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| 1 | Intrusion Detection Systems in Internet of Things and Mobile Ad-Hoc Networks显示文摘Internet of Things(IoT)devices work mainly in wireless mediums;requiring different Intrusion Detection System(IDS)kind of solutions to leverage 802.11 header information for intrusion detection.Wireless-specific traffic features with high information gain are primarily found in data link layers rather than application layers in wired networks.This survey investigates some of the complexities and challenges in deploying wireless IDS in terms of data collection methods,IDS techniques,IDS placement strategies,and traffic data analysis techniques.This paper’s main finding highlights the lack of available network traces for training modern machine-learning models against IoT specific intrusions.Specifically,the Knowledge Discovery in Databases(KDD)Cup dataset is reviewed to highlight the design challenges of wireless intrusion detection based on current data attributes and proposed several guidelines to future-proof following traffic capture methods in the wireless network(WN).The paper starts with a review of various intrusion detection techniques,data collection methods and placement methods.The main goal of this paper is to study the design challenges of deploying intrusion detection system in a wireless environment.Intrusion detection system deployment in a wireless environment is not as straightforward as in the wired network environment due to the architectural complexities.So this paper reviews the traditional wired intrusion detection deployment methods and discusses how these techniques could be adopted into the wireless environment and also highlights the design challenges in the wireless environment.The main wireless environments to look into would be Wireless Sensor Networks(WSN),Mobile Ad Hoc Networks(MANET)and IoT as this are the future trends and a lot of attacks have been targeted into these networks.So it is very crucial to design an IDS specifically to target on the wireless networks. | Vasaki Ponnusamy Mamoona Humayun NZJhanjhi Aun Yichiet Maram Fahhad Almufareh | 2022 | Computer Systems Science & Engineering2022，40，3： | 2 |
| 2 | Prediction of COVID-19 Cases Using Machine Learning for Effective Public Health Management显示文摘COVID-19 is a pandemic that has affected nearly every country in the world.At present,sustainable development in the area of public health is considered vital to securing a promising and prosperous future for humans.However,widespread diseases,such as COVID-19,create numerous challenges to this goal,and some of those challenges are not yet defined.In this study,a Shallow Single-Layer Perceptron Neural Network(SSLPNN)and Gaussian Process Regression(GPR)model were used for the classification and prediction of confirmed COVID-19 cases in five geographically distributed regions of Asia with diverse settings and environmental conditions:namely,China,South Korea,Japan,Saudi Arabia,and Pakistan.Significant environmental and non-environmental features were taken as the input dataset,and confirmed COVID-19 cases were taken as the output dataset.A correlation analysis was done to identify patterns in the cases related to fluctuations in the associated variables.The results of this study established that the population and air quality index of a region had a statistically significant influence on the cases.However,age and the human development index had a negative influence on the cases.The proposed SSLPNN-based classification model performed well when predicting the classes of confirmed cases.During training,the binary classification model was highly accurate,with a Root Mean Square Error(RMSE)of 0.91.Likewise,the results of the regression analysis using the GPR technique with Matern 5/2 were highly accurate(RMSE=0.95239)when predicting the number of confirmed COVID-19 cases in an area.However,dynamic management has occupied a core place in studies on the sustainable development of public health but dynamic management depends on proactive strategies based on statistically verified approaches,like Artificial Intelligence(AI).In this study,an SSLPNN model has been trained to fit public health associated data into an appropriate class,allowing GPR to predict the number of confirmed COVID-19 cases in an area based on the given values of selected parameters. Therefore, this tool can help authorities in different ecological settingseffectively manage COVID-19. | Fahad Ahmad Saleh N.Almuayqil Mamoona Humayun Shahid Naseem Wasim Ahmad Khan Kashaf Junaid | 2021 | Computers, Materials & Continua2021，，3： | 1 |
| 3 | Role of Fuzzy Approach towards Fault Detection for Distributed Components显示文摘Component-based software development is rapidly introducing numerous new paradigms and possibilities to deliver highly customized software in a distributed environment.Among other communication,teamwork,and coordination problems in global software development,the detection of faults is seen as the key challenge.Thus,there is a need to ensure the reliability of component-based applications requirements.Distributed device detection faults applied to tracked components from various sources and failed to keep track of all the large number of components from different locations.In this study,we propose an approach for fault detection from componentbased systems requirements using the fuzzy logic approach and historical information during acceptance testing.This approach identified error-prone components selection for test case extraction and for prioritization of test cases to validate components in acceptance testing.For the evaluation,we used empirical study,and results depicted that the proposed approach significantly outperforms in component selection and acceptance testing.The comparison to the conventional procedures,i.e.,requirement criteria,and communication coverage criteria without irrelevancy and redundancy successfully outperform other procedures.Consequently,the F-measures of the proposed approach define the accurate selection of components,and faults identification increases in components using the proposed approach were higher(i.e.,more than 80 percent)than requirement criteria,and code coverage criteria procedures(i.e.,less than 80 percent),respectively.Similarly,the rate of fault detection in the proposed approach increases,i.e.,92.80 compared to existing methods i.e.,less than 80 percent.The proposed approach will provide a comprehensive guideline and roadmap for practitioners and researchers. | Yaser Hafeez Sadia Ali Nz Jhanjhi Mamoona Humayun Anand Nayyar Mehedi Masud | 2021 | Computers, Materials & Continua2021，，5： | 0 |
| 4 | Deep Learning Based Sentiment Analysis of COVID-19 Tweets via Resampling and Label Analysis显示文摘Twitter has emerged as a platform that produces new data every day through its users which can be utilized for various purposes.People express their unique ideas and views onmultiple topics thus providing vast knowledge.Sentiment analysis is critical from the corporate and political perspectives as it can impact decision-making.Since the proliferation of COVID-19,it has become an important challenge to detect the sentiment of COVID-19-related tweets so that people’s opinions can be tracked.The purpose of this research is to detect the sentiment of people regarding this problem with limited data as it can be challenging considering the various textual characteristics that must be analyzed.Hence,this research presents a deep learning-based model that utilizes the positives of random minority oversampling combined with class label analysis to achieve the best results for sentiment analysis.This research specifically focuses on utilizing class label analysis to deal with the multiclass problem by combining the class labels with a similar overall sentiment.This can be particularly helpful when dealing with smaller datasets.Furthermore,our proposed model integrates various preprocessing steps with random minority oversampling and various deep learning algorithms including standard deep learning and bi-directional deep learning algorithms.This research explores several algorithms and their impact on sentiment analysis tasks and concludes that bidirectional neural networks do not provide any advantage over standard neural networks as standard Neural Networks provide slightly better results than their bidirectional counterparts.The experimental results validate that our model offers excellent results with a validation accuracy of 92.5%and an F1 measure of 0.92. | Mamoona Humayun Danish Javed Nz Jhanjhi Maram Fahaad Almufareh Saleh Naif Almuayqil | 2023 | Computer Systems Science & Engineering2023，47，10： | 0 |
| 5 | Cyber Security and Privacy Issues in Industrial Internet of Things显示文摘The emergence of industry 4.0 stems from research that has received a great deal of attention in the last few decades.Consequently,there has been a huge paradigm shift in the manufacturing and production sectors.However,this poses a challenge for cybersecurity and highlights the need to address the possible threats targeting(various pillars of)industry 4.0.However,before providing a concrete solution certain aspect need to be researched,for instance,cybersecurity threats and privacy issues in the industry.To fill this gap,this paper discusses potential solutions to cybersecurity targeting this industry and highlights the consequences of possible attacks and countermeasures(in detail).In particular,the focus of the paper is on investigating the possible cyber-attacks targeting 4 layers of IIoT that is one of the key pillars of Industry 4.0.Based on a detailed review of existing literature,in this study,we have identified possible cyber threats,their consequences,and countermeasures.Further,we have provided a comprehensive framework based on an analysis of cybersecurity and privacy challenges.The suggested framework provides for a deeper understanding of the current state of cybersecurity and sets out directions for future research and applications. | NZ Jhanjhi Mamoona Humayun Saleh NAlmuayqil | 2021 | Computer Systems Science & Engineering2021，37，6： | 0 |
| 6 | Improved Video Steganography with Dual Cover Medium,DNA and Complex Frames显示文摘The most valuable resource on the planet is no longer oil,but data.The transmission of this data securely over the internet is another challenge that comes with its ever-increasing value.In order to transmit sensitive information securely,researchers are combining robust cryptography and steganographic approaches.The objective of this research is to introduce a more secure method of video steganography by using Deoxyribonucleic acid(DNA)for embedding encrypted data and an intelligent frame selection algorithm to improve video imperceptibility.In the previous approach,DNA was used only for frame selection.If this DNA is compromised,then our frames with the hidden and unencrypted data will be exposed.Moreover the frame selected in this way were random frames,and no consideration was made to the contents of frames.Hiding data in this way introduces visible artifacts in video.In the proposed approach rather than using DNA for frame selection we have created a fakeDNA out of our data and then embedded it in a video file on intelligently selected frames called the complex frames.Using chaotic maps and linear congruential generators,a unique pixel set is selected each time only from the identified complex frames,and encrypted data is embedded in these random locations.Experimental results demonstrate that the proposed technique shows minimum degradation of the stenographic video hence reducing the very first chances of visual surveillance.Further,the selection of complex frames for embedding and creation of a fake DNA as proposed in this research have higher peak signal-to-noise ratio(PSNR)and reduced mean squared error(MSE)values that indicate improved results.The proposed methodology has been implemented in Matlab. | Asma Sajjad Humaira Ashraf NZ Jhanjhi Mamoona Humayun Mehedi Masud Mohammed A.AlZain | 2023 | Computers, Materials & Continua2023，，2： | 0 |
| 7 | IoT Wireless Intrusion Detection and Network Traffic Analysis显示文摘Enhancement in wireless networks had given users the ability to use the Internet without a physical connection to the router.Almost every Internet of Things(IoT)devices such as smartphones,drones,and cameras use wireless technology(Infrared,Bluetooth,IrDA,IEEE 802.11,etc.)to establish multiple interdevice connections simultaneously.With the flexibility of the wireless network,one can set up numerous ad-hoc networks on-demand,connecting hundreds to thousands of users,increasing productivity and profitability significantly.However,the number of network attacks in wireless networks that exploit such flexibilities in setting and tearing down networks has become very alarming.Perpetrators can launch attacks since there is no first line of defense in an ad hoc network setup besides the standard IEEE802.11 WPA2 authentication.One feasible countermeasure is to deploy intrusion detection systems at the edge of these ad hoc networks(Network-based IDS)or at the node level(Host-based IDS).The challenge here is that there is no readily available benchmark data available for IoT network traffic.Creating this benchmark data is very tedious as IoT can work on multiple platforms and networks,and crafting and labelling such dataset is very labor-intensive.This research aims to study the characteristics of existing datasets available such as KDD-Cup and NSL-KDD,and their suitability for wireless IDS implementation.We hypothesize that network features are parametrically different depending on the types of network and assigning weight dynamically to these features can potentially improve the subsequent threat classifications.This paper analyses packet and flow features for the data packet captured on a wireless network rather than a wired network.Combining domain heuristcs and early classification results,the paper had identified 19 header fields exclusive to wireless network that contain high information gain to be used as ML features in Wireless IDS. | Vasaki Ponnusamy Aun Yichiet NZ Jhanjhi Mamoona humayun MaramFahhad Almufareh | 2022 | Computer Systems Science & Engineering2022，40，3： | 0 |
| 8 | Prediction Model for Coronavirus Pandemic Using Deep Learning显示文摘The recent global outbreak of COVID-19 damaged the world health systems,human health,economy,and daily life badly.None of the countries was ready to face this emerging health challenge.Health professionals were not able to predict its rise and next move,as well as the future curve and impact on lives in case of a similar pandemic situation happened.This created huge chaos globally,for longer and the world is still struggling to come up with any suitable solution.Here the better use of advanced technologies,such as artificial intelligence and deep learning,may aid healthcare practitioners in making reliable COVID-19 diagnoses.The proposed research would provide a prediction model that would use Artificial Intelligence and Deep Learning to improve the diagnostic process by reducing unreliable diagnostic interpretation of chest CT scans and allowing clinicians to accurately discriminate between patients who are sick with COVID-19 or pneumonia,and also empowering health professionals to distinguish chest CT scans of healthy people.The efforts done by the Saudi government for the management and control of COVID-19 are remarkable,however;there is a need to improve the diagnostics process for better perception.We used a data set from Saudi regions to build a prediction model that can help distinguish between COVID-19 cases and regular cases from CT scans.The proposed methodology was compared to current models and found to be more accurate(93 percent)than the existing methods. | Mamoona Humayun Ahmed Alsayat | 2022 | Computer Systems Science & Engineering2022，40，3： | 0 |
| 9 | Smart-City-based Data Fusion Algorithm for Internet of Things显示文摘Increasingly,Wireless Sensor Networks(WSNs)are contributing enormous amounts of data.Since the recent deployments of wireless sensor networks in Smart City infrastructures,significant volumes of data have been produced every day in several domains ranging from the environment to the healthcare system to transportation.Using wireless sensor nodes,a Smart City environment may now be shown for the benefit of residents.The Smart City delivers intelligent infrastructure and a stimulating environment to citizens of the Smart Society,including the elderly and others.Weak,Quality of Service(QoS)and poor data performance are common problems in WSNs,caused by the data fusion method,where a small amount of bad data can significantly impact the total fusion outcome.In our proposed research,a WSN multisensor data fusion technique employing fuzzy logic for event detection.Using the new proposed Algorithm,sensor nodes will collect less repeated data,and redundant data will be used to increase the data’s overall reliability.The network’s fusion delay problem is investigated,and a minimum fusion delay approach is provided based on the nodes’fusion waiting time.The proposed algorithm performs well in fusion,according to the results of the experiment.As a result of these discoveries,It is concluded that the algorithm describe here is effective and dependable instrument with a wide range of applications. | Jawad Khan Muhammad Amir Khan N.Z.Jhanjhi Mamoona Humayun Abdullah Alourani | 2022 | Computers, Materials & Continua2022，，11： | 0 |
| 10 | A Compact Rhombus Shaped Antenna with Extended Stubs for Ultra-Wideband Applications显示文摘Ultra-wideband(UWB)is highly preferred for short distance communication.As a result of this significance,this project targets the design of a compact UWB antennas.This paper describes a printed UWB rhombusshaped antenna with a partial ground plane.To achieve wideband response,two stubs and a notch are incorporated at both sides of the rhombus design and ground plane respectively.To excite the antenna,a simple microstrip feed line is employed.The suggested antenna is built on a 1.6 mm thick FR4 substrate.The proposed design is very compact with overall electrical size of 0.18λ×0.25λ(14×18 mm2).The rhombus shaped antenna covers frequency ranging from 3.5 to 11 GHz with 7.5 GHz impedance bandwidth.The proposed design simulated and measured bandwidths are 83.33%and 80%,respectively.Radiation pattern in terms of E-field and H-field are discussed at 4,5.5 and 10 GHz respectively.The proposed design has 65%radiation efficiency and 1.5 dBi peak gain.The proposed design is simulated in CST(Computer Simulation Technology)simulator and the simulated design is fabricated for the measured results.The simulated and measured findings are in close resemblance.The obtained results confirm the application of the proposed design for the ultra-wide band applications. | Syed Misbah un Noor Muhammad Amir Khan Shahid Khan NZ Jhanjhi Mamoona Humayun Hesham A.Alhumyan | 2022 | Computers, Materials & Continua2022，，11： | 0 |
| 11 | A Monte Carlo Based COVID-19 Detection Framework for Smart Healthcare显示文摘COVID-19 is a novel coronavirus disease that has been declared as a global pandemic in 2019.It affects the whole world through personto-person communication.This virus spreads by the droplets of coughs and sneezing,which are quickly falling over the surface.Therefore,anyone can get easily affected by breathing in the vicinity of the COVID-19 patient.Currently,vaccine for the disease is under clinical investigation in different pharmaceutical companies.Until now,multiple medical companies have delivered health monitoring kits.However,a wireless body area network(WBAN)is a healthcare system that consists of nano sensors used to detect the real-time health condition of the patient.The proposed approach delineates is to fill a gap between recent technology trends and healthcare structure.If COVID-19 affected patient is monitored through WBAN sensors and network,a physician or a doctor can guide the patient at the right timewith the correct possible decision.This scenario helps the community to maintain social distancing and avoids an unpleasant environment for hospitalized patients Herein,a Monte Carlo algorithm guided protocol is developed to probe a secured cipher output.Security cipher helps to avoid wireless network issues like packet loss,network attacks,network interference,and routing problems.Monte Carlo based covid-19 detection technique gives 90%better results in terms of time complexity,performance,and efficiency.Results indicate that Monte Carlo based covid-19 detection technique with edge computing idea is robust in terms of time complexity,performance,and efficiency and thus,is advocated as a significant application for lessening hospital expenses. | Tallat Jabeen Ishrat Jabeen Humaira Ashraf Nz Jhanjhi Mamoona Humayun Mehedi Masud Sultan Aljahdali | 2022 | Computers, Materials & Continua2022，，2： | 0 |
| 12 | Machine Learning Enabled Early Detection of Breast Cancer by Structural Analysis of Mammograms显示文摘Clinical image processing plays a signicant role in healthcare systems and is currently a widely used methodology.In carcinogenic diseases,time is crucial;thus,an image’s accurate analysis can help treat disease at an early stage.Ductal carcinoma in situ(DCIS)and lobular carcinoma in situ(LCIS)are common types of malignancies that affect both women and men.The number of cases of DCIS and LCIS has increased every year since 2002,while it still takes a considerable amount of time to recommend a controlling technique.Image processing is a powerful technique to analyze preprocessed images to retrieve useful information by using some remarkable processing operations.In this paper,we used a dataset from the Mammographic Image Analysis Society and MATLAB 2019b software from MathWorks to simulate and extract our results.In this proposed study,mammograms are primarily used to diagnose,more precisely,the breast’s tumor component.The detection of DCIS and LCIS on breast mammograms is done by preprocessing the images using contrast-limited adaptive histogram equalization.The resulting images’tumor portions are then isolated by a segmentation process,such as threshold detection.Furthermore,morphological operations,such as erosion and dilation,are applied to the images,then a gray-level co-occurrence matrix texture features,Harlick texture features,and shape features are extracted from the regions of interest.For classication purposes,a support vector machine(SVM)classier is used to categorize normal and abnormal patterns.Finally,the adaptive neuro-fuzzy inference system is deployed for the amputation of fuzziness due to overlapping features of patterns within the images,and the exact categorization of prior patterns is gained through the SVM.Early detection of DCIS and LCIS can save lives and help physicians and surgeons todiagnose and treat these diseases.Substantial results are obtained through cubic support vector machine(CSVM),respectively,showing 98.95%and 98.01%accuracies for normal and abnormal mammograms.Through ANFIS,promising results of mean square error(MSE)0.01866,0.18397,and 0.19640 for DCIS and LCIS differentiation during the training,testing,and checking phases. | Mavra Mehmood Ember Ayub Fahad Ahmad Madallah Alruwaili Ziyad AAlrowaili Saad Alanazi Mamoona Humayun Muhammad Rizwan Shahid Naseem Tahir Alyas | 2021 | Computers, Materials & Continua2021，，4： | 0 |
| 13 | Security Threat and Vulnerability Assessment and Measurement in Secure Software Development显示文摘Security is critical to the success of software,particularly in today’s fast-paced,technology-driven environment.It ensures that data,code,and services maintain their CIA(Confidentiality,Integrity,and Availability).This is only possible if security is taken into account at all stages of the SDLC(Software Development Life Cycle).Various approaches to software quality have been developed,such as CMMI(Capabilitymaturitymodel integration).However,there exists no explicit solution for incorporating security into all phases of SDLC.One of the major causes of pervasive vulnerabilities is a failure to prioritize security.Even the most proactive companies use the“patch and penetrate”strategy,inwhich security is accessed once the job is completed.Increased cost,time overrun,not integrating testing and input in SDLC,usage of third-party tools and components,and lack of knowledge are all reasons for not paying attention to the security angle during the SDLC,despite the fact that secure software development is essential for business continuity and survival in today’s ICT world.There is a need to implement best practices in SDLC to address security at all levels.To fill this gap,we have provided a detailed overview of secure software development practices while taking care of project costs and deadlines.We proposed a secure SDLC framework based on the identified practices,which integrates the best security practices in various SDLC phases.A mathematical model is used to validate the proposed framework.A case study and findings show that the proposed system aids in the integration of security best practices into the overall SDLC,resulting in more secure applications. | Mamoona Humayun NZ Jhanjhi Maram Fahhad Almufareh Muhammad Ibrahim Khalil | 2022 | Computers, Materials & Continua2022，，6： | 0 |
| 14 | Multi-Scale Network for Thoracic Organs Segmentation显示文摘Medical Imaging Segmentation is an essential technique for modern medical applications.It is the foundation of many aspects of clinical diagnosis,oncology,and computer-integrated surgical intervention.Although significant successes have been achieved in the segmentation of medical images,DL(deep learning)approaches.Manual delineation of OARs(organs at risk)is vastly dominant but it is prone to errors given the complex irregularities in shape,low texture diversity between tissues and adjacent blood area,patientwide location of organisms,and weak soft tissue contrast across adjacent organs in CT images.Till now several models have been implemented onmulti organs segmentation but not caters to the problemof imbalanced classes some organs have relatively small pixels as compared to others.To segment OARs in thoracic CT images,we proposed the model based on the encoder-decoder approach using transfer learning with the efficientnetB7 DL model.We have built a fully connected CNN(Convolutional Neural network)having 5 layers of encoding and 5 layers of decoding with efficientnetB7 specifically to tackle imbalance class pixels in an accurate way for the segmentation of OARs.Proposed methodology achieves 0.93405 IOU score,0.95138 F1 score and class-wise dice score for esophagus 0.92466,trachea 0.94257,heart 0.95038,aorta 0.9351 and background 0.99891.The results showed that our proposed framework can be segmented organs accurately. | Muhammad Ibrahim Khalil Samabia Tehsin Mamoona Humayun N.Z Jhanjhi Mohammed A.AlZain | 2022 | Computers, Materials & Continua2022，，2： | 0 |
| 15 | A Novel Machine Learning-Based Hand Gesture Recognition Using HCI on IoT Assisted Cloud Platform显示文摘Machine learning is a technique for analyzing data that aids the construction of mathematical models.Because of the growth of the Internet of Things(IoT)and wearable sensor devices,gesture interfaces are becoming a more natural and expedient human-machine interaction method.This type of artificial intelligence that requires minimal or no direct human intervention in decision-making is predicated on the ability of intelligent systems to self-train and detect patterns.The rise of touch-free applications and the number of deaf people have increased the significance of hand gesture recognition.Potential applications of hand gesture recognition research span from online gaming to surgical robotics.The location of the hands,the alignment of the fingers,and the hand-to-body posture are the fundamental components of hierarchical emotions in gestures.Linguistic gestures may be difficult to distinguish from nonsensical motions in the field of gesture recognition.Linguistic gestures may be difficult to distinguish from nonsensical motions in the field of gesture recognition.In this scenario,it may be difficult to overcome segmentation uncertainty caused by accidental hand motions or trembling.When a user performs the same dynamic gesture,the hand shapes and speeds of each user,as well as those often generated by the same user,vary.A machine-learning-based Gesture Recognition Framework(ML-GRF)for recognizing the beginning and end of a gesture sequence in a continuous stream of data is suggested to solve the problem of distinguishing between meaningful dynamic gestures and scattered generation.We have recommended using a similarity matching-based gesture classification approach to reduce the overall computing cost associated with identifying actions,and we have shown how an efficient feature extraction method can be used to reduce the thousands of single gesture information to four binary digit gesture codes.The findings from the simulation support the accuracy,precision,gesture recognition,sensitivity,and efficiency rates.The Machine Learning-based Gesture Recognition Framework(ML-GRF)had an accuracy rate of 98.97%,a precision rate of 97.65%,a gesture recognition rate of 98.04%,a sensitivity rate of 96.99%,and an efficiency rate of 95.12%. | Saurabh Adhikari Tushar Kanti Gangopadhayay Souvik Pal D.Akila Mamoona Humayun Majed Alfayad N.Z.Jhanjhi | 2023 | Computer Systems Science & Engineering2023，46，8： | 0 |
| 16 | Robust Cluster-Based Routing Protocol for IoT-Assisted Smart Devices in WSN显示文摘The Internet of Things(IoT)is gaining attention because of its broad applicability,especially by integrating smart devices for massive communication during sensing tasks.IoT-assisted Wireless Sensor Networks(WSN)are suitable for various applications like industrial monitoring,agriculture,and transportation.In this regard,routing is challenging to nd an efcient path using smart devices for transmitting the packets towards big data repositories while ensuring efcient energy utilization.This paper presents the Robust Cluster Based Routing Protocol(RCBRP)to identify the routing paths where less energy is consumed to enhances the network lifespan.The scheme is presented in six phases to explore ow and communication.We propose the two algorithms:(i)energy-efcient clustering and routing algorithm and (ii)distance and energy consumption calculation algorithm.The scheme consumes less energy and balances the load by clustering the smart devices.Our work is validated through extensive simulation using Matlab.Results elucidate the dominance of the proposed scheme is compared to counterparts in terms of energy consumption,the number of packets received at BS and the number of active and dead nodes.In the future,we shall consider edge computing to analyze the performance of robust clustering. | Maryam Shaq Humaira Ashraf Ata Ullah Mehedi Masud Muhammad Azeem N.Z.Jhanjhi Mamoona Humayun | 2021 | Computers, Materials & Continua2021，，6： | 0 |
| 17 | Machine Learning Empowered Security Management and Quality of Service Provision in SDN-NFV Environment显示文摘With the rising demand for data access,network service providers face the challenge of growing their capital and operating costs while at the same time enhancing network capacity and meeting the increased demand for access.To increase efficacy of Software Defined Network(SDN)and Network Function Virtualization(NFV)framework,we need to eradicate network security configuration errors that may create vulnerabilities to affect overall efficiency,reduce network performance,and increase maintenance cost.The existing frameworks lack in security,and computer systems face few abnormalities,which prompts the need for different recognition and mitigation methods to keep the system in the operational state proactively.The fundamental concept behind SDN-NFV is the encroachment from specific resource execution to the programming-based structure.This research is around the combination of SDN and NFV for rational decision making to control and monitor traffic in the virtualized environment.The combination is often seen as an extra burden in terms of resources usage in a heterogeneous network environment,but as well as it provides the solution for critical problems specially regarding massive network traffic issues.The attacks have been expanding step by step;therefore,it is hard to recognize and protect by conventional methods.To overcome these issues,there must be an autonomous system to recognize and characterize the network traffic’s abnormal conduct if there is any.Only four types of assaults,including HTTP Flood,UDP Flood,Smurf Flood,and SiDDoS Flood,are considered in the identified dataset,to optimize the stability of the SDN-NFVenvironment and security management,through several machine learning based characterization techniques like Support Vector Machine(SVM),K-Nearest Neighbors(KNN),Logistic Regression(LR)and Isolation Forest(IF).Python is used for simulation purposes,including several valuable utilities like the mine package,the open-source Python ML libraries Scikit-learn,NumPy,SciPy,Matplotlib.Few Flood assaults and Structured Query Language(SQL)injections anomalies are validated and effectively-identified through the anticipated procedure.The classification results are promising and show that overall accuracy lies between 87%to 95%for SVM,LR,KNN,and IF classifiers in the scrutiny of traffic,whether the network traffic is normal or anomalous in the SDN-NFV environment. | Shumaila Shahzadi Fahad Ahmad Asma Basharat Madallah Alruwaili Saad Alanazi Mamoona Humayun Muhammad Rizwan Shahid Naseem | 2021 | Computers, Materials & Continua2021，，3： | 0 |
| 18 | Energy Optimised Security against Wormhole Attack in IoT-Based Wireless Sensor Networks显示文摘An IoT-based wireless sensor network(WSN)comprises many small sensors to collect the data and share it with the central repositories.These sensors are battery-driven and resource-restrained devices that consume most of the energy in sensing or collecting the data and transmitting it.During data sharing,security is an important concern in such networks as they are prone to many threats,of which the deadliest is the wormhole attack.These attacks are launched without acquiring the vital information of the network and they highly compromise the communication,security,and performance of the network.In the IoT-based network environment,its mitigation becomes more challenging because of the low resource availability in the sensing devices.We have performed an extensive literature study of the existing techniques against the wormhole attack and categorised them according to their methodology.The analysis of literature has motivated our research.In this paper,we developed the ESWI technique for detecting the wormhole attack while improving the performance and security.This algorithm has been designed to be simple and less complicated to avoid the overheads and the drainage of energy in its operation.The simulation results of our technique show competitive results for the detection rate and packet delivery ratio.It also gives an increased throughput,a decreased end-to-end delay,and a much-reduced consumption of energy. | Hafsa Shahid Humaira Ashraf Hafsa Javed Mamoona Humayun Nz Jhanjhi Mohammed A.AlZain | 2021 | Computers, Materials & Continua2021，，8： | 0 |
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