Networked Systems Laboratory

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Unmanned aerial systems, namely drones, have greatly improved and expanded drastically over the years. Due to their efficiency and ease of use, drones have been utilized in a wide range of applications. Despite various potential uses, drones are also being utilized for illegal operations and exposing security threats to citizens. It is vital to install an effective anti-drone system to identify and defend against intruding malevolent drones to protect national security. Although there has been tremendous advancement in the development of machine learning to deploy lightweight architectures in the sensor industry, no such drone detection approach has yet been described in the literature. Therefore, this paper proposed a lightweight convolution neural network (CNN), namely RFDNet, to investigate the problem of 17 types of drone RF fingerprint classification problems in the low SNR regime. The network is configured with two principle modules, which are leveraged by the grouped and depth-wise convolution layers, incorporating accuracy improvement while keeping the complexity low. Notably, most existing networks fail to outstandingly detect drones at low SNR levels because the RF signal envelope is distorted and the transient information is lost in the noise. To solve this issue, we collected an open-source RF dataset that stores 17 types of drone RF signals at 30 dB SNR. To investigate the RF dataset at various SNR levels, we regenerate the dataset at different SNRs (i.e., 
−
10
 dB to 30 dB SNR with the 5 dB interval) and analyze the performance of the proposed network. The empirical results show that RFDNet performed outstandingly compared to the existing deep learning-based drone detection methods and achieved an overall 99.07% accuracy at 15 dB signal-to-noise ratio (SNR).

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The widespread implementation of the Internet of Flying Things (IoFT) in several domains, such as military, industrial, and civilian, has emphasized their effectiveness in delivering adaptable and dynamic services. However, IoFT networks' growing interconnectivity and transparency make them susceptible to various malicious threats. Existing intrusion detection systems (IDS), especially those grounded on antiquated machine learning (ML) methods, face difficulty identifying real-time and abrupt assaults since they depend on obsolete datasets. Recent progress in Large Language Models (LLMs) has shown remarkable competencies in understanding complex data and tackling complicated challenges. In IoFT networks, these advancements could offer a potential solution to conventional IDS constraints.
Furthermore, distinct IDS solutions lack the robustness to protect IoFT networks from sophisticated threats, particularly when a single hacked node can endanger the entire network's security. This paper proposed a blockchain-aided LLM-based context-aware IDS for IoFT networks. Using LLM with context-aware capabilities, the proposed framework systems can analyze real-time network traffic, adapt to evolving threat contexts, and efficiently identify zero-day attacks. The proposed model achieves (98.47% and 98.12%)  accuracy on different datasets, demonstrating the advantages of using LLM models in detecting cyber-attacks. Furthermore, detecting threats is only one aspect of the proposed framework; effective mitigation is equally essential. A real-time incident response system is designed to address this, employing blockchain-aided smart contracts to mitigate detected attacks effectively. The proposed framework combines the dynamic learning of LLMs with the blockchain's decentralized and immutable nature to enhance the detection of unknown threats and ensure accurate identification and response.

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In conjunction with current deep learning networks, the potential for power anomaly detection in manufacturing systems has been turned into a realized task that serves the process efficiently and reliably. This paper proposes an ensemble model developed for anomaly detection within a manufacturing environment employing six convolutional neural network architectures: DenseNet121, EfficientNetB0, InceptionV3, ResNet50, VGG16, and Xception, over two datasets representing structural and logical anomalies, MVTec LOCO AD and MVTec AD. The proposed ensemble model can handle various issues associated with anomaly detection, which include class imbalance, poor variability of images, and model overfitting. A large dose of intensive data augmentation techniques and the use of Gaussian noise in a way that gives an overwhelming boost to the generalization of the models will be passed over. The models underwent multiple training iterations to optimize testing accuracy. The ensemble technique was employed to aggregate the results, culminating in the highest accuracy, precision, recall, and F1 scores achieved in the experiment. This approach demonstrated promising performance and increased model robustness and reliability. Additionally, it achieved approximately 98.15 and 97.56 AUROC on the MVTec LOCO AD and MVTec AD datasets, respectively.

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Marine tactical networks (MTNs) are essential for the safety and security of marine operations. Due to their sensitive nature, MTNs are susceptible to severe cyber risks, such as unauthorized access, data breaches, intrusion attacks. Traditional Intrusion Detection Systems (IDS) frequently have difficulties in effectively countering cyber threats in MTNs due to their static characteristics and restricted flexibility to changing threat environments. Thus, an IDS is vital for safeguarding MTNs from these vulnerabilities by proactively detecting and addressing threats. This paper proposes a novel blockchain-aided IDS for MTNs, leveraging reinforcement learning (RL) techniques. The proposed system aims to enhance the security and reliability of critical marine operations by effectively detecting and mitigating cyber threats. By integrating blockchain technology, the system ensures the integrity and immutability of intrusion detection data, preventing tampering and ensuring accountability. RL algorithms are employed to dynamically adapt the IDS's detection capabilities to evolving threat landscapes, improving its effectiveness and responsiveness. The key innovation of this proposed framework is integrating multi-agent adversarial RL, which enhances the model’s robustness and enables it to effectively identify and classify complex and dynamic threats within the MTNs. Experimental results demonstrate the superior performance of the proposed system in terms of detection accuracy, false positive rate, and computational efficiency compared to traditional IDS approaches. The proposed framework weighted accuracy of 80.16% and 95.9% for NSL-KDD and AWID datasets, respectively. This shows the robustness and applicability of the proposed model in complex MTN environments.

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The advancement of intelligent transport systems and the emergence of autonomous cars have the potential to reduce road accidents significantly. However, mountainous regions, like South Korea, are especially vulnerable to the threats of black ice formation in winter. Due to its near-invisibility and sudden formation, this translucent layer of ice is challenging to detect. It poses a severe risk to both human and autonomous drivers. Conventional approaches to monitoring road conditions frequently struggle to swiftly identify black ice, highlighting the need to advance more sophisticated sensing systems. This project aims to identify road surface conditions, including black ice, by integrating images and audio with sensor data, such as surface and ambient temperatures. The system utilizes convolutional neural networks (CNNs)-based multimodal system to analyze visual and acoustic data to detect road surface conditions.  The data was gathered over two years from a testbed set up in three separate terrains in South Korea. Subsequently, drivers are promptly notified of potential dangers on the road, thereby reducing the probability of accidents resulting from hazardous conditions.  The evaluation results indicate the efficacy of the suggested method, as the multimodal system achieves a 97\% accuracy rate in detecting black ice. The multimodal fusion system overcomes individual techniques' limitations, providing dependable early warnings for black ice and improving road safety.

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The immersive metaverse environment offers distinct social interactions and opportunities, yet it also presents significant challenges in securely managing misbehavior, including hate speech, bullying, and harassment. Existing solutions primarily focus on detecting such behavior through artificial intelligence but lack robust mechanisms for management and governance. This gap is critical as the metaverse continues to mirror complex real-world interactions and centralized authority systems prove vulnerable to compromise. Our research introduces a novel framework, Meta-Governance, which not only detects but also effectively manages and governs user behavior through smart contracts, ensuring a secure, fair, and transparent metaverse environment. The system incorporates behavior monitoring to identify and condemn inappropriate behavior, specifically targeting problems such as hate speech and cyberbullying. Occurrences of misbehavior are permanently preserved on the blockchain to ensure the capacity to trace and bear accountability. In this article, we deploy a Natural Language Processing (NLP) model and a smart contract-based framework to address unusual behavior monitoring, access control, and credit scoring. Deep learning models are used to identify and classify linguistic patterns that may be considered hazardous. Blockchain technology addresses virtual misconduct using smart contracts, while a distinctive credit scoring mechanism ensures that users are held responsible for making disrespectful statements. The efficacy of the proposed smart contract is comprehensively evaluated within the context of a private Hyperledger Besu system. The integration of AI and blockchain may greatly improve the security and inclusiveness of the metaverse, highlighting the crucial role of these technologies in combating hate speech and enhancing user engagement.

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The worldwide education landscape has transformed due to the COVID-19 pandemic, providing an opportunity to enhance virtual learning. Nevertheless, this transition has disadvantages, including less face-to-face interaction and the possibility of student seclusion. The metaverse, a digital realm, provides an optimal substitute for conventional virtual education. Nevertheless, its immersive nature poses issues for conventional digital learning management systems. This article proposes using blockchain technology and non-fungible tokens (NFTs) as unique identifiers to handle credentials and transactions in the metaverse while ensuring security effectively. This approach tackles difficulties associated with the administration of certificates, including issuing, maintaining records, ensuring traceability, and preserving integrity. Additionally, it establishes a fairly secure setting that effectively prevents data manipulation. The proposed approach entails the implementation of a smart contract that integrates a credential's transactions and an attribute-based access control mechanism. The research further addresses the challenge of limited storage resources caused by the dynamic nature of the metaverse and the requirement to manage huge quantities of data. The implementation results and applicability assessment confirm the feasibility of the proposed approach. Eventually, this study contributes to the ongoing discussion on the convergence of blockchain, NFTs, and smart contracts with its new perspective on digital learning management in the metaverse platform.

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Unmanned aerial vehicles (UAVs) have recently sparked attention in the logistics supply chain throughout the period. However, using UAVs in logistics has challenges related to security, data integrity, and trustworthiness in the healthcare industry. To address these issues, this paper introduces a blockchain-integrated scheme to improve the security and trustworthiness of medical supply delivery by leveraging UAVs. Blockchain, a distributed ledger system, enables transparent and reliable participant interactions. The proposed approach employs a sophisticated two-phase verification mechanism to tackle security concerns. The initial phase employs $\alpha-hash$ bloom filter to verify the integrity of data and identify any unauthorized senders. In the second step, digital signature methods authenticate the sender's identity, guaranteeing the data's integrity and prohibiting unauthorized entry. Smart contracts ensure transactions maintain trustworthiness, data integrity, and security without alteration. The InterPlanetary File System (IPFS) protocol addresses storage cost issues. Introducing the 5G-enabled Tactile Internet (TI) enhances communication by being more responsive and efficient. The proposed approach creates a credit scoring mechanism for UAV role selection to improve the delivery process swiftly. The scheme surpasses traditional systems in key performance metrics, and comparing the model's validity with several existing schemes illustrates the proposed scheme's efficacy and feasibility. This scheme could efficiently tackle challenges in providing unmanned medical supplies in smart healthcare systems using blockchain, smart contracts, IPFS, and TI.

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With the annual rise in the number of creative startup enterprises in the Manufacturing Execution System (MES), there is
a rising need for research on the job-shop scheduling problem(JSSP). JSSP, a complex NP-hard issue, aims to optimize
allocating scarce resources to improve production efficiency. JSS is employed in several sectors, such as warehouse order
packaging and manufacturing on production lines. During practical circumstances, the operating context might become
Complicated owing to dynamic events such as the delivery of tasks at different times, delays in job completion, machine
malfunctions, or unexpected incidents. A lot of scheduling heuristics, including dispatching rules, have been used to
make good schedules by effectively prioritizing candidates, like manufacturing machines. Recently, Artificial Intelligence
(AI), specifically Deep Reinforcement Learning (DRL), has proven advantageous in autonomously acquiring scheduling
heuristics for JSS, mostly because of its versatility, effectiveness, and efficiency. Regrettably, there is an insufficient
number of surveys accessible to analyze and evaluate the benefits and drawbacks of these active initiatives. This work
seeks to address this deficiency by undertaking a thorough investigation of Bio-inspired heuristic algorithms and different
types of AI algorithms for JSS. This article has provided a concise overview of the utilized learning models, primary
objectives, datasets, and performance metrics. The outcomes are presented in tabular format, which enables a clear and
concise exhibition of the discoveries. In addition, this article analyzes certain issues and hurdles to recommend specific
areas that may be targeted for the future advancement of autonomous scheduling heuristic design.

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Deep learning (DL)-based automatic modulation classification (AMC) is a primary research field for identifying modulation types. However, traditional DL-based AMC approaches rely on hand-crafted features, which can be time consuming and may not capture all relevant information in the signal. Additionally, they are centralized solutions that are trained on large amounts of data acquired from local clients and stored on a server, leading to weak performance in terms of correct classification probability. To address these issues, a federated learning (FL)-based AMC approach is proposed, called FL-MP-CNN-AMC, which takes into account the effects of multipath channels (reflected and scattered paths) and considers the use of a modified loss function for solving the class imbalance problem caused by these channels. In addition, hyper parameter tuning and optimization of the loss function are discussed and analyzed to improve the performance of the proposed approach. The classification performance is investigated by considering the effects of interference level, delay spread, scattered and reflected paths, phase offset, and frequency offset. The simulation results show that the proposed approach provides  excellent performance in terms of correct classification probability, confusion matrix, convergence and communication overhead when compared to contemporary methods.

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The rapid integration of the Internet of Artificial Intelligence and Internet of Things (AI-IoT) technologies has given rise to a pivotal element of the upcoming digital era, the Metaverse. This confluence has significantly impacted virtual learning platforms by introducing enhanced, immersive, and interactive environments for learners, educators, and institutions. However, the growing reliance on Metaverse necessitates robust cybersecurity measures to detect and mitigate cyber threats and ensure the safety of users. This paper proposes an explainable deep neural network (DNN) designed to identify and address network intrusion attacks within Metaverse learning environments. By leveraging the latest cybersecurity IoT datasets and employing an effective feature selection method, this study provides a visually interpretable, trustworthy, and quantitative explanation of the network intrusion detection system (NIDS) model using Shapley Additive exPlanations  (SHAP) and local interpretable model-agnostic explanation (LIME) explainability methods. The adopted explainable DNN, capable of processing network traffic features from interconnected Metaverse devices and IoT sensors, can facilitate accurate and comprehensible remediation of anomalous from benign activities within Metaverse. The NIDS model yields a high performing accuracy of 99.9%  for establishing a more secure and trustworthy metaverse learning environment. https://www.sciencedirect.com/science/article/pii/S2542660523003694

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Over the last few decades, technology has been improving dramatically and consequently transformed the standard of living and socio-economic conditions. The entire process will revolutionize when the next advanced technologies will be fully functional. Advanced technologies like the metaverse, Web 3.0, and others necessitate high computing power, invincible security, and ultra-fast internet. Despite increasing demand, traditional computing methods have limitations and are not capable of satisfying the requirements. To solve these tribulations, quantum computing is shining a light of hope. This survey aims to analyze the methodology, constraints, and potential of integrating quantum computing with the metaverse. We begin with an overview of quantum computing and related terms. We then investigate the feasibility of applying quantum-enabled technologies to enhance the metaverse. Furthermore, this survey also considers middleware for seamless conversion between traditional and quantum computing with the metaverse. In the subsequent phase of this survey, our objective is to discern and delineate the prospective application domains for the quantum-enabled metaverse. In essence, the difficulties of integrating quantum computing with the metaverse, present research approaches, and open research issues with consequences for additional in-depth investigations are highlighted.

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The heterogeneous connections in metaverse environments pose vulnerabilities to cyber-attacks. To prevent and mitigate malicious network activities in a distributed metaverse, conventional intrusion detection systems (IDS) have communication overhead and privacy concerns. Federated learning (FL) techniques are widely employed to develop IDS frameworks and enable privacy-preserving collaborative learning schemes in decentralized ecosystems. However, the vanilla FL system utilizes a centralized FL aggregation technique, which introduces a single point of failure (SPoF) and potential unauthorized aggregators, allowing malicious clients to inject false data parameters, known as poisoning attacks. Furthermore, low-quality clients in the FL system can result in degraded model performance and hinder convergence. This study proposes a secure and reliable blockchain-aided federated learning (BFL)-based IDS framework using a lightweight model for securing metaverse. An authorized federated IDS is proposed to establish a trustworthy decentralized aggregation mechanism, utilizing proof-of-authority (PoA) consensus. The proposed federated IDS implemented a hybrid client selection (HCS) technique, considering the accuracy and reputation of client histories, to select high-quality metaverse edge devices. Additionally, a fairness ERC-20 token-based incentive mechanism was developed to reward selected FL clients as a token of appreciation for their contribution to the FL training processes. According to the IDS framework measurements, the proposed model performs better than the existing approaches for detecting cyber-attacks in metaverse environments, achieving an accuracy of 99.28% with trainable parameters of 1.8K and mega floating-point operations (MFLOPs) of 0.0016.

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#	Title	Status	Type	By
50	Sparsely Connected Low Complexity CNN for Unmanned Vehicles Detection - Sensing RF Signal IEEE Transactions on Vehicular Technology	Published	International	Esmot Ara
49	BELL-IDS: Blockchain-Empowered Large Language Model-Aided Context-Aware IDS in IoFT IEEE Internet of Things	Submitted	International	Mohtasin
48	Advanced Anomaly Detection in Manufacturing System: An Optimized Multi-CNN Ensemble Learning Approach IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE	Under Review	International	Md Mahinur
47	Blockchain-Aided Intrusion Detection in Marine Tactical Network Using Reinforcement Learning IEEE Transactions on Network and Service Management	Under Review	International	Md Raihan
46	BlackIceNet: Explainable AI-Enhanced Multimodal for Black Ice Detection to Prevent Accident in Intelligent Vehicles IEEE Internet of Things	Under Review	International	Mohtasin
45	Blockchain Private Key Storage System for IoT device The Journal of Korean Institute of Communications and Information Science	Accepted	Domestic	Odinachi
44	Blockchain Private Key Storage System for IoT device The Journal of Korean Institute of Communications and Information Science	Accepted	Domestic	Odinachi
43	DroneGuard: An Explainable and Efficient Machine Learning Framework for Intrusion Detection in Drone Networks IEEE Internet of Things	Under Review	International	Vivian
42	QFed: A Secure Federated Learning Framework for IoMT Using Quantum Cryptography IEEE Journal of Biomedical and Health Informatics	Under Review	International	Vivian
41	Blockchain-Aided Intrusion Detection in Marine Tactical Network Using Reinforcement Learning IEEE Transactions on Network and Service Management	Preparation	International	Md Raihan
40	Enhanced Anomaly Detection with Optimized Deep Learning in Manufacturing System ICT Express	Under Review	International	Md Raihan
39	A Human-Centric Interactive Avatar Creation Framework using Motion Capture and Face Expression Detection in Metaverse The Journal of Korean Institute of Communications and Information Sciences	Accepted	Domestic	Esmot Ara
38	Pure Loyalty: Efficient and Secure Framework for NFT Loyalty Programs The Journal of Korean Institute of Communications and Information Science	Published	Domestic	Gifar Arif
37	Meta-Governance: Blockchain-Driven Metaverse Platform for Mitigating Misbehavior Using Smart Contract and AI IEEE Transactions on Network and Service Management	Published	International	Mohtasin
36	Explainable Data-driven Digital Twins for Predicting Battery States in Electric Vehicles IEEE Access	Published	International	Judith
35	ASR-Fed: Agnostic Straggler-Resilient Semi-Asynchronous Federated Learning Technique for Secured Drone Network Journal of Machine Learning and Cybernetics	Published	International	Vivian
34	Evaluating Regression Techniques for Service Advisor Performance Analysis in Automotive Dealerships Journal of Retailing and Consumer Services	Published	International	Judith
33	Leveraging Quantum Blockchain for Secure Multiparty Space Sharing and Authentication on Specialized Metaverse Platform Nature Scientific Reports	Accepted	International	Esmot Ara
32	Meta-Learning: A Digital Learning Management Framework using Blockchain for Metaverses IEEE Access	Accepted	International	Mohtasin
31	BUS-Health: Blockchain and UAV Integrated Smart Medical Supplies Blockchain: Research and Applications	Minor Revision	International	Mohtasin
30	AI-Based Job Shop Scheduling in Manufacturing Execution System: A Review Perspective Elsevier computers & industrial engineering	Submitted	International	Md Javed Ahmed
29	Enhancing Security and Accountability in Autonomous Vehicles through Robust Speaker Identification and Blockchain-Based Event Recording MDPI Electronics	Published	International	Judith
28	Federated Learning Based Modulation Classification for Multipath Channels Parallel Computing	Published	International	Sanjay
27	RF-RSCV: Robust Security Framework Against Intrusions in Internet of Drone Communication Networks %Securing the Drone Network Based On Hierarchical Intrusion Detection System Using Efficient and Optimized Random-Forest Model Journal Of Communications and Networks	Minor Revision	International	Vivian
26	ASR-Fed: Agnostic Straggler-Resilient Semi-Asynchronous Federated Learning Technique for Secured Drone Network International Journal of Machine Learning and Cybernetics	Minor Revision	International	Vivian
25	MetaWatch: Trends, Challenges, and Future Directions for Intrusion Detection in the Metaverse IEEE Internet of Things	Under Review	International	Ebuka
24	Zero-Trust Marine Cyberdefense for IoT-Based Communications: An Explainable Approach Electronics	Published	International	Ebuka
23	Detecting Cyberthreats in Metaverse Learning Platforms using an Explainable DNN Internet of Things	Published	International	Ebuka
22	Integration of Quantum Technologies into Metaverse: Applications, Potentials, and Challenges IEEE Access	Published	International
21	Whale optimization-based PTS scheme for PAPR reduction in UFMC systems IET Communications	Published	International
20	Tamper-Resistant Collaborative Attack Detection using Efficient Blockchain for Securing IIoT IEEE Transactions on Industrial Informatics	Under Review	International	Ahmad
19	Dragonfly Interaction Algorithm for Optimization of Queuing Delay in Industrial Wireless Networks, 10.1007/s42235-023-00462-7 Bionic Engineering	Published	International	Sanjay
18	IoMT-Net: Blockchain Integrated Unauthorized UAV Localization Using Lightweight Convolution Neural Network for Internet of Military Things IEEE Internet Things of Journal	Accepted	International
17	Droneilliance and detection dynamics: A review of radar techniques and trends None	Preparation	International
16	Sanjay Bhardwaj, Da-Hye Kim and Dong-Seong Kim,” Federated Learning Based Resource Allocation for V2X Communications”. Transactions on Intelligent Transportation Systems	Minor Revision	International	Sanjay
15	Enhancing Reliability and Energy Efficiency for Industrial IoT using IRSA-PSO The Journal of Korean Institute of Communications and Information Sciences (J-KICS)	Published	Domestic	Love Allen Chijioke
14	Paul Michael Custodio, Made Adi Paramartha Putra, Jae-Min Lee, and Dong-Seong Kim, "TLFed: Secure Smart Grid Transmission Line Fault Detection and Classification using Federated Learning", IEEE Transactions on Power Delivery, 2023 (Under Review) None	Preparation	International
13	PureRx: An NFT-Prescription Management for Efficient and Secure Healthcare System IEEE Transactions on Big Data Journal	Minor Revision	International	Gifar Arif
12	Collaborative Edge Learning-Assisted Interactive Avatar Generation for Metaverse in B5G Networks IEEE Internet of Things Journal	Under Review	International	Ahmad
11	XAI with Adversarial Defence for Zero-Trust Metaverse DDoS Attack Detection IEEE Transactions on Dependable and Secure Computing	Under Review	International	Love Allen Chijioke
10	PureFed: An Efficient Collaborative and Trustworthy Blockchain-Assisted Federated Learning Framework with Dynamic Aggregation Internet of Things, Elsevier	Under Review	International	Ahmad
9	Bandit Learning based Distributed Computation Offloading in Fog Computing Networks: A survey IEEE Access	Accepted	International	Love Allen Chijioke
8	Blockchain-Inspired Collaborative Cyber-Attacks Detection for Securing Metaverse IEEE Internet of Things Journal	Published	International	Ahmad
7	AI Model Stability in Industrial IoT Intrusion Detection: Leveraging the Characteristics Stability The Journal of Korean Institute of Communications and Information Sciences (J-KICS)	Published	Domestic	Love Allen Chijioke
6	Federated Learning Inspired Low-Complexity Intrusion Detection and Classification Technique for SDN-based Industrial CPS IEEE Transactions on Network and Service Management	Published	International	Ahmad
5	Goodness Oluchi Anyanwu, Cosmas Ifeanyi Nwakanma, Jae-Min Lee, Dong-Seong Kim, Optimization of RBF-SVM Kernel Using Grid Search Algorithm for DDoS Attack Detection in SDN-Based VANET," in IEEE Internet of Things Journal, vol. 10, no. 10, pp. 8477-8490, 15 May15, 2023, doi: 10.1109/JIOT.2022.3199712. None	Accepted	International
4	TSFed: A Three-Stage Optimization Mechanism for Secure and Efficient Federated Learning in Industrial IoT Networks IEEE Transactions on Industrial Informatics	Under Review	International	Ahmad
3	Blockchain-Empowered Federated Human Activity Recognition for Creating Interactive Consumer Avatar in Metaverse Platform IEEE Transactions on Customer Electronics	Preparation	International	Ahmad
2	MAD-DDS: Memory-efficient Automatic Discovery Data Distribution Service for Large-Scale Distributed Control Network IET Communications	Published	International	Love Allen Chijioke
1	Machine Learning Explainability for Intrusion Detection in the Industrial Internet of Things IEEE Internet of Things Magazine	Under Review	International	Love Allen Chijioke

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