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Security, Cybercrime, and Digital Forensics for the IoT

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 17671

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Prof. Dr. Jong Hyuk Park

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Department of Computer Science and Engineering, Seoul National University of Science and Technology, 232 Gongneung-ro, Nowon-gu, Seoul 01811, Republic of Korea
Interests: IoT security; digital forensics; blockchain; privacy protection; information security
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Special Issue Information

Dear Colleagues,

The Internet of Things (IoT) connects all physical and virtual environments around us to provide personalized services to enhance our lifestyle. Embedded sensors that support smart applications improve our daily experiences in various fields, including smart agriculture, autonomous vehicles, connected healthcare, cyber–physical systems, and the Industrial Internet of Things 4.0. The advancement of the Internet and communication technologies in addition to their pervasiveness present new vectors which, once exploited, enable threat actors to materialize their objectives. The growing number of sophisticated attack patterns, tactics, and techniques, combined with the ever-growing volume of data, require new approaches in developing defensive mechanisms against newly introduced cybercrimes. One approach that tackles the increasing number of cybercrimes is digital forensics, where incident response investigators are provided with a wealth of information to address data security and privacy concerns.

This Special Issue invites contributions and recent advancements in investigating and addressing security and privacy challenges for smart sensors, emphasizing cybercrimes and digital forensics.

Prof. Dr. Jong Hyuk Park
Guest Editor

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Keywords

Cybercrime and digital forensics for the IoT
Incident response methods for the IoT
Data analysis of the IoT for forensic investigation
System, network, and mobile forensics for the IoT
AI-accelerated intrusion detection for the IoT
Reverse-engineering and forensic activities for the IoT
Cybercrime risk management for the IoT
Cryptography protocols and algorithms for the IoT
Blockchain and digital identity management in the IoT
Intrusion and malware detection for the IoT
Security, privacy, and trust in the IoT
Other challenges and soultions for IoT security and forensics

Published Papers (10 papers)

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Research

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21 pages, 2886 KiB

Open AccessArticle

A Comprehensive Approach to User Delegation and Anonymity within Decentralized Identifiers for IoT

by Taehoon Kim, Daehee Seo, Su-Hyun Kim and Im-Yeong Lee

Sensors 2024, 24(7), 2215; https://doi.org/10.3390/s24072215 - 29 Mar 2024

Viewed by 409

Abstract

Decentralized Identifiers have recently expanded into Internet of Things devices and are crucial in securing users’ digital identities and data. However, Decentralized Identifiers face challenges in scenarios necessitating authority delegation and anonymity, such as when dealing with legal guardianship for minors, device loss or damage, and specific medical contexts involving patient information. This paper aims to strengthen data sovereignty within the Decentralized Identifier system by implementing a secure authority delegation and anonymity scheme. It suggests optimizing verifiable presentations by utilizing a sequential aggregate signature, a Non-Interactive Zero-Knowledge Proof, and a Merkle tree to prevent against linkage and Sybil attacks while facilitating delegation. This strategy mitigates security risks related to delegation and anonymity, efficiently reduces the computational and verification efforts for signatures, and reduces the size of verifiable presentations by about 1.2 to 2 times. Full article

(This article belongs to the Special Issue Security, Cybercrime, and Digital Forensics for the IoT)

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15 pages, 5659 KiB

Open AccessArticle

Bluetooth Device Identification Using RF Fingerprinting and Jensen-Shannon Divergence

by Rene Francisco Santana-Cruz, Martin Moreno-Guzman, César Enrique Rojas-López, Ricardo Vázquez-Morán and Rubén Vázquez-Medina

Sensors 2024, 24(5), 1482; https://doi.org/10.3390/s24051482 - 24 Feb 2024

Viewed by 466

Abstract

The proliferation of radio frequency (RF) devices in contemporary society, especially in the fields of smart homes, Internet of Things (IoT) gadgets, and smartphones, underscores the urgent need for robust identification methods to strengthen cybersecurity. This paper delves into the realms of RF fingerprint (RFF) based on applying the Jensen-Shannon divergence (JSD) to the statistical distribution of noise in RF signals to identify Bluetooth devices. Thus, through a detailed case study, Bluetooth RF noise taken at 5 Gsps from different devices is explored. A noise model is considered to extract a unique, universal, permanent, permanent, collectable, and robust statistical RFF that identifies each Bluetooth device. Then, the different JSD noise signals provided by Bluetooth devices are contrasted with the statistical RFF of all devices and a membership resolution is declared. The study shows that this way of identifying Bluetooth devices based on RFF allows one to discern between devices of the same make and model, achieving 99.5% identification effectiveness. By leveraging statistical RFFs extracted from noise in RF signals emitted by devices, this research not only contributes to the advancement of the field of implicit device authentication systems based on wireless communication but also provides valuable insights into the practical implementation of RF identification techniques, which could be useful in forensic processes. Full article

(This article belongs to the Special Issue Security, Cybercrime, and Digital Forensics for the IoT)

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29 pages, 32760 KiB

Open AccessArticle

Digital Forensic Research for Analyzing Drone Pilot: Focusing on DJI Remote Controller

by Sungwon Lee, Hyeongmin Seo and Dohyun Kim

Sensors 2023, 23(21), 8934; https://doi.org/10.3390/s23218934 - 02 Nov 2023

Viewed by 1560

Abstract

Drones, also known as unmanned aerial vehicles (UAVs) and sometimes referred to as ‘Mobile IoT’ or ‘Flying IoT’, are widely adopted worldwide, with their market share continuously increasing. While drones are generally harnessed for a wide range of positive applications, recent instances of drones being employed as lethal weapons in conflicts between countries like Russia, Ukraine, Israel, Palestine, and Hamas have demonstrated the potential consequences of their misuse. Such misuse poses a significant threat to cybersecurity and human lives, thereby highlighting the need for research to swiftly and accurately analyze drone-related crimes, identify the responsible pilot, and establish when and what illegal actions were carried out. In contrast to existing research, involving limited data collection and analysis of the drone, our study focused on collecting and rigorously analyzing data without restrictions from the remote controller used to operate the drone. This comprehensive approach allowed us to unveil essential details, including the pilot’s account information, the specific drone used, pairing timestamps, the pilot’s operational location, the drone’s flight path, and the content captured during flights. We developed methodologies and proposed artifacts to reveal these specifics, which were supported by real-world data. Significantly, this study is the pioneering digital forensic investigation of remote controller devices. We meticulously collected and analyzed all internal data, and we even employed reverse engineering to decrypt critical information files. These achievements hold substantial significance. The outcomes of this research are expected to serve as a digital forensic methodology for drone systems, thereby making valuable contributions to numerous investigations. Full article

(This article belongs to the Special Issue Security, Cybercrime, and Digital Forensics for the IoT)

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19 pages, 2967 KiB

Open AccessArticle

Digital Forensic Analysis of Vehicular Video Sensors: Dashcams as a Case

by Yousef-Awwad Daraghmi and Ibrahim Shawahna

Sensors 2023, 23(17), 7548; https://doi.org/10.3390/s23177548 - 31 Aug 2023

Viewed by 1176

Abstract

Dashcams are considered video sensors, and the number of dashcams installed in vehicles is increasing. Native dashcam video players can be used to view evidence during investigations, but these players are not accepted in court and cannot be used to extract metadata. Digital forensic tools, such as FTK, Autopsy and Encase, are specifically designed for functions and scripts and do not perform well in extracting metadata. Therefore, this paper proposes a dashcam forensics framework for extracting evidential text including time, date, speed, GPS coordinates and speed units using accurate optical character recognition methods. The framework also transcribes evidential speech related to lane departure and collision warning for enabling automatic analysis. The proposed framework associates the spatial and temporal evidential data with a map, enabling investigators to review the evidence along the vehicle’s trip. The framework was evaluated using real-life videos, and different optical character recognition (OCR) methods and speech-to-text conversion methods were tested. This paper identifies that Tesseract is the most accurate OCR method that can be used to extract text from dashcam videos. Also, the Google speech-to-text API is the most accurate, while Mozilla’s DeepSpeech is more acceptable because it works offline. The framework was compared with other digital forensic tools, such as Belkasoft, and the framework was found to be more effective as it allows automatic analysis of dashcam evidence and generates digital forensic reports associated with a map displaying the evidence along the trip. Full article

(This article belongs to the Special Issue Security, Cybercrime, and Digital Forensics for the IoT)

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18 pages, 5838 KiB

Open AccessArticle

Investigating the Effectiveness of Novel Support Vector Neural Network for Anomaly Detection in Digital Forensics Data

by Umar Islam, Hathal Salamah Alwageed, Malik Muhammad Umer Farooq, Inayat Khan, Fuad A. Awwad, Ijaz Ali and Mohamed R. Abonazel

Sensors 2023, 23(12), 5626; https://doi.org/10.3390/s23125626 - 15 Jun 2023

Cited by 1 | Viewed by 1205

Abstract

As criminal activity increasingly relies on digital devices, the field of digital forensics plays a vital role in identifying and investigating criminals. In this paper, we addressed the problem of anomaly detection in digital forensics data. Our objective was to propose an effective approach for identifying suspicious patterns and activities that could indicate criminal behavior. To achieve this, we introduce a novel method called the Novel Support Vector Neural Network (NSVNN). We evaluated the performance of the NSVNN by conducting experiments on a real-world dataset of digital forensics data. The dataset consisted of various features related to network activity, system logs, and file metadata. Through our experiments, we compared the NSVNN with several existing anomaly detection algorithms, including Support Vector Machines (SVM) and neural networks. We measured and analyzed the performance of each algorithm in terms of the accuracy, precision, recall, and F1-score. Furthermore, we provide insights into the specific features that contribute significantly to the detection of anomalies. Our results demonstrated that the NSVNN method outperformed the existing algorithms in terms of anomaly detection accuracy. We also highlight the interpretability of the NSVNN model by analyzing the feature importance and providing insights into the decision-making process. Overall, our research contributes to the field of digital forensics by proposing a novel approach, the NSVNN, for anomaly detection. We emphasize the importance of both performance evaluation and model interpretability in this context, providing practical insights for identifying criminal behavior in digital forensics investigations. Full article

(This article belongs to the Special Issue Security, Cybercrime, and Digital Forensics for the IoT)

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25 pages, 12891 KiB

Open AccessArticle

A Blockchain-Enabled Secure Digital Twin Framework for Early Botnet Detection in IIoT Environment

by Mikail Mohammed Salim, Alowonou Kowovi Comivi, Tojimurotov Nurbek, Heejae Park and Jong Hyuk Park

Sensors 2022, 22(16), 6133; https://doi.org/10.3390/s22166133 - 16 Aug 2022

Cited by 5 | Viewed by 2622

Abstract

Resource constraints in the Industrial Internet of Things (IIoT) result in brute-force attacks, transforming them into a botnet to launch Distributed Denial of Service Attacks. The delayed detection of botnet formation presents challenges in controlling the spread of malicious scripts in other devices and increases the probability of a high-volume cyberattack. In this paper, we propose a secure Blockchain-enabled Digital Framework for the early detection of Bot formation in a Smart Factory environment. A Digital Twin (DT) is designed for a group of devices on the edge layer to collect device data and inspect packet headers using Deep Learning for connections with external unique IP addresses with open connections. Data are synchronized between the DT and a Packet Auditor (PA) for detecting corrupt device data transmission. Smart Contracts authenticate the DT and PA, ensuring malicious nodes do not participate in data synchronization. Botnet spread is prevented using DT certificate revocation. A comparative analysis of the proposed framework with existing studies demonstrates that the synchronization of data between the DT and PA ensures data integrity for the Botnet detection model training. Data privacy is maintained by inspecting only Packet headers, thereby not requiring the decryption of encrypted data. Full article

(This article belongs to the Special Issue Security, Cybercrime, and Digital Forensics for the IoT)

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17 pages, 5629 KiB

Open AccessArticle

Blockchain Token-Based Wild-Simulated Ginseng Quality Management Method

by Youngjun Sung, Sunghyun Yu and Yoojae Won

Sensors 2022, 22(14), 5153; https://doi.org/10.3390/s22145153 - 09 Jul 2022

Cited by 4 | Viewed by 1723

Abstract

Countries require measures to prevent food fraud, such as forgery of certificates or content change during production, which can occur throughout the supply chain, even if they have a certification system for quality food management. Therefore, there are recent cases of the introduction of blockchain tokens for quality and supply chain management; however, there are difficulties in introducing tokens in food fields, such as forest and agricultural products. To introduce tokens in the food sector, we selected wild-simulated ginseng, subject to quality management in Korea, analyzed the quality management process of wild-simulated ginseng, and selected the target for blockchain token introduction. We then identified potential token-related issues from consumers and suggested possible solutions. Full article

(This article belongs to the Special Issue Security, Cybercrime, and Digital Forensics for the IoT)

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28 pages, 11281 KiB

Open AccessArticle

A Lightweight System-On-Chip Based Cryptographic Core for Low-Cost Devices

by Dennis Agyemanh Nana Gookyi and Kwangki Ryoo

Sensors 2022, 22(8), 3004; https://doi.org/10.3390/s22083004 - 14 Apr 2022

Cited by 4 | Viewed by 2459

Abstract

The backbone of the Internet of things (IoT) platform consists of tiny low-cost devices that are continuously exchanging data. These devices are usually limited in terms of hardware footprint, memory capacity, and processing power. The devices are usually insecure because implementing standard cryptographic algorithms requires the use of a large hardware footprint which leads to an increase in the prices of devices. This study implements a System-on-Chip (SoC) based lightweight cryptographic core that consists of two encryption protocols, four authentication protocols, and a key generation/exchange protocol for ultra-low-cost devices. The hardware architectures use the concept of resource sharing to minimize the hardware area. The lightweight cryptographic SoC is tested by designing a desktop software application to serve as an interface to the hardware. The design is implemented using Verilog HDL and the 130 nm CMOS cell library is used for synthesis, which results in 33 k gate equivalents at a maximum clock frequency of 50 MHz. Full article

(This article belongs to the Special Issue Security, Cybercrime, and Digital Forensics for the IoT)

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14 pages, 2196 KiB

Open AccessArticle

Runtime ML-DL Hybrid Inference Platform Based on Multiplexing Adaptive Space-Time Resolution for Fast Car Incident Prevention in Low-Power Embedded Systems

by Sunghoon Hong and Daejin Park

Sensors 2022, 22(8), 2998; https://doi.org/10.3390/s22082998 - 14 Apr 2022

Cited by 1 | Viewed by 1733

Abstract

Forward vehicle detection is the key technique to preventing car incidents in front. Artificial intelligence (AI) techniques are used to more accurately detect vehicles, but AI-based vehicle detection takes a lot of processing time due to its high computational complexity. When there is a risk of collision with a vehicle in front, the slow detection speed of the vehicle may lead to an accident. To quickly detect a vehicle in real-time, a high-speed and lightweight vehicle detection technique with similar detection performance to that of an existing AI-based vehicle detection is required. In addition, to apply forward collision warning system (FCWS) technology to vehicles, it is important to provide high performance based on low-power embedded systems because the vehicle’s battery consumption must remain low. The vehicle detection algorithm occupies the most resources in FCWS. To reduce power consumption, it is important to reduce the computational complexity of an algorithm, that is, the amount of resources required to run it. This paper describes a method for fast, accurate forward vehicle detection using machine learning and deep learning. To detect a vehicle in consecutive images consistently, a Kalman filter is used to predict the bounding box based on the tracking algorithm and correct it based on the detection algorithm. As a result, its vehicle detection speed is about 25.85 times faster than deep-learning-based object detection is, and its detection accuracy is better than machine-learning-based object detection is. Full article

(This article belongs to the Special Issue Security, Cybercrime, and Digital Forensics for the IoT)

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Review

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30 pages, 1172 KiB

Open AccessReview

Cloud Digital Forensics: Beyond Tools, Techniques, and Challenges

by Annas Wasim Malik, David Samuel Bhatti, Tae-Jin Park, Hafiz Usama Ishtiaq, Jae-Cheol Ryou and Ki-Il Kim

Sensors 2024, 24(2), 433; https://doi.org/10.3390/s24020433 - 10 Jan 2024

Viewed by 2788

Abstract

Cloud computing technology is rapidly becoming ubiquitous and indispensable. However, its widespread adoption also exposes organizations and individuals to a broad spectrum of potential threats. Despite the multiple advantages the cloud offers, organizations remain cautious about migrating their data and applications to the cloud due to fears of data breaches and security compromises. In light of these concerns, this study has conducted an in-depth examination of a variety of articles to enhance the comprehension of the challenges related to safeguarding and fortifying data within the cloud environment. Furthermore, the research has scrutinized several well-documented data breaches, analyzing the financial consequences they inflicted. Additionally, it scrutinizes the distinctions between conventional digital forensics and the forensic procedures specific to cloud computing. As a result of this investigation, the study has concluded by proposing potential opportunities for further research in this critical domain. By doing so, it contributes to our collective understanding of the complex panorama of cloud data protection and security, while acknowledging the evolving nature of technology and the need for ongoing exploration and innovation in this field. This study also helps in understanding the compound annual growth rate (CAGR) of cloud digital forensics, which is found to be quite high at ≈16.53% from 2023 to 2031. Moreover, its market is expected to reach ≈USD 36.9 billion by the year 2031; presently, it is ≈USD 11.21 billion, which shows that there are great opportunities for investment in this area. This study also strategically addresses emerging challenges in cloud digital forensics, providing a comprehensive approach to navigating and overcoming the complexities associated with the evolving landscape of cloud computing. Full article

(This article belongs to the Special Issue Security, Cybercrime, and Digital Forensics for the IoT)

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