 48 features as a benchmark for our work. In this work, we have achieved maximum accuracy of 98.70% with CNN. On the other hand, we have obtained 96.36% accuracy with the top 92 features of the dataset. We can see from the experimental results that if we could select the features properly then not only could the accuracy be improved but also the training and testing time of the model. Full article

(This article belongs to the Special Issue Recent Advances in IoT and Cyber/Physical Security)

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