Securing smart healthcare cyber-physical systems against blackhole and greyhole attacks using a blockchain-enabled gini index framework
- Authors: Javed, Mannan , Tariq, Noshina , Ashraf, Muhammad , Khan, Farrukh , Asim, Muhammad , Imran, Muhammad
- Date: 2023
- Type: Text , Journal article
- Relation: Sensors Vol. 23, no. 23 (2023), p.
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- Description: The increasing reliance on cyber-physical systems (CPSs) in critical domains such as healthcare, smart grids, and intelligent transportation systems necessitates robust security measures to protect against cyber threats. Among these threats, blackhole and greyhole attacks pose significant risks to the availability and integrity of CPSs. The current detection and mitigation approaches often struggle to accurately differentiate between legitimate and malicious behavior, leading to ineffective protection. This paper introduces Gini-index and blockchain-based Blackhole/Greyhole RPL (GBG-RPL), a novel technique designed for efficient detection and mitigation of blackhole and greyhole attacks in smart health monitoring CPSs. GBG-RPL leverages the analytical prowess of the Gini index and the security advantages of blockchain technology to protect these systems against sophisticated threats. This research not only focuses on identifying anomalous activities but also proposes a resilient framework that ensures the integrity and reliability of the monitored data. GBG-RPL achieves notable improvements as compared to another state-of-the-art technique referred to as BCPS-RPL, including a 7.18% reduction in packet loss ratio, an 11.97% enhancement in residual energy utilization, and a 19.27% decrease in energy consumption. Its security features are also very effective, boasting a 10.65% improvement in attack-detection rate and an 18.88% faster average attack-detection time. GBG-RPL optimizes network management by exhibiting a 21.65% reduction in message overhead and a 28.34% decrease in end-to-end delay, thus showing its potential for enhanced reliability, efficiency, and security. © 2023 by the authors.
A cloud-based IoMT data sharing scheme with conditional anonymous source authentication
- Authors: Wang, Yan-Ping , Wang, Xiao-Fen , Dai, Hong-Ning , Zhang, Xiao-Song , Su, Yu , Imran, Muhammad , Nasser, Nidal
- Date: 2022
- Type: Text , Conference paper
- Relation: 2022 IEEE Global Communications Conference, GLOBECOM 2022, Virtual, online, 4-8 December 2022, 2022 IEEE Global Communications Conference, GLOBECOM 2022 - Proceedings p. 2915-2920
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- Description: As a rapidly growing subset of the Internet of Thing (IoT), the cloud-based Internet of Medical Thing (IoMT) has been widely applied in remote healthcare industries, which allows the physicians to monitor patients' body parameters remotely to offer continuous and timely healthcare. These healthcare parameters usually contain sensitive information, such as heart rates, glucose levels and etc., and the exposure of them may pose serious threats to the patients' health and lives. To guarantee security and privacy, many IoMT data sharing schemes have been proposed. However, most of these schemes either exhibit a one-to-one data sharing structure or fail to protect the patients' privacy. Since the data usually needs to be shared to different physicians, patients may want to be assisted without revealing their identities. To meet these requirements in healthcare systems, we propose a multi-receiver secure healthcare data sharing scheme, in which the patients are allowed to share their IoMT data to multiple physicians simultaneously for a multidisciplinary treatment, and the conditional anonymity is achieved where data source authentication is provided without revealing the patient's identity. When the patient health condition is abnormal, the hospital can correctly and quickly trace the patient's identity and inform him/her immediately. Our scheme is formally proved to achieve multiple security properties including confidentiality, unforgeability and anonymity. Simulation results demonstrate that the proposed scheme is efficient and practical. © 2022 IEEE.
A cooperative crowdsensing system based on flying and ground vehicles to control respiratory viral disease outbreaks
- Authors: Sahraoui, Yesin , Kerrache, Chaker , Amadeo, Marica , Vegni, Anna , Korichi, Ahmed , Nebhen, Jamel , Imran, Muhammad
- Date: 2022
- Type: Text , Journal article
- Relation: Ad Hoc Networks Vol. 124, no. (2022), p.
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- Description: The massive increase in population density in cities has led to several urban problems, such as an increment of air pollution, traffic congestion, and a faster spread of infectious diseases. With the rapid innovation in the intelligent sensors technology, and its integration into smart vehicles and Unmanned Aerial Vehicles (UAVs), a novel sensing paradigm has been promoted, namely vehicular crowdsensing, which leverages on-board sensors to capture information from the surrounding environment. Collected data are then analyzed to take proper countermeasures. In this paper, we present a smart coordination mechanism between UAVs and ground vehicles (GVs), which sense information like body temperature and breathing rate of people, in order to support a variety of monitoring applications, including discovering the presence of infectious diseases. In our framework, namely GUAVA, aerial and ground vehicles are equipped with GPS devices and thermal cameras to monitor specific geographic areas, detect humans’ vital parameters and, at the same time, discover duplicate data by identifying matching faces in thermal video sequences with the GaussianFace algorithm. The sensing tasks in hard-to-reach places are assigned to UAVs, with the ability to power up wirelessly from the nearest GV and offload the collected monitoring images to it. Simulation results have assessed our proposed framework, showing good performance in terms of distinct Quality of Service (QoS) metrics. © 2021
A lightweight federated learning based privacy preserving B5G pandemic response network using unmanned aerial vehicles: A proof-of-concept
- Authors: Nasser, Nasser , Fadlullah, Zubair , Fouda, Mostafa , Ali, Asmaa , Imran, Muhammad
- Date: 2022
- Type: Text , Journal article
- Relation: Computer Networks Vol. 205, no. (2022), p.
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- Description: The concept of an intelligent pandemic response network is gaining momentum during the current novel coronavirus disease (COVID-19) era. A heterogeneous communication architecture is essential to facilitate collaborative and intelligent medical analytics in the fifth generation and beyond (B5G) networks to intelligently learn and disseminate pandemic-related information and diagnostic results. However, such a technique raises privacy issues pertaining to the health data of the patients. In this paper, we envision a privacy-preserving pandemic response network using a proof-of-concept, aerial–terrestrial network system serving mobile user entities/equipment (UEs). By leveraging the unmanned aerial vehicles (UAVs), a lightweight federated learning model is proposed to collaboratively yet privately learn medical (e.g., COVID-19) symptoms with high accuracy using the data collected by individual UEs using ambient sensors and wearable devices. An asynchronous weight updating technique is introduced in federated learning to avoid redundant learning and save precious networking as well as computing resources of the UAVs/UEs. A use-case where an Artificial Intelligence (AI)-based model is employed for COVID-19 detection from radiograph images is presented to demonstrate the effectiveness of our proposed approach. © 2021 Elsevier B.V.
A privacy-preserving framework for smart context-aware healthcare applications
- Authors: Azad, Muhammad , Arshad, Junaid , Mahmoud, Shazia , Salah, Khaled , Imran, Muhammad
- Date: 2022
- Type: Text , Journal article
- Relation: Transactions on Emerging Telecommunications Technologies Vol. 33, no. 8 (2022), p.
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- Description: Smart connected devices are widely used in healthcare to achieve improved well-being, quality of life, and security of citizens. While improving quality of healthcare, such devices generate data containing sensitive patient information where unauthorized access constitutes breach of privacy leading to catastrophic outcomes for an individual as well as financial loss to the governing body via regulations such as the General Data Protection Regulation. Furthermore, while mobility afforded by smart devices enables ease of monitoring, portability, and pervasive processing, it introduces challenges with respect to scalability, reliability, and context awareness. This paper is focused on privacy preservation within smart context-aware healthcare emphasizing privacy assurance challenges within Electronic Transfer of Prescription. We present a case for a comprehensive, coherent, and dynamic privacy-preserving system for smart healthcare to protect sensitive user data. Based on a thorough analysis of existing privacy preservation models, we propose an enhancement to the widely used Salford model to achieve privacy preservation against masquerading and impersonation threats. The proposed model therefore improves privacy assurance for smart healthcare while addressing unique challenges with respect to context-aware mobility of such applications. © 2019 John Wiley & Sons, Ltd.
An automatic detection of breast cancer diagnosis and prognosis based on machine learning using ensemble of classifiers
- Authors: Naseem, Usman , Rashid, Junaid , Ali, Liaqat , Kim, Jungeun , Haq, Qazi , Awan, Mazhar , Imran, Muhammad
- Date: 2022
- Type: Text , Journal article
- Relation: IEEE Access Vol. 10, no. (2022), p. 78242-78252
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- Description: Breast cancer (BC) is the second most prevalent type of cancer among women leading to death, and its rate of mortality is very high. Its effects will be reduced if diagnosed early. BC's early detection will greatly boost the prognosis and likelihood of recovery, as it may encourage prompt surgical care for patients. It is therefore vital to have a system enabling the healthcare industry to detect breast cancer quickly and accurately. Machine learning (ML) is widely used in breast cancer (BC) pattern classification due to its advantages in modelling a critical feature detection from complex BC datasets. In this paper, we propose a system for automatic detection of BC diagnosis and prognosis using ensemble of classifiers. First, we review various machine learning (ML) algorithms and ensemble of different ML algorithms. We present an overview of ML algorithms including ANN, and ensemble of different classifiers for automatic BC diagnosis and prognosis detection. We also present and compare various ensemble models and other variants of tested ML based models with and without up-sampling technique on two benchmark datasets. We also studied the effects of using balanced class weight on prognosis dataset and compared its performance with others. The results showed that the ensemble method outperformed other state-of-the-art methods and achieved 98.83% accuracy. Because of high performance, the proposed system is of great importance to the medical industry and relevant research community. The comparison shows that the proposed method outperformed other state-of-the-art methods. © 2013 IEEE.
An effective data-collection scheme with AUV path planning in underwater wireless sensor networks
- Authors: Khan, Wahab , Hua, Wang , Anwar, Muhammad , Alharbi, Abdullah , Imran, Muhammad , Khan, Javed
- Date: 2022
- Type: Text , Journal article
- Relation: Wireless Communications and Mobile Computing Vol. 2022, no. (2022), p.
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- Description: Data collection in underwater wireless sensor networks (UWSNs) using autonomous underwater vehicles (AUVs) is a more robust solution than traditional approaches, instead of transmitting data from each node to a destination node. However, the design of delay-aware and energy-efficient path planning for AUVs is one of the most crucial problems in collecting data for UWSNs. To reduce network delay and increase network lifetime, we proposed a novel reliable AUV-based data-collection routing protocol for UWSNs. The proposed protocol employs a route planning mechanism to collect data using AUVs. The sink node directs AUVs for data collection from sensor nodes to reduce energy consumption. First, sensor nodes are organized into clusters for better scalability, and then, these clusters are arranged into groups to assign an AUV to each group. Second, the traveling path for each AUV is crafted based on the Markov decision process (MDP) for the reliable collection of data. The simulation results affirm the effectiveness and efficiency of the proposed technique in terms of throughput, energy efficiency, delay, and reliability. © 2022 Wahab Khan et al.
An efficient network intrusion detection and classification system
- Authors: Ahmad, Iftikhar , Haq, Qazi , Imran, Muhammad , Alassafi, Madini , Alghamdi, Rayed
- Date: 2022
- Type: Text , Journal article
- Relation: Mathematics Vol. 10, no. 3 (2022), p.
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- Description: Intrusion detection in computer networks is of great importance because of its effects on the different communication and security domains. The detection of network intrusion is a challenge. Moreover, network intrusion detection remains a challenging task as a massive amount of data is required to train the state-of-the-art machine learning models to detect network intrusion threats. Many approaches have already been proposed recently on network intrusion detection. However, they face critical challenges owing to the continuous increase in new threats that current systems do not understand. This paper compares multiple techniques to develop a network intrusion detection system. Optimum features are selected from the dataset based on the correlation between the features. Furthermore, we propose an AdaBoost-based approach for network intrusion detection based on these selected features and present its detailed functionality and performance. Unlike most previous studies, which employ the KDD99 dataset, we used a recent and comprehensive UNSW-NB 15 dataset for network anomaly detection. This dataset is a collection of network packets exchanged between hosts. It comprises 49 attributes, including nine types of threats such as DoS, Fuzzers, Exploit, Worm, shellcode, reconnaissance, generic, and analysis Backdoor. In this study, we employ SVM and MLP for comparison. Finally, we propose AdaBoost based on the decision tree classifier to classify normal activity and possible threats. We monitored the network traffic and classified it into either threats or non-threats. The experimental findings showed that our proposed method effectively detects different forms of network intrusions on computer networks and achieves an accuracy of 99.3% on the UNSW-NB15 dataset. The proposed system will be helpful in network security applications and research domains. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
An intelligent and efficient network intrusion detection system using deep learning
- Authors: Qazi, Emad-ul-Haq , Imran, Muhammad , Haider, Noman , Shoaib, Muhammad , Razzak, Imran
- Date: 2022
- Type: Text , Journal article
- Relation: Computers and Electrical Engineering Vol. 99, no. (2022), p.
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- Description: With continuously escalating threats and attacks, accurate and timely intrusion detection in communication networks is challenging. Many approaches have already been proposed recently on network intrusion detection. However, they face critical challenges due to the continuous increase of new threats that current systems do not understand. Motivated by the outstanding performance of deep learning (DL) in many detection and recognition tasks, we introduce an intelligent and efficient network intrusion detection system (NIDS) based on DL. This study proposes a non-symmetric deep auto-encoder for network intrusion detection problems and presents its detailed functionality and performance. We validate the robustness and effectiveness of the proposed NIDS using a benchmark dataset, i.e., KDD CUP'99. Our DL-based method is implemented in the TensorFlow library and GPU framework, and it achieves an accuracy of 99.65%. The proposed system can be used in network security research domains and DL-based detection and classification systems. © 2022
An IoT-based smart healthcare system to detect dysphonia
- Authors: Ali, Zulfiqar , Imran, Muhammad , Shoaib, Muhammad
- Date: 2022
- Type: Text , Journal article
- Relation: Neural Computing and Applications Vol. 34, no. 14 (2022), p. 11255-11265
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- Description: Smart healthcare systems for the internet of things (IoT) platform are cost-efficient and facilitate continuous remote monitoring of patients to avoid unnecessary hospital visits and long waiting times to see practitioners. Presenting a smart healthcare system for the detection of dysphonia can reduce the suffering and pain of patients by providing an initial evaluation of voice. This preliminary feedback of voice could minimize the burden on ENT specialists by referring only genuine cases to them as well as giving an early alarm of potential voice complications to patients. Any possible delay in the treatment and/or inaccurate diagnosis using the subjective nature of tools may lead to severe circumstances for an individual because some types of dysphonia are life-threatening. Therefore, an accurate and reliable smart healthcare system for IoT platform to detect dysphonia is proposed and implemented in this study. Higher-order directional derivatives are used to analyze the time–frequency spectrum of signals in the proposed system. The computed derivatives provide essential and vital information by analyzing the spectrum along different directions to capture the changes that appeared due to malfunctioning the vocal folds. The proposed system provides 99.1% accuracy, while the sensitivity and specificity are 99.4 and 98.1%, respectively. The experimental results showed that the proposed system could provide better classification accuracy than the traditional non-directional first-order derivatives. Hence, the system can be used as a reliable tool for detecting dysphonia and implemented in edge devices to avoid latency issues and protect privacy, unlike cloud processing. © 2021, Springer-Verlag London Ltd., part of Springer Nature.
Clustering-based real-time anomaly detection—a breakthrough in big data technologies
- Authors: Habeeb, Riyaz , Nasaruddin, Fariza , Gani, Abdullah , Amanullah, Mohamed , Hashem, Ibrahim , Ahmed, Ejaz , Imran, Muhammad
- Date: 2022
- Type: Text , Journal article
- Relation: Transactions on Emerging Telecommunications Technologies Vol. 33, no. 8 (2022), p.
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- Description: Off late, the ever increasing usage of a connected Internet-of-Things devices has consequently augmented the volume of real-time network data with high velocity. At the same time, threats on networks become inevitable; hence, identifying anomalies in real time network data has become crucial. To date, most of the existing anomaly detection approaches focus mainly on machine learning techniques for batch processing. Meanwhile, detection approaches which focus on the real-time analytics somehow deficient in its detection accuracy while consuming higher memory and longer execution time. As such, this paper proposes a novel framework which focuses on real-time anomaly detection based on big data technologies. In addition, this paper has also developed streaming sliding window local outlier factor coreset clustering algorithms (SSWLOFCC), which was then implemented into the framework. The proposed framework that comprises BroIDS, Flume, Kafka, Spark streaming, SparkMLlib, Matplot and HBase was evaluated to substantiate its efficacy, particularly in terms of accuracy, memory consumption, and execution time. The evaluation is done by performing critical comparative analysis using existing approaches, such as K-means, hierarchical density-based spatial clustering of applications with noise (HDBSCAN), isolation forest, spectral clustering and agglomerative clustering. Moreover, Adjusted Rand Index and memory profiler package were used for the evaluation of the proposed framework against the existing approaches. The outcome of the evaluation has substantially proven the efficacy of the proposed framework with a much higher accuracy rate of 96.51% when compared to other algorithms. Besides, the proposed framework also outperformed the existing algorithms in terms of lesser memory consumption and execution time. Ultimately the proposed solution enable analysts to precisely track and detect anomalies in real time. © 2019 John Wiley & Sons, Ltd.
Contrastive GNN-based traffic anomaly analysis against imbalanced dataset in IoT-based ITS
- Authors: Wang, Yang , Lin, Xi , Wu, Jun , Bashir, Ali , Yang, Wu , Li, Jianhua , Imran, Muhammad
- Date: 2022
- Type: Text , Conference paper
- Relation: 2022 IEEE Global Communications Conference, GLOBECOM 2022, Virtual, online, 4-8 December 2022, 2022 IEEE Global Communications Conference, GLOBECOM 2022 - Proceedings p. 3557-3562
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- Description: The traffic anomaly analysis in IoT-based intelligent transportation system (ITS) is crucial to improving public transportation safety and efficiency. The issue is also challenging due to the unbalanced distribution of anomaly data in IoT-based ITS, which may cause overfitting or underfitting in the training phase. However, some research on traffic anomaly analysis injected limited data to address the shortage of anomaly samples or even neglects this issue, which overlooks the potential representation of nodes in graph neural networks. In this paper, we propose an improved contrastive GNN-based learning framework for traffic anomaly analysis that alleviates the problem of imbalanced datasets in the training phase. In this framework, we provide a graph augmentation approach with coupled features to learn different views of graph data. Besides, we design an effective training method based on the contrastive loss for our framework, which can learn the better performance of latent representations utilized in the downstream tasks. Finally, we conduct extensive experiments to evaluate the performance of our proposed frame-works based on real-world datasets. We demonstrate that our framework achieves as high as 6.45% precision improvement compared to the state-of-the-art. © 2022 IEEE.
Energy harvesting in underwater acoustic wireless sensor networks : design, taxonomy, applications, challenges and future directions
- Authors: Khan, Anwar , Imran, Muhammad , Alharbi, Abdullah , Mohamed, Ehab , Fouda, Mostafa
- Date: 2022
- Type: Text , Journal article , Review
- Relation: IEEE Access Vol. 10, no. (2022), p. 134606-134622
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- Description: In underwater acoustic wireless sensor networks (UAWSNs), energy harvesting either enhances the lifetime of a network by increasing the battery power of sensor nodes or ensures battery-less operation of nodes. This, in effect, results in sustainable and reliable operation of the network deployed for various underwater applications. This work provides a survey of the energy harvesting techniques for UAWSNs. Our work is unique than the existing work on underwater energy harvesting in that it includes state-of-the art techniques designed in the last decade. It analyzes every harvesting scheme in terms of its main idea, merits, demerits and the extent of the harvested power (energy). The description of the merits results in selection of the suitable scheme for the suitable underwater applications. The demerits of the addressed schemes provide an insight to their future enhancement and improvement. Moreover, the harvested techniques are classified into various categories depending upon the involved energy harvesting mechanism and compared based on the maximum and minimum amount of harvested power, which helps in selection of the suitable category keeping in view the power budget of an underwater network before deployment. The challenges in energy harvesting and in UAWSNs are described to provide an insight to them and to address them for further enhancement in the harvested extent. Finally, research directions are specified for future investigation. © 2013 IEEE.
Extracting built-up areas from spectro-textural information using machine learning
- Authors: Maqsoom, Ahsen , Aslam, Bilal , Yousafzai, Arbaz , Ullah, Fahim , Ullah, Sami , Imran, Muhammad
- Date: 2022
- Type: Text , Journal article
- Relation: Soft Computing Vol. 26, no. 16 (2022), p. 7789-7808
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- Description: Extraction of built-up area (BUA) is essential for proper city planning and management. It enables the concerned authorities to formulate better city development policies and manage emergent disasters. However, the traditionally used optical data present spectral confusion where BUAs are mixed with other features adding to management complexities. Therefore, an advanced automated method is required to extract the spectral and textural features from satellite data for the pattern recognition of BUA. Landsat-8 Operational Land Imager (OLI) has been used in the current study to identify the pattern and extract BUA of Gujranwala, Pakistan. First, eight textural features based on the gray-level co-occurrence matrix (GLCM) are selected and combined with multispectral data. Then, feature selection methods are applied to select optimal features used to train the proposed support vector machine (SVM) classifier. Finally, the results from SVM classifiers are compared with k-nearest neighbor (k-NN) and backpropagation neural network (BP-NN) to highlight any improvements in results. The comparisons show that the proposed approach increases the overall accuracy of linear-SVM by 8.41%, radial basis function SVM by 8.3%, BP-NN by 7.63%, and k-NN by 6.6%. This can help city managers and planners to extract critical BUA information in otherwise unplanned and rapidly expanding cities to move toward smart and sustainable cities. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
Formal modeling and verification of a blockchain-based crowdsourcing consensus protocol
- Authors: Afzaal, Hamra , Imran, Muhammad , Janjua, Muhammad , Gochhayat, Sarada
- Date: 2022
- Type: Text , Journal article
- Relation: IEEE Access Vol. 10, no. (2022), p. 8163-8183
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- Description: Crowdsourcing is an effective technique that allows humans to solve complex problems that are hard to accomplish by automated tools. Some significant challenges in crowdsourcing systems include avoiding security attacks, effective trust management, and ensuring the system's correctness. Blockchain is a promising technology that can be efficiently exploited to address security and trust issues. The consensus protocol is a core component of a blockchain network through which all the blockchain peers achieve an agreement about the state of the distributed ledger. Therefore, its security, trustworthiness, and correctness have vital importance. This work proposes a Secure and Trustworthy Blockchain-based Crowdsourcing (STBC) consensus protocol to address these challenges. Model checking is an effective and automatic technique based on formal methods that is utilized to ensure the correctness of STBC consensus protocol. The proposed consensus protocol's formal specification is described using Communicating Sequential Programs (CSP#). Safety, fault tolerance, leader trust, and validators' trust are important properties for a consensus protocol, which are formally specified through Linear Temporal Logic (LTL) to prevent several security attacks, such as blockchain fork, selfish mining, and invalid block insertion. Process Analysis Toolkit (PAT) is utilized for the formal verification of the proposed consensus protocol. © 2022 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.
Formal verification of persistence and liveness in the trust-based blockchain crowdsourcing consensus protocol
- Authors: Afzaal, Hamra , Imran, Muhammad , Janjua, Muhammad
- Date: 2022
- Type: Text , Journal article
- Relation: Computer Communications Vol. 192, no. (2022), p. 384-401
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- Description: Crowdsourcing is a potential computing paradigm that exploits collective human intelligence to solve complex tasks, but it suffers from various safety and security problems. Blockchain has emerged as a promising technology to address most of the security issues, however, it is challenging to find an appropriate and trusted blockchain-based consensus protocol for crowdsourcing services. This work proposes a novel Trust-based Blockchain Crowdsourcing consensus protocol that selects a leader and validators based on various trust factors. The proposed protocol addresses a major issue of ensuring correctness associated with the safety and security-critical systems which has a vital importance because failure of such systems may lead to adverse consequences. Mainly it is focused on persistence and liveness properties preventing invalid block insertion and consensus delay attacks. Model checking technique is utilized because of its effectiveness and automatic nature to perform formal verification. The proposed protocol is specified using Communicating Sequential Programs, and the persistence and liveness properties are specified through Linear Temporal Logic. The model verification is performed by giving the formal model and the properties as input to the Process Analysis Toolkit which checks for the satisfaction or violation of the properties. © 2022 Elsevier B.V.
Link and stability-aware adaptive cooperative routing with restricted packets transmission and void-avoidance for underwater acoustic wireless sensor networks
- Authors: Khan, Anwar , Imran, Muhammad , Shoaib, Muhammad , Rahman, Atiq , Sama, Najmus
- Date: 2022
- Type: Text , Journal article
- Relation: Computer Communications Vol. 181, no. (2022), p. 428-437
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- Description: In underwater acoustic wireless sensor networks (UAWSNs), the conventional routing protocols continuously transmit data packets containing the same information when a source node detects an event of interest. This approach, however, leads to high energy consumption, high interference and challenges the stability of the network due to heavy data load on the nodes close to water surface. To overcome these challenges, this article proposes two routing protocols for UAWSNs. They are, SRRPV: stability-aware routing with restricted packets transmission and void-avoidance and LS-ACRPV: link and stability-aware adaptive cooperative routing with restricted packets transmission and void-avoidance. In SRRPV, a source node detects the event of interest, generates and routes packets. Subsequent packets routing is performed only when a change in intensity of the event is detected. Due to restricted packets transmission, three-hop connected paths with suitable neighbors are chosen to overcome the existence of a void (non-availability of a suitable forwarder) and, therefore, control packets loss. Moreover, the defined packet holding time ensures that nodes with the highest available energy transfer the most packets by holding the packets for the shortest interval of time. This strategy, in combination with restricted packets transmission, achieves network stability. Since limited number of packets are routed, the LS-ACRPV protocol is designed that adds reliability to the SRRPV protocol by selecting a routing link with the highest probability of successfully delivering packets to the destination or using adaptive cooperative routing to combat severe link conditions. Unlike the conventional protocols, the proposed protocols do not require the geographical position coordinates of nodes, which is challenging in underwater environment as nodes change their positions with sea tides and currents. Extensive simulation results reveal promising performance of the proposed schemes in terms of energy expenditure, delay and network stability at the expense of low packets delivery due to restricted packets transmission. © 2021 Elsevier B.V.
Metric learning-based few-shot malicious node detection for IoT backhaul/fronthaul networks
- Authors: Zhou, Ke , Lin, Xi , Wu, Jun , Bashir, Ali , Li, Jianhua , Imran, Muhammad
- Date: 2022
- Type: Text , Conference paper
- Relation: 2022 IEEE Global Communications Conference, GLOBECOM 2022, Virtual, online, 4-8 December 2022, 2022 IEEE Global Communications Conference, GLOBECOM 2022 - Proceedings p. 5777-5782
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- Description: The development of backhaul/fronthaul networks can enable low latency and high reliability, but nodes in future networks like Internet of Things (IoT) can conduct malicious activities like flooding attack and DDoS attack, which can decrease QoS of smart backhaul/fronthaul network. Timely detection of malicious nodes in future networks is significant for low-latency backhaul/fronthaul networks. However, conventional supervised learning-based detection models require abundant malicious training samples, while capturing adequate malicious samples can not meet the requirement of timely detection. In this paper, we propose a novel few-shot malicious node detection system for improving QoS of IoT backhaul/fronthaul network, which can detect malicious nodes with unknown malicious activities through a limited number of network traffic samples. In our proposed system, we first design a fresh IoT traffic sample processing approach, which integrates normal activity samples and known malicious activity samples to generate training pairs. Then, we design a metric learning-based malicious node detection model training method, which employs a contrastive loss over distance metric to distinguish between similar and dissimilar pairs of samples. Besides, the trained model can detect nodes with unknown malicious activities by comparing real-time samples with few-shot samples of malicious nodes. Finally, the proposed system is evaluated on a real-world IoT network dataset named N-BaIoT. The exhaustive experiment results show that our model can achieve an average accuracy around 97.67 % when detecting malicious nodes with unknown malicious activities, which is comparable to state-of-the-art supervised learning models while our model only needs 5-shot samples of malicious node. © 2022 IEEE.
Multitask deep learning for cost-effective prediction of patient's length of stay and readmission state using multimodal physical activity sensory data
- Authors: Ali, Sajid , El-Sappagh, Shaker , Ali, Farman , Imran, Muhammad , Abuhmed, Tamer
- Date: 2022
- Type: Text , Journal article
- Relation: IEEE Journal of Biomedical and Health Informatics Vol. 26, no. 12 (2022), p. 5793-5804
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- Description: In a hospital, accurate and rapid mortality prediction of Length of Stay (LOS) is essential since it is one of the essential measures in treating patients with severe diseases. When predictions of patient mortality and readmission are combined, these models gain a new level of significance. Therefore, the most expensive components of patient care are LOS and readmission rates. Several studies have assessed readmission to the hospital as a single-task issue. The performance, robustness, and stability of the model increase when many correlated tasks are optimized. This study develops multimodal multitasking Long Short-Term Memory (LSTM) Deep Learning (DL) model that can predict both LOS and readmission for patients using multi-sensory data from 47 patients. Continuous sensory data is divided into eight sections, each of which is recorded for an hour. The time steps are constructed using a dual 10-second window-based technique, resulting in six steps per hour. The 30 statistical features are computed by transforming the sensory input into the resulting vector. The proposed multitasking model predicts 30-day readmission as a binary classification problem and LOS as a regression task by constructing discrete time-step data based on the length of physical activity during a hospital stay. The proposed model is compared to a random forest for a single-task problem (classification or regression) because typical machine learning algorithms are unable to handle the multitasking challenge. In addition, sensory data combined with other cost-effective modalities such as demographics, laboratory tests, and comorbidities to construct reliable models for personalized, cost-effective, and medically acceptable prediction. With a high accuracy of 94.84%, the proposed multitask multimodal DL model classifies the patient's readmission status and determines the patient's LOS in hospital with a minimal Mean Square Error (MSE) of 0.025 and Root Mean Square Error (RMSE) of 0.077, which is promising, effective, and trustworthy. © 2013 IEEE.
Water quality management using hybrid machine learning and data mining algorithms : an indexing approach
- Authors: Aslam, Bilal , Maqsoom, Ahsen , Cheema, Ali , Ullah, Fahim , Alharbi, Abdullah , Imran, Muhammad
- Date: 2022
- Type: Text , Journal article
- Relation: IEEE Access Vol. 10, no. (2022), p. 119692-119705
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- Description: One of the key functions of global water resource management authorities is river water quality (WQ) assessment. A water quality index (WQI) is developed for water assessments considering numerous quality-related variables. WQI assessments typically take a long time and are prone to errors during sub-indices generation. This can be tackled through the latest machine learning (ML) techniques renowned for superior accuracy. In this study, water samples were taken from the wells in the study area (North Pakistan) to develop WQI prediction models. Four standalone algorithms, i.e., random trees (RT), random forest (RF), M5P, and reduced error pruning tree (REPT), were used in this study. In addition, 12 hybrid data-mining algorithms (a combination of standalone, bagging (BA), cross-validation parameter selection (CVPS), and randomizable filtered classification (RFC)) were also used. Using the 10-fold cross-validation technique, the data were separated into two groups (70:30) for algorithm creation. Ten random input permutations were created using Pearson correlation coefficients to identify the best possible combination of datasets for improving the algorithm prediction. The variables with very low correlations performed poorly, whereas hybrid algorithms increased the prediction capability of numerous standalone algorithms. Hybrid RT-Artificial Neural Network (RT-ANN) with RMSE = 2.319, MAE = 2.248, NSE = 0.945, and PBIAS = -0.64 outperformed all other algorithms. Most algorithms overestimated WQI values except for BA-RF, RF, BA-REPT, REPT, RFC-M5P, RFC-REPT, and ANN- Adaptive Network-Based Fuzzy Inference System (ANFIS). © 2013 IEEE.