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Security Monitoring and Management for the Network Services in the Orchestration of SDN-NFV Environment Using Machine Learning Techniques

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Computer Systems Science & Engineering 2024年第2期48卷 363-394页

作者： Nasser Alshammari Shumaila Shahzadi saad Awadh alanazi Shahid Naseem Muhammad Anwar Madallah Alruwaili Muhammad Rizwan Abid Omar Alruwaili Ahmed Alsayat Fahad Ahmad Department of Computer Science College of Computer and Information SciencesJouf UniversitySakakaAl Jouf72341Saudi Arabia Department of Computer Sciences Kinnaird College forWomenLahorePunjab54700Pakistan Department of Information Sciences Division of Sciences and TechnologyUniversity of EducationLahore54770Pakistan Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySakaka72341Saudi Arabia Computer Science Florida Polytechnic UniversityLakelandFlorida33805USA Delta3T LahorePunjab54700Pakistan Department of Basic Sciences Common First YearJouf UniversitySakaka72341Saudi Arabia

Software Defined Network(SDN)and Network Function Virtualization(NFV)technology promote several benefits to network operators,including reduced maintenance costs,increased network operational performance,simplified network lifecycle,and policies *** vulnerabilities try to modify services provided by Network Function Virtualization MANagement and Orchestration(NFV MANO),and malicious attacks in different scenarios disrupt the NFV Orchestrator(NFVO)and Virtualized Infrastructure Manager(VIM)lifecycle management related to network services or individual Virtualized Network Function(VNF).This paper proposes an anomaly detection mechanism that monitors threats in NFV MANO and manages promptly and adaptively to implement and handle security functions in order to enhance the quality of experience for end *** anomaly detector investigates these identified risks and provides secure network *** enables virtual network security functions and identifies anomalies in Kubernetes(a cloud-based platform).For training and testing purpose of the proposed approach,an intrusion-containing dataset is used that hold multiple malicious activities like a Smurf,Neptune,Teardrop,Pod,Land,IPsweep,etc.,categorized as Probing(Prob),Denial of Service(DoS),User to Root(U2R),and Remote to User(R2L)*** anomaly detector is anticipated with the capabilities of a Machine Learning(ML)technique,making use of supervised learning techniques like Logistic Regression(LR),Support Vector Machine(SVM),Random Forest(RF),Naïve Bayes(NB),and Extreme Gradient Boosting(XGBoost).The proposed framework has been evaluated by deploying the identified ML algorithm on a Jupyter notebook in Kubeflow to simulate Kubernetes for validation *** classifier has shown better outcomes(99.90%accuracy)than other classifiers in detecting anomalies/intrusions in the containerized environment.

关键词： Software defined network network function virtualization network function virtualization management and orchestration virtual infrastructure manager virtual network function Kubernetes Kubectl artificial intelligence machine learning

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Melanoma Identification Through X-ray Modality Using Inception-v3 Based Convolutional Neural Network

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Computers, Materials & Continua 2022年第7期72卷 37-55页

作者： saad Awadh alanazi College of Computer and Information Sciences Jouf UniversitySakakaAljouf72341Saudi Arabia

Melanoma,also called malignant melanoma,is a form of skin cancer triggered by an abnormal proliferation of the pigment-producing cells,which give the skin its *** is one of the skin diseases,which is exceptionally and globally dangerous,Skin lesions are considered to be a serious ***-based early recognition and detection procedure is fundamental for melanoma *** detection of melanoma using dermoscopy images improves survival rates *** the same time,well-experienced dermatologists dominate the precision of ***,precise melanoma recognition is incredibly hard due to several factors:low contrast between lesions and surrounding skin,visual similarity between melanoma and non-melanoma lesions,and so ***,reliable automatic detection of skin tumors is critical for pathologists’effectiveness and *** take care of this issue,numerous research centers around the world are creating autonomous image processing-oriented *** suggested deep learning methods in this article to address significant tasks that have emerged in the field of skin lesion image processing:we provided a Convolutional Neural Network(CNN)based framework using an Inception-v3(INCP-v3)melanoma detection scheme and accomplished very high precision(98.96%)against melanoma *** classification framework of CNN is created utilizing TensorFlow and Keras in the backend(in Python).It likewise utilizes Transfer-Learning(TL)*** is prepared on the data gathered from the“International Skin Imaging Collaboration(ISIC)”*** experiments show that the suggested technique outperforms state-of-the-art methods in terms of predictive performance.

关键词： Deep learning chronic obstructive pulmonary disease chronic bronchitis convolutional neural network X-ray images

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Artificial Fish Swarm for Multi Protein Sequences Alignment in Bioinformatics

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Computers, Materials & Continua 2022年第9期72卷 6091-6106页

作者： Medhat A.Tawfeek saad alanazi A.A.Abd El-Aziz Department of Computer Science College of Computer and Information SciencesJouf UniversitySaudi Arabia(KSA Department of Computer Science Faculty of Computers and InformationMenoufia UniversityEgypt Department of Information Systems College of Computer and Information SciencesJouf UniversitySaudi Arabia(KSA) Department of Information Systems and Technology Faculty of Graduates Studies and ResearchCairo UniversityEgypt

The alignment operation between many protein sequences or DNAsequences related to the scientific bioinformatics application is very *** is a trade-off in the objectives in the existing techniques of MultipleSequence Alignment (MSA). The techniques that concern with speed ignoreaccuracy, whereas techniques that concern with accuracy ignore speed. Theterm alignment means to get the similarity in different sequences with highaccuracy. The more growing number of sequences leads to a very complexand complicated problem. Because of the emergence;rapid development;anddependence on gene sequencing, sequence alignment has become importantin every biological relationship analysis process. Calculating the numberof similar amino acids is the primary method for proving that there is arelationship between two sequences. The time is a main issue in any alignmenttechnique. In this paper, a more effective MSA method for handling themassive multiple protein sequences alignment maintaining the highest accuracy with less time consumption is proposed. The proposed method dependson Artificial Fish Swarm (AFS) algorithm that can break down the mostchallenges of MSA problems. The AFS is exploited to obtain high accuracyin adequate time. ASF has been increasing popularly in various applicationssuch as artificial intelligence, computer vision, machine learning, and dataintensive application. It basically mimics the behavior of fish trying to getthe food in nature. The proposed mechanisms of AFS that is like preying,swarming, following, moving, and leaping help in increasing the accuracy andconcerning the speed by decreasing execution time. The sense organs that aidthe artificial fishes to collect information and vision from the environmenthelp in concerning the accuracy. These features of the proposed AFS make thealignment operation more efficient and are suitable especially for large-scaledata. The implementation and experimental results put the proposed AFS as afirst choice in th

关键词： Multiple sequence alignment swarm intelligence artificial fish swarm protein sequences

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A Template Matching Based Feature Extraction for Activity Recognition

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Computers, Materials & Continua 2022年第7期72卷 611-634页

作者： Muhammad Hameed Siddiqi Helal Alshammari Amjad Ali Madallah Alruwaili Yousef Alhwaiti saad alanazi M.M.Kamruzzaman College of Computer and Information Sciences Jouf UniversitySakakaAljouf2014Kingdom of Saudi Arabia Department of Computer Science COMSATS University IslamabadLahore CampusPakistan

Human activity recognition(HAR)can play a vital role in the monitoring of human activities,particularly for healthcare conscious *** accuracy of HAR systems is completely reliant on the extraction of prominent *** methods find it very challenging to extract optimal features due to the dynamic nature of activities,thereby reducing recognition *** this paper,we propose a robust feature extraction method for HAR systems based on template ***,in this method,we want to associate a template of an activity frame or sub-frame comprising the corresponding *** this regard,the template is placed on the frame pixels to calculate the equivalent number of pixels in the template correspondent those in the *** process is replicated for the whole frame,and the pixel is directed to the optimum *** best count is estimated to be the pixel where the silhouette(provided via the template)presented inside the *** this way,the feature vector is *** feature vector generation,the hiddenMarkovmodel(HMM)has been utilized to label the incoming *** utilized different publicly available standard datasets for *** proposed method achieved the best accuracy against existing state-of-the-art systems.

关键词： Activity recognition feature extraction template matching video surveillance

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Personality Detection Using Context Based Emotions in Cognitive Agents

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Computers, Materials & Continua 2022年第3期70卷 4947-4964页

作者： Nouh Sabri Elmitwally Asma Kanwal Sagheer Abbas Muhammad A.Khan Muhammad Adnan Khan Munir Ahmad saad alanazi College of Computer and Information Sciences Jouf UniversitySakaka72341Saudi Arabia Department of Computer Science Faculty of Computers and Artificial IntelligenceCairo University12613Egyp t 3School of Computer Science National College of Business administration and EconomicsLahore54000Pakistan Department of Computer Science GC UniversityLahorePakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnam13120Korea

Detection of personality using emotions is a research domain in artificial *** present,some agents can keep the human’s profile for interaction and adapts themselves according to their ***,the effective method for interaction is to detect the person’s personality by understanding the emotions and context of the *** idea behind adding personality in cognitive agents begins an attempt to maximize adaptability on the basis of *** our daily life,humans socially interact with each other by analyzing the emotions and context of interaction from audio or visual *** paper presents a conceptual personality model in cognitive agents that can determine personality and behavior based on some text input,using the context subjectivity of the given data and emotions obtained from a particular situation/*** proposed work consists of Jumbo Chatbot,which can chat with *** this social interaction,the chatbot predicts human personality by understanding the emotions and context of interactive ***,the Jumbo chatbot is using the BFI technique to interact with a *** accuracy of proposed work varies and improve through getting more experiences of interaction.

关键词： Emotions fuzzy personality detection contextual analysis semantic analysis

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Performance Analysis of Intelligent Neural-Based Deep Learning System on Rank Images Classification

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Computer Systems Science & Engineering 2023年第11期47卷 2219-2239页

作者： Muhammad Hameed Siddiqi Asfandyar Khan Muhammad Bilal Khan Abdullah Khan Madallah Alruwaili saad alanazi College of Computer and Information Sciences Jouf UniversitySakakaAljouf73211Kingdom of Saudi Arabia Institute of Computer Science and Information Technology ICS/IT FMCSUniversity of AgriculturePeshawar25000Pakistan

The use of the internet is increasing all over the world on a daily basis in the last two *** increase in the internet causes many sexual crimes,such as sexual misuse,domestic violence,and child *** research has been done for pornographic image detection and *** of the used models used machine learning techniques and deep learning models which show less accuracy,while the deep learning model ware used for classification and detection performed better as compared to machine ***,this research evaluates the performance analysis of intelligent neural-based deep learning models which are based on Convolution neural network(CNN),Visual geometry group(VGG-16),VGG-14,and Residual Network(ResNet-50)with the expanded dataset,trained using transfer learning approaches applied in the fully connected layer for datasets to classify rank(Pornographic ***)classification in *** simulation result shows that VGG-16 performed better than the used model in this study without augmented *** VGG-16 model with augmented data reached a training and validation accuracy of 0.97,0.94 with a loss of 0.070,*** precision,recall,and f-measure values for explicit and non-explicit images are(0.94,0.94,0.94)and(0.94,0.94,0.94).Similarly,The VGG-14 model with augmented data reached a training and validation accuracy of 0.98,0.96 with a loss of 0.059,*** f-measure,recall,and precision values for explicit and non-explicit images are(0.98,0.98,0.98)and(0.98,0.98,0.98).The CNN model with augmented data reached a training and validation accuracy of 0.776&0.78 with losses of 0.48&*** f-measure,recall,and precision values for explicit and non-explicit images are(0.80,0.80,0.80)and(0.78,0.79,0.78).The ResNet-50 model with expanded data reached with training accuracy of 0.89 with a loss of 0.389 and 0.86 of validation accuracy and a loss of *** f-measure,recall,and precision values for explicit and non-explicit

关键词： VGG-16 VGG-14 pornography detection expansion ResNet-50 convolution neural network(CNN) machine learning

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A Health Monitoring System Using IoT-Based Android Mobile Application

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Computer Systems Science & Engineering 2023年第11期47卷 2293-2311页

作者： Madallah Alruwaili Muhammad Hameed Siddiqi Kamran Farid Muhammad Azad saad alanazi Asfandyar Khan Abdullah Khan College of Computer and Information Sciences Jouf UniversitySakakaAl-Jouf73211Kingdom of Saudi Arabia Institute of Computer Science and Information Technology The University of Agriculture PeshawarPeshawar25000Pakistan

Numerous types of research on healthcare monitoring systems have been conducted for calculating heart rate,ECG,nasal/oral airflow,temperature,light sensor,and fall detection *** researchers have done different work in the field of health monitoring with sensor *** researchers used built-in apps,such as some used a small number of parameters,while some other studies used more than one microcontroller and used senders and receivers among the microcontrollers to communicate,and outdated tools for study *** no efficient,cheap,and updated work is proposed in the field of sensor-based health monitoring ***,this study developed an android-based mobile system that can remotely monitor electrocardiograms(ECGs),pulse oximetry,heart rate,and body *** microcontroller’s Wi-Fi device is used to manage wireless data *** findings of the patient are saved on the Firebase server for further usage in the mobile *** performance of the proposed device is tested on ten numbers of different patients age-wise in terms of beats per minute(BPM),ECG,Temperature,and *** system uses temperature,pulse,ECG,blood pressure,and eye blink *** device makes the usage of a tiny pulse sensor that has been designed to provide an accurate and optimal readout of the pulse rate and a temperature sensor is also *** the help of an MCU,our system measures the pulse rate in beats per minute(bpm),blood oxygen level temperature measurements,and ECG readings and communicates this information to the Firebase *** check the performance of the proposed system first,the BPM parameter was checked on the cardiac ***,the proposed model is tested on different patients *** simulation result shows that the BPM reading is not much different than the BPM of the cardiac *** to the simulation findings,the proposed model achieved the best performance as compared to commercially available

关键词： ICUs DS18B20 sensors SpO2 AD8232 sensors ECGs Internet of Things

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Applying and Comparison of Chaotic-Based Permutation Algorithms for Audio Encryption

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Computers, Materials & Continua 2021年第6期67卷 3161-3176页

作者： Osama M.Abu Zaid Medhat A.Tawfeek saad alanazi Department of Computer Science Faculty of Computers and InformationKafrelsheikh UniversityEgypt Department of Computer Science College of Computer and Information SciencesJouf UniversityKSA Department of Computer Science Faculty of Computers and InformationMenoua UniversityEgypt

This research presents,and claries the application of two permutation algorithms,based on chaotic map systems,and applied to a le of speech *** are the Arnold cat map-based permutation algorithm,and the Baker’s chaotic map-based permutation *** algorithms are implemented on the same speech signal ***,both the premier and the encrypted le histograms are documented and *** speech signal amplitude values with time signals of the original le are recorded and plotted against the encrypted and decrypted ***,the original le is plotted against the encrypted le,using the spectrogram frequencies of speech signals with the signal *** permutation algorithms are used to shufe the positions of the speech les signals’values without any changes,to produce an encrypted speech le.A comparative analysis is introduced by using some of sundry statistical and experimental analyses for the procedures of encryption and decryption,e.g.,the time of both procedures,the encrypted audio signals histogram,the correlation coefcient between specimens in the premier and encrypted signals,a test of the Spectral Distortion(SD),and the Log-Likelihood Ratio(LLR)*** outcomes of the different experimental and comparative studies demonstrate that the two permutation algorithms(Baker and Arnold)are sufcient for providing an efcient and reliable voice signal encryption ***,the Arnold’s algorithm gives better results in most cases as compared to the results of Baker’s algorithm.

关键词： Arnold’s cat map chaotic maps permutation algorithms speech encryption Baker’s chaotic map

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Cloud-Based Diabetes Decision Support System Using Machine Learning Fusion

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Computers, Materials & Continua 2021年第7期68卷 1341-1357页

作者： Shabib Aftab saad alanazi Munir Ahmad Muhammad Adnan Khan Areej Fatima Nouh Sabri Elmitwally School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Department of Computer Science Virtual University of PakistanLahore54000Pakistan College of Computer and Information Sciences Jouf UniversitySakaka72341Saudi Arabia Riphah School of Computing&Innovation Riphah International UniversityLahore CampusLahore54000Pakistan Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Department of Computer Science Faculty of Computers and Articial IntelligenceCairo University12613Egypt

Diabetes mellitus,generally known as diabetes,is one of the most common diseases *** is a metabolic disease characterized by insulin deciency,or glucose(blood sugar)levels that exceed 200 mg/dL(11.1 ml/L)for prolonged periods,and may lead to death if left uncontrolled by medication or insulin *** is categorized into two main types—type 1 and type 2—both of which feature glucose levels above“normal,”dened as 140 mg/*** is triggered by malfunction of the pancreas,which releases insulin,a natural hormone responsible for controlling glucose levels in blood *** and comprehensive analysis of this potentially fatal disease necessitate application of techniques with minimal rates of *** primary purpose of this research study is to assess the potential role of machine learning in predicting a person’s risk of developing ***,research has supported the use of various machine algorithms,such as naïve Bayes,decision trees,and articial neural networks,for early diagnosis of ***,to achieve maximum accuracy and minimal error in diagnostic predictions,there remains an immense need for further research and innovation to improve the machine-learning tools and techniques available to healthcare ***,in this paper,we propose a novel cloud-based machine-learning fusion technique involving synthesis of three machine algorithms and use of fuzzy systems for collective generation of highly accurate nal decisions regarding early diagnosis of diabetes.

关键词： Machine learning fusion articial neural network decision trees naïve Bayes diabetes prediction

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Intelligent Ammunition Detection and Classification System Using Convolutional Neural Network

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Computers, Materials & Continua 2021年第5期67卷 2585-2600页

作者： Gulzar Ahmad saad alanazi Madallah Alruwaili Fahad Ahmad Muhammad Adnan Khan Sagheer Abbas Nadia Tabassum School of Computer Science NCBA&ELahore54000Pakistan College of Computer and Information Sciences Jouf UniversitySakakaAljouf72341Saudi Arabia Department of Basic Sciences Jouf UniversitySakakaAljouf72341Saudi Arabia Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Computer Science Virtual University of PakistanLahore54000Pakistan Department of Computer Sciences Kinnaird College for WomenLahore54000Pakistan

Security is a significant issue for everyone due to new and creative ways to commit *** Closed-Circuit Television(CCTV)systems are being installed in offices,houses,shopping malls,and on streets to protect *** monitor CCTV;however,it is difficult for a single person to monitor the actions of multiple people at one ***,there is a dire need for an automated monitoring system that detects a person with ammunition or any other harmful material Based on our research and findings of this study,we have designed a new Intelligent Ammunition Detection and Classification(IADC)system using Convolutional Neural Network(CNN).The proposed system is designed to identify persons carrying weapons and ammunition using CCTV *** weapons are identified,the cameras sound an *** the proposed IADC system,CNN was used to detect firearms and *** CNN model which is a Deep Learning technique consists of neural networks,most commonly applied to analyzing visual imagery has gained popularity for unstructured(images,videos)data ***,this system generates an early warning through detection of ammunition before conditions become *** the faster and earlier the prediction,the lower the response time,loses and potential *** proposed IADC system provides better results than earlier published models like VGGNet,OverFeat-1,OverFeat-2,and OverFeat-3.

关键词： CCTV CNN IADC deep learning intelligent ammunition detection DnCNN

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