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  1. Book ; Online: Trustworthy Privacy-preserving Hierarchical Ensemble and Federated Learning in Healthcare 4.0 with Blockchain

    Stephanie, Veronika / Khalil, Ibrahim / Atiquzzaman, Mohammed / Yi, Xun

    2023  

    Abstract: The advancement of Internet and Communication Technologies (ICTs) has led to the era of Industry 4.0. This shift is followed by healthcare industries creating the term Healthcare 4.0. In Healthcare 4.0, the use of IoT-enabled medical imaging devices for ... ...

    Abstract The advancement of Internet and Communication Technologies (ICTs) has led to the era of Industry 4.0. This shift is followed by healthcare industries creating the term Healthcare 4.0. In Healthcare 4.0, the use of IoT-enabled medical imaging devices for early disease detection has enabled medical practitioners to increase healthcare institutions' quality of service. However, Healthcare 4.0 is still lagging in Artificial Intelligence and big data compared to other Industry 4.0 due to data privacy concerns. In addition, institutions' diverse storage and computing capabilities restrict institutions from incorporating the same training model structure. This paper presents a secure multi-party computation-based ensemble federated learning with blockchain that enables heterogeneous models to collaboratively learn from healthcare institutions' data without violating users' privacy. Blockchain properties also allow the party to enjoy data integrity without trust in a centralized server while also providing each healthcare institution with auditability and version control capability.
    Keywords Computer Science - Cryptography and Security ; Computer Science - Artificial Intelligence
    Subject code 303
    Publishing date 2023-05-16
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Book ; Online: Smart Policy Control for Securing Federated Learning Management System

    Kalapaaking, Aditya Pribadi / Khalil, Ibrahim / Atiquzzaman, Mohammed

    2023  

    Abstract: The widespread adoption of Internet of Things (IoT) devices in smart cities, intelligent healthcare systems, and various real-world applications have resulted in the generation of vast amounts of data, often analyzed using different Machine Learning (ML) ...

    Abstract The widespread adoption of Internet of Things (IoT) devices in smart cities, intelligent healthcare systems, and various real-world applications have resulted in the generation of vast amounts of data, often analyzed using different Machine Learning (ML) models. Federated learning (FL) has been acknowledged as a privacy-preserving machine learning technology, where multiple parties cooperatively train ML models without exchanging raw data. However, the current FL architecture does not allow for an audit of the training process due to the various data-protection policies implemented by each FL participant. Furthermore, there is no global model verifiability available in the current architecture. This paper proposes a smart contract-based policy control for securing the Federated Learning (FL) management system. First, we develop and deploy a smart contract-based local training policy control on the FL participants' side. This policy control is used to verify the training process, ensuring that the evaluation process follows the same rules for all FL participants. We then enforce a smart contract-based aggregation policy to manage the global model aggregation process. Upon completion, the aggregated model and policy are stored on blockchain-based storage. Subsequently, we distribute the aggregated global model and the smart contract to all FL participants. Our proposed method uses smart policy control to manage access and verify the integrity of machine learning models. We conducted multiple experiments with various machine learning architectures and datasets to evaluate our proposed framework, such as MNIST and CIFAR-10.
    Keywords Computer Science - Cryptography and Security ; Computer Science - Machine Learning
    Subject code 006
    Publishing date 2023-05-15
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Book ; Online: A statistical approach for enhancing security in VANETs with efficient rogue node detection using fog computing

    Paranjothi, Anirudh / Atiquzzaman, Mohammed

    2021  

    Abstract: Rogue nodes broadcasting false information in beacon messages may lead to catastrophic consequences in Vehicular Ad Hoc Networks (VANETs). Previous researchers used either cryptography, trust scores, or past vehicle data to detect rogue nodes; however, ... ...

    Abstract Rogue nodes broadcasting false information in beacon messages may lead to catastrophic consequences in Vehicular Ad Hoc Networks (VANETs). Previous researchers used either cryptography, trust scores, or past vehicle data to detect rogue nodes; however, these methods suffer from high processing delay, overhead, and False-Positive Rate (FPR). We propose herein Greenshield's traffic model-based fog computing scheme called Fog-based Rogue Nodes Detection (F-RouND), which dynamically utilizes the On-Board Units (OBUs) of all vehicles in the region for rogue node detection. We aim to reduce the data processing delays and FPR in detecting rogue nodes at high vehicle densities. The performance of the F-RouND framework was evaluated via simulations. Results show that the F-RouND framework ensures 45% lower processing delays, 12% lower overhead, and 36% lower FPR at the urban scenario compared to the existing rogue node detection schemes even when the number of rogue nodes increases by up to 40% in the region.

    Comment: arXiv admin note: text overlap with arXiv:2102.00839
    Keywords Computer Science - Networking and Internet Architecture
    Subject code 000
    Publishing date 2021-08-23
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Book ; Online: Enhancing Security in VANETs with Efficient Sybil Attack Detection using Fog Computing

    Paranjothi, Anirudh / Atiquzzaman, Mohammed

    2021  

    Abstract: Vehicular ad hoc networks (VANETs) facilitate vehicles to broadcast beacon messages to ensure road safety. Rogue nodes in VANETs cause a Sybil attack to create an illusion of fake traffic congestion by broadcasting malicious information leading to ... ...

    Abstract Vehicular ad hoc networks (VANETs) facilitate vehicles to broadcast beacon messages to ensure road safety. Rogue nodes in VANETs cause a Sybil attack to create an illusion of fake traffic congestion by broadcasting malicious information leading to catastrophic consequences, such as the collision of vehicles. Previous researchers used either cryptography, trust scores, or past vehicle data to detect rogue nodes, but they suffer from high processing delay, overhead, and false-positive rate (FPR). We propose a fog computing-based Sybil attack detection for VANETs (FSDV), which utilizes onboard units (OBUs) of all the vehicles in the region to create a dynamic fog for rogue nodes detection. We aim to reduce the data processing delays, overhead, and FPR in detecting rogue nodes causing Sybil attacks at high vehicle densities. The performance of our framework was carried out with simulations using OMNET++ and SUMO simulators. Results show that our framework ensures 43% lower processing delays, 13% lower overhead, and 35% lower FPR at high vehicle densities compared to existing Sybil attack detection schemes.

    Comment: arXiv admin note: substantial text overlap with arXiv:2102.00839, arXiv:2108.10267
    Keywords Computer Science - Cryptography and Security ; Computer Science - Networking and Internet Architecture
    Subject code 303
    Publishing date 2021-08-23
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Book ; Online: Privacy-Preserving Ensemble Infused Enhanced Deep Neural Network Framework for Edge Cloud Convergence

    Stephanie, Veronika / Khalil, Ibrahim / Rahman, Mohammad Saidur / Atiquzzaman, Mohammed

    2023  

    Abstract: We propose a privacy-preserving ensemble infused enhanced Deep Neural Network (DNN) based learning framework in this paper for Internet-of-Things (IoT), edge, and cloud convergence in the context of healthcare. In the convergence, edge server is used for ...

    Abstract We propose a privacy-preserving ensemble infused enhanced Deep Neural Network (DNN) based learning framework in this paper for Internet-of-Things (IoT), edge, and cloud convergence in the context of healthcare. In the convergence, edge server is used for both storing IoT produced bioimage and hosting DNN algorithm for local model training. The cloud is used for ensembling local models. The DNN-based training process of a model with a local dataset suffers from low accuracy, which can be improved by the aforementioned convergence and Ensemble Learning. The ensemble learning allows multiple participants to outsource their local model for producing a generalized final model with high accuracy. Nevertheless, Ensemble Learning elevates the risk of leaking sensitive private data from the final model. The proposed framework presents a Differential Privacy-based privacy-preserving DNN with Transfer Learning for a local model generation to ensure minimal loss and higher efficiency at edge server. We conduct several experiments to evaluate the performance of our proposed framework.
    Keywords Computer Science - Cryptography and Security ; Computer Science - Artificial Intelligence
    Subject code 006
    Publishing date 2023-05-16
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    Inter-library loan at ZB MED

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  6. Book ; Online: How Can Optical Communications Shape the Future of Deep Space Communications? A Survey

    Karmous, Sarah / Adem, Nadia / Atiquzzaman, Mohammed / Samarakoon, Sumudu

    2022  

    Abstract: With a large number of deep space (DS) missions anticipated by the end of this decade, reliable and high capacity DS communications are needed more than ever. Nevertheless, existing technologies are far from meeting such a goal. Improving current systems ...

    Abstract With a large number of deep space (DS) missions anticipated by the end of this decade, reliable and high capacity DS communications are needed more than ever. Nevertheless, existing technologies are far from meeting such a goal. Improving current systems does not only require engineering leadership, but also, very crucially, investigating potential technologies that overcome the unique challenges of ultra-long DS links. To the best of our knowledge, there has not been any comprehensive surveys of DS communications technologies over the last decade. Free space optical (FSO) is an emerging DS technology, proven to acquire lower communications systems size, weight and power (SWaP) and achieve a very high capacity compared to its counterpart radio frequency (RF), the currently used DS technology. In this survey, we discuss the pros and cons of deep space optical communications (DSOC) and review physical and networking characteristics. Furthermore, we provide, for the very first time, thoughtful discussions about implementing orbital angular momentum (OAM) and quantum communications (QC) for DS. We elaborate on how these technologies among other field advances, including interplanetary network, and RF/FSO systems improve reliability, capacity, and security. This paper provides a holistic survey in DSOC technologies gathering 200+ fragmented literature and including novel perspectives aiming to setting the stage for more developments in the field.

    Comment: 17 pages, 8 Figures
    Keywords Electrical Engineering and Systems Science - Signal Processing
    Subject code 303
    Publishing date 2022-12-07
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    Inter-library loan at ZB MED

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  7. Book ; Online: Clustering in VANET

    Mukhtaruzzaman, Mohammad / Atiquzzaman, Mohammed

    Algorithms and Challenges

    2020  

    Abstract: Clustering is an important concept in vehicular ad hoc network (VANET) where several vehicles join to form a group based on common features. Mobility-based clustering strategies are the most common in VANET clustering; however, machine learning and fuzzy ...

    Abstract Clustering is an important concept in vehicular ad hoc network (VANET) where several vehicles join to form a group based on common features. Mobility-based clustering strategies are the most common in VANET clustering; however, machine learning and fuzzy logic algorithms are also the basis of many VANET clustering algorithms. Some VANET clustering algorithms integrate machine learning and fuzzy logic algorithms to make the cluster more stable and efficient. Network mobility (NEMO) and multi-hop-based strategies are also used for VANET clustering. Mobility and some other clustering strategies are presented in the existing literature reviews; however, extensive study of intelligence-based, mobility-based, and multi-hop-based strategies still missing in the VANET clustering reviews. In this paper, we presented a classification of intelligence-based clustering algorithms, mobility-based algorithms, and multi-hop-based algorithms with an analysis on the mobility metrics, evaluation criteria, challenges, and future directions of machine learning, fuzzy logic, mobility, NEMO, and multi-hop clustering algorithms.
    Keywords Computer Science - Networking and Internet Architecture
    Subject code 006
    Publishing date 2020-09-03
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Article ; Online: An Unsupervised Learning-Based Spatial Co-Location Detection System from Low-Power Consumption Sensor.

    Kosasih, David Ishak / Lee, Byung-Gook / Lim, Hyotaek / Atiquzzaman, Mohammed

    Sensors (Basel, Switzerland)

    2021  Volume 21, Issue 14

    Abstract: Spatial co-location detection is the task of inferring the co-location of two or more objects in the geographic space. Mobile devices, especially a smartphone, are commonly employed to accomplish this task with the human object. Previous work focused on ... ...

    Abstract Spatial co-location detection is the task of inferring the co-location of two or more objects in the geographic space. Mobile devices, especially a smartphone, are commonly employed to accomplish this task with the human object. Previous work focused on analyzing mobile GPS data to accomplish this task. While this approach may guarantee high accuracy from the perspective of the data, it is considered inefficient since knowing the object's absolute geographic location is not required to accomplish this task. This work proposed the implementation of the unsupervised learning-based algorithm, namely convolutional autoencoder, to infer the co-location of people from a low-power consumption sensor data-magnetometer readings. The idea is that if the trained model can also reconstruct the other data with the structural similarity (SSIM) index being above 0.5, we can then conclude that the observed individuals were co-located. The evaluation of our system has indicated that the proposed approach could recognize the spatial co-location of people from magnetometer readings.
    MeSH term(s) Algorithms ; Computers, Handheld ; Humans ; Smartphone ; Unsupervised Machine Learning
    Language English
    Publishing date 2021-07-13
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2052857-7
    ISSN 1424-8220 ; 1424-8220
    ISSN (online) 1424-8220
    ISSN 1424-8220
    DOI 10.3390/s21144773
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article ; Online: Smart Cybersecurity Framework for IoT-Empowered Drones: Machine Learning Perspective.

    Aldaej, Abdulaziz / Ahanger, Tariq Ahamed / Atiquzzaman, Mohammed / Ullah, Imdad / Yousufudin, Muhammad

    Sensors (Basel, Switzerland)

    2022  Volume 22, Issue 7

    Abstract: Drone advancements have ushered in new trends and possibilities in a variety of sectors, particularly for small-sized drones. Drones provide navigational interlocation services, which are made possible by the Internet of Things (IoT). Drone networks, on ... ...

    Abstract Drone advancements have ushered in new trends and possibilities in a variety of sectors, particularly for small-sized drones. Drones provide navigational interlocation services, which are made possible by the Internet of Things (IoT). Drone networks, on the other hand, are subject to privacy and security risks due to design flaws. To achieve the desired performance, it is necessary to create a protected network. The goal of the current study is to look at recent privacy and security concerns influencing the network of drones (NoD). The current research emphasizes the importance of a security-empowered drone network to prevent interception and intrusion. A hybrid ML technique of logistic regression and random forest is used for the purpose of classification of data instances for maximal efficacy. By incorporating sophisticated artificial-intelligence-inspired techniques into the framework of a NoD, the proposed technique mitigates cybersecurity vulnerabilities while making the NoD protected and secure. For validation purposes, the suggested technique is tested against a challenging dataset, registering enhanced performance results in terms of temporal efficacy (34.56 s), statistical measures (precision (97.68%), accuracy (98.58%), recall (98.59%), F-measure (99.01%), reliability (94.69%), and stability (0.73).
    MeSH term(s) Computer Security ; Internet of Things ; Machine Learning ; Reproducibility of Results ; Unmanned Aerial Devices
    Language English
    Publishing date 2022-03-29
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2052857-7
    ISSN 1424-8220 ; 1424-8220
    ISSN (online) 1424-8220
    ISSN 1424-8220
    DOI 10.3390/s22072630
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article ; Online: Distributed Blockchain-Based Platform for Unmanned Aerial Vehicles.

    Ahamed Ahanger, Tariq / Aldaej, Abdulaziz / Atiquzzaman, Mohammed / Ullah, Imdad / Yousufudin, Muhammad

    Computational intelligence and neuroscience

    2022  Volume 2022, Page(s) 4723124

    Abstract: Internet of Things (IoT)-inspired drone environment is having a greater influence on daily lives in the form of drone-based smart electricity monitoring, traffic routing, and personal healthcare. However, communication between drones and ground control ... ...

    Abstract Internet of Things (IoT)-inspired drone environment is having a greater influence on daily lives in the form of drone-based smart electricity monitoring, traffic routing, and personal healthcare. However, communication between drones and ground control systems must be protected to avoid potential vulnerabilities and improve coordination among scattered UAVs in the IoT context. In the current paper, a distributed UAV scheme is proposed that uses blockchain technology and a network topology similar to the IoT and cloud server to secure communications during data collection and transmission and reduce the likelihood of attack by maliciously manipulated UAVs. As an alternative to relying on a traditional blockchain approach, a unique, safe, and lightweight blockchain architecture is proposed that reduces computing and storage requirements while keeping privacy and security advantages. In addition, a unique reputation-based consensus protocol is built to assure the dependability of the decentralized network. Numerous types of transactions are established to characterize diverse data access. To validate the presented blockchain-based distributed system, performance evaluations are conducted to estimate the statistical effectiveness in the form of temporal delay, packet flow efficacy, precision, specificity, sensitivity, and security efficiency.
    MeSH term(s) Blockchain ; Computer Communication Networks ; Computer Security ; Delivery of Health Care ; Unmanned Aerial Devices
    Language English
    Publishing date 2022-08-31
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2388208-6
    ISSN 1687-5273 ; 1687-5273
    ISSN (online) 1687-5273
    ISSN 1687-5273
    DOI 10.1155/2022/4723124
    Database MEDical Literature Analysis and Retrieval System OnLINE

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