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Vulnerability modelling for hybrid industrial control system networks

  • Authors: Ur-Rehman, Attiq , Gondal, Iqbal , Kamruzzaman, Joarder , Jolfaei, Alireza
  • Date: 2020
  • Type: Text , Journal article
  • Relation: Journal of Grid Computing Vol. 18, no. 4 (2020), p. 863-878
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  • Description: With the emergence of internet-based devices, the traditional industrial control system (ICS) networks have evolved to co-exist with the conventional IT and internet enabled IoT networks, hence facing various security challenges. The IT industry around the world has widely adopted the common vulnerability scoring system (CVSS) as an industry standard to numerically evaluate the vulnerabilities in software systems. This mathematical score of vulnerabilities is combined with environmental knowledge to determine the vulnerable nodes and attack paths. IoT and ICS systems have unique dynamics and specific functionality as compared to traditional computer networks, and therefore, the legacy cyber security models would not fit these advanced networks. In this paper, we studied the CVSS v3.1 framework’s application to ICS embedded networks and an improved vulnerability framework, named CVSSIoT-ICS, is proposed. CVSSIoT-ICS and CVSS v3.1 are applied to a realistic supply chain hybrid network which consists of IT, IoT, and ICS nodes. This hybrid network is assigned with actual vulnerabilities listed in the national vulnerability database (NVD). The comparison results confirm the effectiveness of CVSSIoT-ICS framework as it is equally applicable to all nodes of a hybrid network and evaluates the vulnerabilities based on the distinct features of each node type. © 2020, Springer Nature B.V.

Cyberattacks detection in iot-based smart city applications using machine learning techniques

  • Authors: Rashid, Md Mamunur , Kamruzzaman, Joarder , Hassan, Mohammad , Imam, Tassadduq , Gordon, Steven
  • Date: 2020
  • Type: Text , Journal article
  • Relation: International Journal of Environmental Research and Public Health Vol. 17, no. 24 (2020), p. 1-21
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  • Description: In recent years, the widespread deployment of the Internet of Things (IoT) applications has contributed to the development of smart cities. A smart city utilizes IoT-enabled technologies, communications and applications to maximize operational efficiency and enhance both the service providers’ quality of services and people’s wellbeing and quality of life. With the growth of smart city networks, however, comes the increased risk of cybersecurity threats and attacks. IoT devices within a smart city network are connected to sensors linked to large cloud servers and are exposed to malicious attacks and threats. Thus, it is important to devise approaches to prevent such attacks and protect IoT devices from failure. In this paper, we explore an attack and anomaly detection technique based on machine learning algorithms (LR, SVM, DT, RF, ANN and KNN) to defend against and mitigate IoT cybersecurity threats in a smart city. Contrary to existing works that have focused on single classifiers, we also explore ensemble methods such as bagging, boosting and stacking to enhance the performance of the detection system. Additionally, we consider an integration of feature selection, cross-validation and multi-class classification for the discussed domain, which has not been well considered in the existing literature. Experimental results with the recent attack dataset demonstrate that the proposed technique can effectively identify cyberattacks and the stacking ensemble model outperforms comparable models in terms of accuracy, precision, recall and F1-Score, implying the promise of stacking in this domain. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.

Malware detection in edge devices with fuzzy oversampling and dynamic class weighting

  • Authors: Khoda, Mahbub , Kamruzzaman, Joarder , Gondal, Iqbal , Imam, Tasadduq , Rahman, Ashfaqur
  • Date: 2021
  • Type: Text , Journal article
  • Relation: Applied Soft Computing Vol. 112, no. (2021), p.
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  • Description: In Internet-of-things (IoT) domain, edge devices are used increasingly for data accumulation, preprocessing, and analytics. Intelligent integration of edge devices with Artificial Intelligence (AI) facilitates real-time analysis and decision making. However, these devices simultaneously provide additional attack opportunities for malware developers, potentially leading to information and financial loss. Machine learning approaches can detect such attacks but their performance degrades when benign samples substantially outnumber malware samples in training data. Existing approaches for such imbalanced data assume samples represented as continuous features and thus can generate invalid samples when malware applications are represented by binary features. We propose a novel malware oversampling technique that addresses this issue. Further, we propose two approaches for malware detection. Our first approach uses fuzzy set theory, while the second approach dynamically assigns higher priority to malware samples using a novel loss function. Combining our oversampling technique with these approaches, the proposed approach attains over 9% improvement over competing methods in terms of F1_score. Our approaches can, therefore, result in enhanced privacy and security in edge computing services. © 2021 Elsevier B.V.

A tree-based stacking ensemble technique with feature selection for network intrusion detection

  • Authors: Rashid, Mamanur , Kamruzzaman, Joarder , Imam, Tasadduq , Wibowo, Santoso , Gordon, Steven
  • Date: 2022
  • Type: Text , Journal article
  • Relation: Applied Intelligence Vol. 52, no. 9 (2022), p. 9768-9781
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  • Description: Several studies have used machine learning algorithms to develop intrusion systems (IDS), which differentiate anomalous behaviours from the normal activities of network systems. Due to the ease of automated data collection and subsequently an increased size of collected data on network traffic and activities, the complexity of intrusion analysis is increasing exponentially. A particular issue, due to statistical and computation limitations, a single classifier may not perform well for large scale data as existent in modern IDS contexts. Ensemble methods have been explored in literature in such big data contexts. Although more complicated and requiring additional computation, literature has a note that ensemble methods can result in better accuracy than single classifiers in different large scale data classification contexts, and it is interesting to explore how ensemble approaches can perform in IDS. In this research, we introduce a tree-based stacking ensemble technique (SET) and test the effectiveness of the proposed model on two intrusion datasets (NSL-KDD and UNSW-NB15). We further enhance incorporate feature selection techniques to select the best relevant features with the proposed SET. A comprehensive performance analysis shows that our proposed model can better identify the normal and anomaly traffic in network than other existing IDS models. This implies the potentials of our proposed system for cybersecurity in Internet of Things (IoT) and large scale networks. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Assessing trust level of a driverless car using deep learning

  • Authors: Karmakar, Gour , Chowdhury, Abdullahi , Das, Rajkumar , Kamruzzaman, Joarder , Islam, Syed
  • Date: 2021
  • Type: Text , Journal article
  • Relation: IEEE Transactions on Intelligent Transportation Systems Vol. 22, no. 7 (2021), p. 4457-4466
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  • Description: The increasing adoption of driverless cars already providing a shift to move away from traditional transportation systems to automated ones in many industrial and commercial applications. Recent research has justified that driverless vehicles will considerably reduce traffic congestions, accidents, carbon emissions, and enhance the accessibility of driving to wider cross-section of people and lifestyle choices. However, at present, people's main concerns are about its privacy and security. Since traditional protocol layers based security mechanisms are not so effective for a distributed system, trust value-based security mechanisms, a type of pervasive security, are appearing as popular and promising techniques. A few statistical non-learning based models for measuring the trust level of a driverless are available in the current literature. These are not so effective because of not being able to capture the extremely distributed, dynamic, and complex nature of the traffic systems. To bridge this research gap, in this paper, for the first time, we propose two deep learning-based models that measure the trustworthiness of a driverless car and its major On-Board Unit (OBU) components. The second model also determines its OBU components that were breached during the driving operation. Results produced using real and simulated traffic data demonstrate that our proposed DNN based deep learning models outperform other machine learning models in assessing the trustworthiness of individual car as well as its OBU components. The average precision of detection accuracies for the car, LiDAR, camera, and radar are 0.99, 0.96, 0.81, and 0.83, respectively, which indicates the potential real-life application of our models in assessing the trust level of a driverless car. © 2000-2011 IEEE.

Mobile malware detection with imbalanced data using a novel synthetic oversampling strategy and deep learning

API based discrimination of ransomware and benign cryptographic programs

Sensitivity analysis for vulnerability mitigation in hybrid networks

  • Authors: Ur‐rehman, Attiq , Gondal, Iqbal , Kamruzzaman, Joarder , Jolfaei, Alireza
  • Date: 2022
  • Type: Text , Journal article
  • Relation: Electronics (Switzerland) Vol. 11, no. 2 (2022), p.
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  • Description: The development of cyber‐assured systems is a challenging task, particularly due to the cost and complexities associated with the modern hybrid networks architectures, as well as the recent advancements in cloud computing. For this reason, the early detection of vulnerabilities and threat strategies are vital for minimising the risks for enterprise networks configured with a variety of node types, which are called hybrid networks. Existing vulnerability assessment techniques are unable to exhaustively analyse all vulnerabilities in modern dynamic IT networks, which utilise a wide range of IoT and industrial control devices (ICS). This could lead to having a less optimal risk evaluation. In this paper, we present a novel framework to analyse the mitigation strategies for a variety of nodes, including traditional IT systems and their dependability on IoT devices, as well as industrial control systems. The framework adopts avoid, reduce, and manage as its core principles in characterising mitigation strategies. Our results confirmed the effectiveness of our mitigation strategy framework, which took node types, their criticality, and the network topology into account. Our results showed that our proposed framework was highly effective at reducing the risks in dynamic and resource constraint environments, in contrast to the existing techniques in the literature. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

Assessing reliability of smart grid against cyberattacks using stability index

  • Authors: Rashed, Muhammad , Gondal, Iqbal , Kamruzzaman, Joarder , Islam, Syed
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 31st Australasian Universities Power Engineering Conference, AUPEC 2021, Virtual, Online 26 to 30 September 2021, Proceedings of 2021 31st Australasian Universities Power Engineering Conference, AUPEC 2021
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  • Description: The degradation of stability index within smart grid leads to incorrect power generation and poor load balancing. The remote data dependency of the central energy management system (CEMS) causes communication delay that further leads to poor synchronization within the system. This becomes worse in the presence of cyber-attacks such as stealth or false data injection attack (FDIA). We used dynamic estimation to obtain state data after the inception of false data attack and analyzed its impact on the stability index of the smart grid. A lookup table was constructed based on the fluctuations within the voltage estimates of IEEE-Bus system. An index number was assigned to output estimates at the bus that highlights the level of severity within the grid. We used IEEE-57 Bus using MATLAB to capture and plot the results related to voltage estimates, latency, and inception time delay. The results demonstrate a clear relationship between stability index and state estimates especially when the system is under the influence of a cyber-attack. © 2021 IEEE.

Attacks on self-driving cars and their countermeasures : a survey

  • Authors: Chowdhury, Abdullahi , Karmakar, Gour , Kamruzzaman, Joarder , Jolfaei, Alireza , Das, Rajkumar
  • Date: 2020
  • Type: Text , Journal article , Review
  • Relation: IEEE Access Vol. 8, no. (2020), p. 207308-207342
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  • Description: Intelligent Traffic Systems (ITS) are currently evolving in the form of a cooperative ITS or connected vehicles. Both forms use the data communications between Vehicle-To-Vehicle (V2V), Vehicle-To-Infrastructure (V2I/I2V) and other on-road entities, and are accelerating the adoption of self-driving cars. The development of cyber-physical systems containing advanced sensors, sub-systems, and smart driving assistance applications over the past decade is equipping unmanned aerial and road vehicles with autonomous decision-making capabilities. The level of autonomy depends upon the make-up and degree of sensor sophistication and the vehicle's operational applications. As a result, self-driving cars are being compromised perceived as a serious threat. Therefore, analyzing the threats and attacks on self-driving cars and ITSs, and their corresponding countermeasures to reduce those threats and attacks are needed. For this reason, some survey papers compiling potential attacks on VANETs, ITSs and self-driving cars, and their detection mechanisms are available in the current literature. However, up to our knowledge, they have not covered the real attacks already happened in self-driving cars. To bridge this research gap, in this paper, we analyze the attacks that already targeted self-driving cars and extensively present potential cyber-Attacks and their impacts on those cars along with their vulnerabilities. For recently reported attacks, we describe the possible mitigation strategies taken by the manufacturers and governments. This survey includes recent works on how a self-driving car can ensure resilient operation even under ongoing cyber-Attack. We also provide further research directions to improve the security issues associated with self-driving cars. © 2013 IEEE.

Remote reconfiguration of FPGA-based wireless sensor nodes for flexible Internet of Things

  • Authors: Aziz, Syed , Hoskin, Dylan , Pham, Duc , Kamruzzaman, Joarder
  • Date: 2022
  • Type: Text , Journal article
  • Relation: Computers and Electrical Engineering Vol. 100, no. (2022), p.
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  • Description: Recently, sensor nodes in Wireless Sensor Networks (WSNs) have been using Field Programmable Gate Arrays (FPGA) for high-speed, low-power processing and reconfigurability. Reconfigurability enables adaptation of functionality and performance to changing requirements. This paper presents an efficient architecture for full remote reconfiguration of FPGA-based wireless sensors. The novelty of the work includes the ability to wirelessly upload new configuration bitstreams to remote sensor nodes using a protocol developed to provide full remote access to the flash memory of the sensor nodes. Results show that the FPGA can be remotely reconfigured in 1.35 s using a bitstream stored in the flash memory. The proposed scheme uses negligible amount of FPGA logic and does not require a dedicated microcontroller or softcore processor. It can help develop truly flexible IoT, where the FPGAs on thousands of sensor nodes can be reprogrammed or new configuration bitstreams uploaded without requiring physical access to the nodes. © 2022

Adversarial training for deep learning-based cyberattack detection in IoT-based smart city applications

  • Authors: Rashid, Md Mamunur , Kamruzzaman, Joarder , Mehedi Hassan, Mohammad , Imam, Tasadduq , Wibowo, Santoso , Gordon, Steven , Fortino, Giancarlo
  • Date: 2022
  • Type: Text , Journal article
  • Relation: Computers and Security Vol. 120, no. (2022), p.
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  • Description: Intrusion Detection Systems (IDS) based on deep learning models can identify and mitigate cyberattacks in IoT applications in a resilient and systematic manner. These models, which support the IDS's decision, could be vulnerable to a cyberattack known as adversarial attack. In this type of attack, attackers create adversarial samples by introducing small perturbations to attack samples to trick a trained model into misclassifying them as benign applications. These attacks can cause substantial damage to IoT-based smart city models in terms of device malfunction, data leakage, operational outage and financial loss. To our knowledge, the impact of and defence against adversarial attacks on IDS models in relation to smart city applications have not been investigated yet. To address this research gap, in this work, we explore the effect of adversarial attacks on the deep learning and shallow machine learning models by using a recent IoT dataset and propose a method using adversarial retraining that can significantly improve IDS performance when confronting adversarial attacks. Simulation results demonstrate that the presence of adversarial samples deteriorates the detection accuracy significantly by above 70% while our proposed model can deliver detection accuracy above 99% against all types of attacks including adversarial attacks. This makes an IDS robust in protecting IoT-based smart city services. © 2022 Elsevier Ltd

Churn prediction in telecom industry using machine learning ensembles with class balancing

  • Authors: Chowdhury, Abdullahi , Kaisar, Shahriar , Rashid, Md Mamunur , Shafin, Sakib , Kamruzzaman, Joarder
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2021, Brisbane, 8-10 December 2021
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  • Description: Telecommunication service providers are going through a very competitive and challenging time to retain existing customers by offering new and attractive services (e.g., unlimited local and international calls, high-speed internet, new phones). It is therefore imperative to analyse and predict customer churn behaviour more accurately. One of the major challenges to analyse churn data and build better prediction model is the imbalance nature of the data. Customer behaviour for churn and non-churn scenarios may contain resembling features. Using a single classifier or simple oversampling method to handle data imbalance often struggles to identify the minority (churn) class data. To overcome the issue, we introduce a model that uses sophisticated oversampling technique in conjunction with ensemble methods, namely Random Forest, Gradient Boost, Extreme Gradient Boost, and AdaBoost. The hyperparameters of the baseline ensemble methods and the oversampling methods were tuned in several ways to investigate their impact on prediction performances. Using a widely used publicly available customer churn dataset, prediction performance of the proposed model was evaluated in term of various metrics, namely, accuracy, precision, recall, F-1 score, AUC under ROC curve. Our model outperformed the existing models and significantly reduced both false positive and false negative prediction. © IEEE 2022.

False data detection in a clustered smart grid using unscented Kalman filter

  • Authors: Rashed, Muhammad , Kamruzzaman, Joarder , Gondal, Iqbal , Islam, Syed
  • Date: 2022
  • Type: Text , Journal article
  • Relation: IEEE Access Vol. 10, no. (2022), p. 78548-78556
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  • Description: The smart grid accessibility over the Internet of Things (IoT) is becoming attractive to electrical grid operators as it brings considerable operational and cost efficiencies. However, this in return creates significant cyber security challenges, such as fortification of state estimation data such as state variables against false data injection attacks (FDIAs). In this paper, a clustered partitioning state estimation (CPSE) technique is proposed to detect FDIA by using static state estimation, namely, weighted least square (WLS) method in conjunction with dynamic state estimation using minimum variance unscented Kalman filter (MV-UKF) which improves the accuracy of state estimation. The estimates acquired from the MV-UKF do not deviate like WLS as these are purely based on the previous iteration saved in the transition matrix. The deviation between the corresponding estimations of WLS and MV-UKF are utilised to partition the smart grid into smaller sub-systems to detect FDIA and then identify its location. To validate the proposed detection technique, FIDAs are injected into IEEE 14-bus, IEEE 30-bus, IEEE 118-bus, and IEEE 300-bus distribution feeder using MATPOWER simulation platform. Our results clearly demonstrate that the proposed technique can locate the attack area efficiently compared to other techniques such as chi square. © 2013 IEEE.

Vulnerability assessment framework for a smart grid

  • Authors: Rashed, Muhammad , Kamruzzaman, Joarder , Gondal, Iqbal , Islam, Syed
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 4th IEEE Global Power, Energy and Communication Conference, GPECOM 2022, Cappadocia, Turkey, 14-17 June 2022, Proceedings - 2022 IEEE 4th Global Power, Energy and Communication Conference, GPECOM 2022 p. 449-454
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  • Description: The increasing demand for the interconnected IoT based smart grid is facing threats from cyber-attacks due to inherent vulnerability in the smart grid network. There is a pressing need to evaluate and model these vulnerabilities in the network to avoid cascading failures in power systems. In this paper, we propose and evaluate a vulnerability assessment framework based on attack probability for the protection and security of a smart grid. Several factors were taken into consideration such as the probability of attack, propagation of attack from a parent node to child nodes, effectiveness of basic metering system, Kalman estimation and Advanced Metering Infrastructure (AMI). The IEEE-300 bus smart grid was simulated using MATPOWER to study the effectiveness of the proposed framework by injecting false data injection attacks (FDIA); and studying their propagation. Our results show that the use of severity assessment standards such as Common Vulnerability Scoring System (CVSS), AMI measurements and Kalman estimates were very effective for evaluating the vulnerability assessment of smart grid in the presence of FDIA attack scenarios. © 2022 IEEE.

Spam email categorization with nlp and using federated deep learning

  • Authors: Ul Haq, Ikram , Black, Paul , Gondal, Iqbal , Kamruzzaman, Joarder , Watters, Paul , Kayes, A.
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 18th International Conference on Advanced Data Mining and Applications, ADMA 2022, Brisbane, Australia, 28-30 November 2022, Advanced Data Mining and Applications, 18th International Conference, ADMA 2022 Vol. 13726 LNAI, p. 15-27
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  • Description: Emails are the most popular and efficient communication method that makes them vulnerable to misuse. Federated learning (FL) provides a decentralized machine learning (ML) model, where a central server coordinates clients that collaboratively train a shared ML model. This paper proposes Federated Phishing Filtering (FPF) technique based on federated learning, natural language processing, and deep learning. FL for intelligent algorithms fuses trained models of ML algorithms from multiple sites for collective learning. This approach improves ML performance by utilizing large collective training data sets across the corporate client base, resulting in higher phishing email detection accuracy. FPF techniques preserve email privacy using local feature extraction on client email servers. Thus, the contents of emails do not need to be transmitted across the network or stored on third-party servers. We have applied FL and Natural Language Processing (NLP) for email phishing detection. This technique provides four training modes that perform FL without sharing email content. Our research categorizes emails as benign, spam, and phishing. Empirical evaluations with publicly available datasets show that accuracy is improved by the use of our Federated Deep Learning model. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Modelling majority and expert influences on opinion formation in online social networks

  • Authors: Das, Rajkumar , Kamruzzaman, Joarder , Karmakar, Gour
  • Date: 2018
  • Type: Text , Journal article
  • Relation: World Wide Web Vol. 21, no. 3 (2018), p. 663-685
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  • Description: Two most important social influences that shape the opinion formation process are: (i) the majority influence caused by the existence of a large group of people sharing similar opinions and (ii) the expert influence originated from the presence of experts in a social group. When these two effects contradict each other in real life, they may pull the public opinions towards their respective directions. Existing models on opinion formation utilised the idea of expertise levels in conjunction with the expressed opinions of the agents to encapsulate the expert effect. However, they have disregarded the explicit consideration of the majority effect, and thereby failed to capture the concurrent and combined impact of these two influences on opinion evolution. To represent the majority and expert impacts, we explicitly use the concept of opinion consistency and expertise level consistency respectively in an innovative way by capitalizing the notion of entropy in measuring the homogeneity of a group. Consequently, our model successfully captures the opinion dynamics under the concomitant influence of majority and expert. We validate the efficacy of our model in capturing opinion dynamics in a real world scenario using the opinion evolution traces collected from a widely used online social network (OSN) platform. Moreover, simulation results reveal the impact of the aforementioned effects, and confirm that our model can properly capture the consensus, polarization and fragmentation properties of public opinion. Our model is also compared with some recent models to evaluate its performance in both real world and simulated environments. © 2017, Springer Science+Business Media, LLC.

Cancer classification utilizing voting classifier with ensemble feature selection method and transcriptomic data

  • Authors: Khatun, Rabea , Akter, Maksuda , Islam, Md Manowarul , Uddin, Md Ashraf , Talukder, Md Alamin , Kamruzzaman, Joarder , Azad, Akm , Paul, Bikash , Almoyad, Muhammad , Aryal, Sunil , Moni, Mohammad
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Genes Vol. 14, no. 9 (2023), p.
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  • Description: Biomarker-based cancer identification and classification tools are widely used in bioinformatics and machine learning fields. However, the high dimensionality of microarray gene expression data poses a challenge for identifying important genes in cancer diagnosis. Many feature selection algorithms optimize cancer diagnosis by selecting optimal features. This article proposes an ensemble rank-based feature selection method (EFSM) and an ensemble weighted average voting classifier (VT) to overcome this challenge. The EFSM uses a ranking method that aggregates features from individual selection methods to efficiently discover the most relevant and useful features. The VT combines support vector machine, k-nearest neighbor, and decision tree algorithms to create an ensemble model. The proposed method was tested on three benchmark datasets and compared to existing built-in ensemble models. The results show that our model achieved higher accuracy, with 100% for leukaemia, 94.74% for colon cancer, and 94.34% for the 11-tumor dataset. This study concludes by identifying a subset of the most important cancer-causing genes and demonstrating their significance compared to the original data. The proposed approach surpasses existing strategies in accuracy and stability, significantly impacting the development of ML-based gene analysis. It detects vital genes with higher precision and stability than other existing methods. © 2023 by the authors.

Deep learning and federated learning for screening COVID-19 : a review

  • Authors: Mondal, M. , Bharati, Subrato , Podder, Prajoy , Kamruzzaman, Joarder
  • Date: 2023
  • Type: Text , Journal article , Review
  • Relation: BioMedInformatics Vol. 3, no. 3 (2023), p. 691-713
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  • Description: Since December 2019, a novel coronavirus disease (COVID-19) has infected millions of individuals. This paper conducts a thorough study of the use of deep learning (DL) and federated learning (FL) approaches to COVID-19 screening. To begin, an evaluation of research articles published between 1 January 2020 and 28 June 2023 is presented, considering the preferred reporting items of systematic reviews and meta-analysis (PRISMA) guidelines. The review compares various datasets on medical imaging, including X-ray, computed tomography (CT) scans, and ultrasound images, in terms of the number of images, COVID-19 samples, and classes in the datasets. Following that, a description of existing DL algorithms applied to various datasets is offered. Additionally, a summary of recent work on FL for COVID-19 screening is provided. Efforts to improve the quality of FL models are comprehensively reviewed and objectively evaluated. © 2023 by the authors.

Decentralized content sharing in mobile ad-hoc networks : a survey

  • Authors: Kaisar, Shahriar , Kamruzzaman, Joarder , Karmakar, Gour , Rashid, Md Mamunur
  • Date: 2023
  • Type: Text , Journal article , Review
  • Relation: Digital Communications and Networks Vol. 9, no. 6 (2023), p. 1363-1398
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  • Description: The evolution of smart mobile devices has significantly impacted the way we generate and share contents and introduced a huge volume of Internet traffic. To address this issue and take advantage of the short-range communication capabilities of smart mobile devices, the decentralized content sharing approach has emerged as a suitable and promising alternative. Decentralized content sharing uses a peer-to-peer network among co-located smart mobile device users to fulfil content requests. Several articles have been published to date to address its different aspects including group management, interest extraction, message forwarding, participation incentive, and content replication. This survey paper summarizes and critically analyzes recent advancements in decentralized content sharing and highlights potential research issues that need further consideration. © 2022 Chongqing University of Posts and Telecommunications