sensors-logo

Journal Browser

Journal Browser

Security for Mobile Sensing Networks

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

Deadline for manuscript submissions: closed (20 August 2022) | Viewed by 48183

Special Issue Editor

Special Issue Information

Dear Colleagues,

With the proliferation of mobile networks and the introduction of 5G networks in particular, the Internet of Things (IoT) and sensing networks have evolved to support communication any time anywhere regardless of movement. Such an evolution will expectedly introduce new innovative services and applications. Obvisouly, these mobile sensing networks will be vulnerable to a variety of security threats and attacks if not properly secured.

This Special Issue aims to bring together the current state-of-the-art research and future directions for security for mobile sensing networks. For this goal, we cordially invite researchers and engineers from both academia and industry to submit their original and novel work for inclusion in this Special Issue. Tutorial or survey papers are also welcome.

The topics related to this Special Issue include but are not limited to:

  • Secure architecture and models for mobile sensing networks;
  • Security issues and protocols for mobile sensing networks;
  • Security threats, models, and countermeasures for mobile sensing networks;
  • Access control and authentication for mobile sensing networks;
  • Privacy, trust, and reliability for mobile sensing networks;
  • Risk/threat assessment and management for mobile sensing networks;
  • Intrusion detection techniques for mobile sensing networks;
  • Availability, recovery, and auditing for mobile sensing networks;
  • Mobility management and handover security for mobile sensing networks;
  • Software security for mobile sensing networks;
  • Threat intelligence for mobile sensing networks;
  • Vehicular-sensor network security;
  • Security of wearable and body-area sensor networks;
  • Security of battlefield mobile sensor applications and solutions;
  • Cryptographic hardware development for mobile sensing devices;
  • Others and emerging new topics.

Dr. Ilsun You
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (16 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

18 pages, 2386 KiB  
Article
Data Analytics, Self-Organization, and Security Provisioning for Smart Monitoring Systems
by Raja Waseem Anwar, Kashif Naseer Qureshi, Wamda Nagmeldin, Abdelzahir Abdelmaboud, Kayhan Zrar Ghafoor, Ibrahim Tariq Javed and Noel Crespi
Sensors 2022, 22(19), 7201; https://doi.org/10.3390/s22197201 - 22 Sep 2022
Cited by 5 | Viewed by 1549
Abstract
Internet availability and its integration with smart technologies have favored everyday objects and things and offered new areas, such as the Internet of Things (IoT). IoT refers to a concept where smart devices or things are connected and create a network. This new [...] Read more.
Internet availability and its integration with smart technologies have favored everyday objects and things and offered new areas, such as the Internet of Things (IoT). IoT refers to a concept where smart devices or things are connected and create a network. This new area has suffered from big data handling and security issues. There is a need to design a data analytics model by using new 5G technologies, architecture, and a security model. Reliable data communication in the presence of legitimate nodes is always one of the challenges in these networks. Malicious nodes are generating inaccurate information and breach the user’s security. In this paper, a data analytics model and self-organizing architecture for IoT networks are proposed to understand the different layers of technologies and processes. The proposed model is designed for smart environmental monitoring systems. This paper also proposes a security model based on an authentication, detection, and prediction mechanism for IoT networks. The proposed model enhances security and protects the network from DoS and DDoS attacks. The proposed model evaluates in terms of accuracy, sensitivity, and specificity by using machine learning algorithms. Full article
(This article belongs to the Special Issue Security for Mobile Sensing Networks)
Show Figures

Figure 1

30 pages, 79065 KiB  
Article
ACF: An Armed CCTV Footage Dataset for Enhancing Weapon Detection
by Narit Hnoohom, Pitchaya Chotivatunyu and Anuchit Jitpattanakul
Sensors 2022, 22(19), 7158; https://doi.org/10.3390/s22197158 - 21 Sep 2022
Cited by 2 | Viewed by 4911
Abstract
Thailand, like other countries worldwide, has experienced instability in recent years. If current trends continue, the number of crimes endangering people or property will expand. Closed-circuit television (CCTV) technology is now commonly utilized for surveillance and monitoring to ensure people’s safety. A weapon [...] Read more.
Thailand, like other countries worldwide, has experienced instability in recent years. If current trends continue, the number of crimes endangering people or property will expand. Closed-circuit television (CCTV) technology is now commonly utilized for surveillance and monitoring to ensure people’s safety. A weapon detection system can help police officers with limited staff minimize their workload through on-screen surveillance. Since CCTV footage captures the entire incident scenario, weapon detection becomes challenging due to the small weapon objects in the footage. Due to public datasets providing inadequate information on our interested scope of CCTV image’s weapon detection, an Armed CCTV Footage (ACF) dataset, the self-collected mockup CCTV footage of pedestrians armed with pistols and knives, was collected for different scenarios. This study aimed to present an image tilling-based deep learning for small weapon object detection. The experiments were conducted on a public benchmark dataset (Mock Attack) to evaluate the detection performance. The proposed tilling approach achieved a significantly better mAP of 10.22 times. The image tiling approach was used to train different object detection models to analyze the improvement. On SSD MobileNet V2, the tiling ACF Dataset achieved an mAP of 0.758 on the pistol and knife evaluation. The proposed method for enhancing small weapon detection by using the tiling approach with our ACF Dataset can significantly enhance the performance of weapon detection. Full article
(This article belongs to the Special Issue Security for Mobile Sensing Networks)
Show Figures

Figure 1

17 pages, 3076 KiB  
Article
Implementation of Disassembler on Microcontroller Using Side-Channel Power Consumption Leakage
by Daehyeon Bae and Jaecheol Ha
Sensors 2022, 22(15), 5900; https://doi.org/10.3390/s22155900 - 07 Aug 2022
Cited by 3 | Viewed by 1659
Abstract
With the development of 5G and network technology, the usage of IoT devices has become popular. Because most of these IoT devices can be controlled by an adversary away from the administrator, several security issues such as firmware dumping can arise. Firmware dumping [...] Read more.
With the development of 5G and network technology, the usage of IoT devices has become popular. Because most of these IoT devices can be controlled by an adversary away from the administrator, several security issues such as firmware dumping can arise. Firmware dumping is the cornerstone or goal of many types of hardware hacking. Therefore, many IoT device manufacturers adopt some protection mechanisms such as the restriction of hardware debuggers. However, several recent studies have shown that the operating instructions of an IoT device can be recovered through the profiling-based side-channel analysis. The Side-Channel-Based Disassembler (SCBD) refers to software that recovers instructions of the device only from the side-channel signal. The SCBD is powerful enough to defeat many firmware protection mechanisms. In this paper, we show how an adversary can build an instruction (opcode)-level disassembler using the power consumption signal of commercial microcontrollers (MCUs) such as the 8-bit ATxmega128 and 32-bit STM32F0. To implement the SCBD, we elaborately constructed the instruction template considering the pipeline of the target MCUs through instruction sequence analysis. Furthermore, we preprocessed the side-channel signals using the Continuous Wavelet Transform (CWT) for noise reduction and Kullback-Leibler Divergence (KLD) for instruction feature extraction. Our experimental results show that the machine-learning-based instruction disassembling models can recover the operating instructions with an accuracy of about 91.9% and 98.6% for the ATxmega128 and STM32F0, respectively. Furthermore, we achieved an accuracy of 77% and 96.5% in a cross-board validation. Full article
(This article belongs to the Special Issue Security for Mobile Sensing Networks)
Show Figures

Figure 1

24 pages, 1870 KiB  
Article
Deep Residual Network for Smartwatch-Based User Identification through Complex Hand Movements
by Sakorn Mekruksavanich and Anuchit Jitpattanakul
Sensors 2022, 22(8), 3094; https://doi.org/10.3390/s22083094 - 18 Apr 2022
Cited by 39 | Viewed by 3018
Abstract
Wearable technology has advanced significantly and is now used in various entertainment and business contexts. Authentication methods could be trustworthy, transparent, and non-intrusive to guarantee that users can engage in online communications without consequences. An authentication system on a security framework starts with [...] Read more.
Wearable technology has advanced significantly and is now used in various entertainment and business contexts. Authentication methods could be trustworthy, transparent, and non-intrusive to guarantee that users can engage in online communications without consequences. An authentication system on a security framework starts with a process for identifying the user to ensure that the user is permitted. Establishing and verifying an individual’s appearance usually requires a lot of effort. Recent years have seen an increase in the usage of activity-based user identification systems to identify individuals. Despite this, there has not been much research into how complex hand movements can be used to determine the identity of an individual. This research used a one-dimensional residual network with squeeze-and-excitation (SE) configurations called the 1D-ResNet-SE model to investigate hand movements and user identification. According to the findings, the SE modules have enhanced the one-dimensional residual network’s identification ability. As a deep learning model, the proposed methodology is capable of effectively identifying features from the input smartwatch sensor and could be utilized as an end-to-end model to clarify the modeling process. The 1D-ResNet-SE identification model is superior to the other models. Hand movement assessment based on deep learning is an effective technique to identify smartwatch users. Full article
(This article belongs to the Special Issue Security for Mobile Sensing Networks)
Show Figures

Figure 1

30 pages, 2970 KiB  
Article
Two-Stage Intelligent Model for Detecting Malicious DDoS Behavior
by Man Li, Huachun Zhou and Yajuan Qin
Sensors 2022, 22(7), 2532; https://doi.org/10.3390/s22072532 - 25 Mar 2022
Cited by 14 | Viewed by 1999
Abstract
5G technologies provide ubiquitous connectivity. However, 5G security is a particularly important issue. Moreover, because public datasets are outdated, we need to create a self-generated dataset on the virtual platform. Therefore, we propose a two-stage intelligent detection model to enable 5G networks to [...] Read more.
5G technologies provide ubiquitous connectivity. However, 5G security is a particularly important issue. Moreover, because public datasets are outdated, we need to create a self-generated dataset on the virtual platform. Therefore, we propose a two-stage intelligent detection model to enable 5G networks to withstand security issues and threats. Finally, we define malicious traffic detection capability metrics. We apply the self-generated dataset and metrics to thoroughly evaluate the proposed mechanism. We compare our proposed method with benchmark statistics and neural network algorithms. The experimental results show that the two-stage intelligent detection model can distinguish between benign and abnormal traffic and classify 21 kinds of DDoS. Our analysis also shows that the proposed approach outperforms all the compared approaches in terms of detection rate, malicious traffic detection capability, and response time. Full article
(This article belongs to the Special Issue Security for Mobile Sensing Networks)
Show Figures

Figure 1

12 pages, 452 KiB  
Article
FILM: Filtering and Machine Learning for Malware Detection in Edge Computing
by Young Jae Kim, Chan-Hyeok Park and MyungKeun Yoon
Sensors 2022, 22(6), 2150; https://doi.org/10.3390/s22062150 - 10 Mar 2022
Cited by 7 | Viewed by 2231
Abstract
Machine learning with static-analysis features extracted from malware files has been adopted to detect malware variants, which is desirable for resource-constrained edge computing and Internet-of-Things devices with sensors; however, this learned model suffers from a misclassification problem because some malicious files have almost [...] Read more.
Machine learning with static-analysis features extracted from malware files has been adopted to detect malware variants, which is desirable for resource-constrained edge computing and Internet-of-Things devices with sensors; however, this learned model suffers from a misclassification problem because some malicious files have almost the same static-analysis features as benign ones. In this paper, we present a new detection method for edge computing that can utilize existing machine learning models to classify a suspicious file into either benign, malicious, or unpredictable categories while existing models make only a binary decision of either benign or malicious. The new method can utilize any existing deep learning models developed for malware detection after appending a simple sigmoid function to the models. When interpreting the sigmoid value during the testing phase, the new method determines if the model is confident about its prediction; therefore, the new method can take only the prediction of high accuracy, which reduces incorrect predictions on ambiguous static-analysis features. Through experiments on real malware datasets, we confirm that the new scheme significantly enhances the accuracy, precision, and recall of existing deep learning models. For example, the accuracy is enhanced from 0.96 to 0.99, while some files are classified as unpredictable that can be entrusted to the cloud for further dynamic or human analysis. Full article
(This article belongs to the Special Issue Security for Mobile Sensing Networks)
Show Figures

Figure 1

23 pages, 1042 KiB  
Article
Privacy Policies of IoT Devices: Collection and Analysis
by Mikhail Kuznetsov, Evgenia Novikova, Igor Kotenko and Elena Doynikova
Sensors 2022, 22(5), 1838; https://doi.org/10.3390/s22051838 - 25 Feb 2022
Cited by 3 | Viewed by 3583
Abstract
Currently, personal data collection and processing are widely used while providing digital services within mobile sensing networks for their operation, personalization, and improvement. Personal data are any data that identifiably describe a person. Legislative and regulatory documents adopted in recent years define the [...] Read more.
Currently, personal data collection and processing are widely used while providing digital services within mobile sensing networks for their operation, personalization, and improvement. Personal data are any data that identifiably describe a person. Legislative and regulatory documents adopted in recent years define the key requirements for the processing of personal data. They are based on the principles of lawfulness, fairness, and transparency of personal data processing. Privacy policies are the only legitimate way to provide information on how the personal data of service and device users is collected, processed, and stored. Therefore, the problem of making privacy policies clear and transparent is extremely important as its solution would allow end users to comprehend the risks associated with personal data processing. Currently, a number of approaches for analyzing privacy policies written in natural language have been proposed. Most of them require a large training dataset of privacy policies. In the paper, we examine the existing corpora of privacy policies available for training, discuss their features and conclude on the need for a new dataset of privacy policies for devices and services of the Internet of Things as a part of mobile sensing networks. The authors develop a new technique for collecting and cleaning such privacy policies. The proposed technique differs from existing ones by the usage of e-commerce platforms as a starting point for document search and enables more targeted collection of the URLs to the IoT device manufacturers’ privacy policies. The software tool implementing this technique was used to collect a new corpus of documents in English containing 592 unique privacy policies. The collected corpus contains mainly privacy policies that are developed for the Internet of Things and reflect the latest legislative requirements. The paper also presents the results of the statistical and semantic analysis of the collected privacy policies. These results could be further used by the researchers when elaborating techniques for analysis of the privacy policies written in natural language targeted to enhance their transparency for the end user. Full article
(This article belongs to the Special Issue Security for Mobile Sensing Networks)
Show Figures

Figure 1

19 pages, 5156 KiB  
Article
Multi-Source Knowledge Reasoning for Data-Driven IoT Security
by Shuqin Zhang, Guangyao Bai, Hong Li, Peipei Liu, Minzhi Zhang and Shujun Li
Sensors 2021, 21(22), 7579; https://doi.org/10.3390/s21227579 - 15 Nov 2021
Cited by 8 | Viewed by 2451
Abstract
Nowadays, there are different kinds of public knowledge bases for cyber security vulnerability and threat intelligence which can be used for IoT security threat analysis. However, the heterogeneity of these knowledge bases and the complexity of the IoT environments make network security situation [...] Read more.
Nowadays, there are different kinds of public knowledge bases for cyber security vulnerability and threat intelligence which can be used for IoT security threat analysis. However, the heterogeneity of these knowledge bases and the complexity of the IoT environments make network security situation awareness and threat assessment difficult. In this paper, we integrate vulnerabilities, weaknesses, affected platforms, tactics, attack techniques, and attack patterns into a coherent set of links. In addition, we propose an IoT security ontology model, namely, the IoT Security Threat Ontology (IoTSTO), to describe the elements of IoT security threats and design inference rules for threat analysis. This IoTSTO expands the current knowledge domain of cyber security ontology modeling. In the IoTSTO model, the proposed multi-source knowledge reasoning method can perform the following tasks: assess the threats of the IoT environment, automatically infer mitigations, and separate IoT nodes that are subject to specific threats. The method above provides support to security managers in their deployment of security solutions. This paper completes the association of current public knowledge bases for IoT security and solves the semantic heterogeneity of multi-source knowledge. In this paper, we reveal the scope of public knowledge bases and their interrelationships through the multi-source knowledge reasoning method for IoT security. In conclusion, the paper provides a unified, extensible, and reusable method for IoT security analysis and decision making. Full article
(This article belongs to the Special Issue Security for Mobile Sensing Networks)
Show Figures

Figure 1

21 pages, 2135 KiB  
Article
Deep Learning Approaches for Continuous Authentication Based on Activity Patterns Using Mobile Sensing
by Sakorn Mekruksavanich and Anuchit Jitpattanakul
Sensors 2021, 21(22), 7519; https://doi.org/10.3390/s21227519 - 12 Nov 2021
Cited by 55 | Viewed by 3507
Abstract
Smartphones as ubiquitous gadgets are rapidly becoming more intelligent and context-aware as sensing, networking, and processing capabilities advance. These devices provide users with a comprehensive platform to undertake activities such as socializing, communicating, sending and receiving e-mails, and storing and accessing personal data [...] Read more.
Smartphones as ubiquitous gadgets are rapidly becoming more intelligent and context-aware as sensing, networking, and processing capabilities advance. These devices provide users with a comprehensive platform to undertake activities such as socializing, communicating, sending and receiving e-mails, and storing and accessing personal data at any time and from any location. Nowadays, smartphones are used to store a multitude of private and sensitive data including bank account information, personal identifiers, account passwords and credit card information. Many users remain permanently signed in and, as a result, their mobile devices are vulnerable to security and privacy risks through assaults by criminals. Passcodes, PINs, pattern locks, facial verification, and fingerprint scans are all susceptible to various assaults including smudge attacks, side-channel attacks, and shoulder-surfing attacks. To solve these issues, this research introduces a new continuous authentication framework called DeepAuthen, which identifies smartphone users based on their physical activity patterns as measured by the accelerometer, gyroscope, and magnetometer sensors on their smartphone. We conducted a series of tests on user authentication using several deep learning classifiers, including our proposed deep learning network termed DeepConvLSTM on the three benchmark datasets UCI-HAR, WISDM-HARB and HMOG. Results demonstrated that combining various motion sensor data obtained the highest accuracy and energy efficiency ratio (EER) values for binary classification. We also conducted a thorough examination of the continuous authentication outcomes, and the results supported the efficacy of our framework. Full article
(This article belongs to the Special Issue Security for Mobile Sensing Networks)
Show Figures

Graphical abstract

17 pages, 5943 KiB  
Article
VerificationTalk: A Verification and Security Mechanism for IoT Applications
by Min-Zheng Shieh, Yi-Bing Lin and Yin-Jui Hsu
Sensors 2021, 21(22), 7449; https://doi.org/10.3390/s21227449 - 09 Nov 2021
Cited by 5 | Viewed by 2354
Abstract
An Internet of Things (IoT) application typically involves implementations in both the device domain and the network domain. In this two-domain environment, it is possible that application developers implement the wrong network functions and/or connect some IoT devices that should never be linked, [...] Read more.
An Internet of Things (IoT) application typically involves implementations in both the device domain and the network domain. In this two-domain environment, it is possible that application developers implement the wrong network functions and/or connect some IoT devices that should never be linked, which result in the execution of wrong operations on network functions. To resolve these issues, we propose the VerificationTalk mechanism to prevent inappropriate IoT application deployment. VerificationTalk consists of two subsystems: the BigraphTalk subsystem which verifies IoT device configuration; and AFLtalk which validates the network functions. VerificationTalk provides mechanisms to conduct online anomaly detection by using a runtime monitor and offline by using American Fuzzy Lop (AFL). The runtime monitor is capable of intercepting potentially harmful data targeting IoT devices. When VerificationTalk detects errors, it provides feedback for debugging. VerificationTalk also assists in building secure IoT applications by identifying security loopholes in network applications. By the appropriate design of the IoTtalk execution engine, the testing capacity of AFLtalk is three times that of traditional AFL approaches. Full article
(This article belongs to the Special Issue Security for Mobile Sensing Networks)
Show Figures

Figure 1

18 pages, 3325 KiB  
Article
A Security Management and Control Solution of Smart Park Based on Sensor Networks
by Yue Zhao, Bo Tian, Yiru Niu, Hao Zhang, Zhongqiang Yi and Ruiqi Zeng
Sensors 2021, 21(20), 6815; https://doi.org/10.3390/s21206815 - 13 Oct 2021
Cited by 4 | Viewed by 1978
Abstract
As a typical application of sensor networks, there exist many information security problems in smart parks, such as confusion of personnel access, lack of security management, disorderly data flow, insufficient collection of audit evidence, and so on. Aiming at the scenario of personnel [...] Read more.
As a typical application of sensor networks, there exist many information security problems in smart parks, such as confusion of personnel access, lack of security management, disorderly data flow, insufficient collection of audit evidence, and so on. Aiming at the scenario of personnel and equipment moving in different areas of smart parks, the paper proposes a joint authorization and dynamic access control mechanism, which can provide unified identity management services, access control services, and policy management services, and effectively solve the problem of multi-authorization in user identity and authority management. The license negotiation interaction protocol is designed to prevent common network attack threats in the process of identity authentication and authority management. In order to realize the tamper-proof storage of personnel and equipment movement trajectory, the paper also designs a movement trajectory traceability protocol based on a Merkle tree, which solves the problems of internal personnel malicious attack, trusted third-party dependency bottleneck, high overheads of tracking algorithms, and so on. The experimental results show that compared with the current security control mechanisms for sensor networks, the joint authorization, and dynamic access control mechanism can support multi-party authorization and traceability, while the overhead it generates in initialization, encryption, decryption, and key generation steps are basically the same as other mechanisms do. Full article
(This article belongs to the Special Issue Security for Mobile Sensing Networks)
Show Figures

Figure 1

20 pages, 83992 KiB  
Article
Screen-Shooting Resilient Watermarking Scheme via Learned Invariant Keypoints and QT
by Li Li, Rui Bai, Shanqing Zhang, Chin-Chen Chang and Mengtao Shi
Sensors 2021, 21(19), 6554; https://doi.org/10.3390/s21196554 - 30 Sep 2021
Cited by 15 | Viewed by 2583
Abstract
This paper proposes a screen-shooting resilient watermarking scheme via learned invariant keypoints and QT; that is, if the watermarked image is displayed on the screen and captured by a camera, the watermark can be still extracted from the photo. A screen-shooting resilient watermarking [...] Read more.
This paper proposes a screen-shooting resilient watermarking scheme via learned invariant keypoints and QT; that is, if the watermarked image is displayed on the screen and captured by a camera, the watermark can be still extracted from the photo. A screen-shooting resilient watermarking algorithm should meet the following two basic requirements: robust keypoints and a robust watermark algorithm. In our case, we embedded watermarks by combining the feature region filtering model to SuperPoint (FRFS) neural networks, quaternion discrete Fourier transform (QDFT), and tensor decomposition (TD). First we applied FRFS to locate the embedding feature regions which are decided by the keypoints that survive screen-shooting. Second, we structured watermark embedding regions centered at keypoints. Third, the watermarks were embedded by the QDFT and TD (QT) algorithm, which is robust for capturing process attacks. In a partial shooting scenario, the watermark is repeatedly embedded into different regions in an image to enhance robustness. Finally, we extracted the watermarks from at least one region at the extraction stage. The experimental results showed that the proposed scheme is very robust for camera shooting (including partial shooting) different shooting scenarios, and special attacks. Moreover, the efficient mechanism of screen-shooting resilient watermarking could have propietary protection and leak tracing applications. Full article
(This article belongs to the Special Issue Security for Mobile Sensing Networks)
Show Figures

Figure 1

12 pages, 1010 KiB  
Communication
Automatically Attributing Mobile Threat Actors by Vectorized ATT&CK Matrix and Paired Indicator
by Kyoungmin Kim, Youngsup Shin, Justin Lee and Kyungho Lee
Sensors 2021, 21(19), 6522; https://doi.org/10.3390/s21196522 - 29 Sep 2021
Cited by 17 | Viewed by 3465
Abstract
During the past decade, mobile attacks have been established as an indispensable attack vector adopted by Advanced Persistent Threat (APT) groups. The ubiquitous nature of the smartphone has allowed users to use mobile payments and store private or sensitive data (i.e., login credentials). [...] Read more.
During the past decade, mobile attacks have been established as an indispensable attack vector adopted by Advanced Persistent Threat (APT) groups. The ubiquitous nature of the smartphone has allowed users to use mobile payments and store private or sensitive data (i.e., login credentials). Consequently, various APT groups have focused on exploiting these vulnerabilities. Past studies have proposed automated classification and detection methods, while few studies have covered the cyber attribution. Our study introduces an automated system that focuses on cyber attribution. Adopting MITRE’s ATT&CK for mobile, we performed our study using the tactic, technique, and procedures (TTPs). By comparing the indicator of compromise (IoC), we were able to help reduce the false flags during our experiment. Moreover, we examined 12 threat actors and 120 malware using the automated method for detecting cyber attribution. Full article
(This article belongs to the Special Issue Security for Mobile Sensing Networks)
Show Figures

Figure 1

18 pages, 8831 KiB  
Article
Research on a Task Offloading Strategy for the Internet of Vehicles Based on Reinforcement Learning
by Shuo Xiao, Shengzhi Wang, Jiayu Zhuang, Tianyu Wang and Jiajia Liu
Sensors 2021, 21(18), 6058; https://doi.org/10.3390/s21186058 - 09 Sep 2021
Cited by 10 | Viewed by 2517
Abstract
Today, vehicles are increasingly being connected to the Internet of Things, which enables them to obtain high-quality services. However, the numerous vehicular applications and time-varying network status make it challenging for onboard terminals to achieve efficient computing. Therefore, based on a three-stage model [...] Read more.
Today, vehicles are increasingly being connected to the Internet of Things, which enables them to obtain high-quality services. However, the numerous vehicular applications and time-varying network status make it challenging for onboard terminals to achieve efficient computing. Therefore, based on a three-stage model of local-edge clouds and reinforcement learning, we propose a task offloading algorithm for the Internet of Vehicles (IoV). First, we establish communication methods between vehicles and their cost functions. In addition, according to the real-time state of vehicles, we analyze their computing requirements and the price function. Finally, we propose an experience-driven offloading strategy based on multi-agent reinforcement learning. The simulation results show that the algorithm increases the probability of success for the task and achieves a balance between the task vehicle delay, expenditure, task vehicle utility and service vehicle utility under various constraints. Full article
(This article belongs to the Special Issue Security for Mobile Sensing Networks)
Show Figures

Figure 1

20 pages, 8334 KiB  
Article
Mask Attention-SRGAN for Mobile Sensing Networks
by Chi-En Huang, Ching-Chun Chang and Yung-Hui Li
Sensors 2021, 21(17), 5973; https://doi.org/10.3390/s21175973 - 06 Sep 2021
Cited by 2 | Viewed by 2727
Abstract
Biometrics has been shown to be an effective solution for the identity recognition problem, and iris recognition, as well as face recognition, are accurate biometric modalities, among others. The higher resolution inside the crucial region reveals details of the physiological characteristics which provides [...] Read more.
Biometrics has been shown to be an effective solution for the identity recognition problem, and iris recognition, as well as face recognition, are accurate biometric modalities, among others. The higher resolution inside the crucial region reveals details of the physiological characteristics which provides discriminative information to achieve extremely high recognition rate. Due to the growing needs for the IoT device in various applications, the image sensor is gradually integrated in the IoT device to decrease the cost, and low-cost image sensors may be preferable than high-cost ones. However, low-cost image sensors may not satisfy the minimum requirement of the resolution, which definitely leads to the decrease of the recognition accuracy. Therefore, how to maintain high accuracy for biometric systems without using expensive high-cost image sensors in mobile sensing networks becomes an interesting and important issue. In this paper, we proposed MA-SRGAN, a single image super-resolution (SISR) algorithm, based on the mask-attention mechanism used in Generative Adversarial Network (GAN). We modified the latest state-of-the-art (nESRGAN+) in the GAN-based SR model by adding an extra part of a discriminator with an additional loss term to force the GAN to pay more attention within the region of interest (ROI). The experiments were performed on the CASIA-Thousand-v4 dataset and the Celeb Attribute dataset. The experimental results show that the proposed method successfully learns the details of features inside the crucial region by enhancing the recognition accuracies after image super-resolution (SR). Full article
(This article belongs to the Special Issue Security for Mobile Sensing Networks)
Show Figures

Figure 1

20 pages, 9945 KiB  
Article
Robust Iris Segmentation Algorithm in Non-Cooperative Environments Using Interleaved Residual U-Net
by Yung-Hui Li, Wenny Ramadha Putri, Muhammad Saqlain Aslam and Ching-Chun Chang
Sensors 2021, 21(4), 1434; https://doi.org/10.3390/s21041434 - 18 Feb 2021
Cited by 28 | Viewed by 3838
Abstract
Iris segmentation plays an important and significant role in the iris recognition system. The prerequisite for accurate iris recognition is the correctness of iris segmentation. However, the efficiency and robustness of traditional iris segmentation methods are severely challenged in a non-cooperative environment because [...] Read more.
Iris segmentation plays an important and significant role in the iris recognition system. The prerequisite for accurate iris recognition is the correctness of iris segmentation. However, the efficiency and robustness of traditional iris segmentation methods are severely challenged in a non-cooperative environment because of unfavorable factors, for instance, occlusion, blur, low resolution, off-axis, motion, and specular reflections. All of the above factors seriously reduce the accuracy of iris segmentation. In this paper, we present a novel iris segmentation algorithm that localizes the outer and inner boundaries of the iris image. We propose a neural network model called “Interleaved Residual U-Net” (IRUNet) for semantic segmentation and iris mask synthesis. The K-means clustering is applied to select saliency points set in order to recover the outer boundary of the iris, whereas the inner border is recovered by selecting another set of saliency points on the inner side of the mask. Experimental results demonstrate that the proposed iris segmentation algorithm can achieve the mean IOU value of 98.9% and 97.7% for inner and outer boundary estimation, respectively, which outperforms the existing approaches on the challenging CASIA-Iris-Thousand database. Full article
(This article belongs to the Special Issue Security for Mobile Sensing Networks)
Show Figures

Figure 1

Back to TopTop