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An overview of cyber security data science from a perspective of machine learning

Machine learning (ML) is sometimes regarded as a subset of u201cArtificial Intelligence,u201d and it is strongly related to data science, data mining, and computational statistics. t<br><br>For #Enquiry:tt<br>Website: https://www.phdassistance.com/blog/an-overview-of-cyber-security-data-science-from-a-perspective-of-machine-learning/<br>India: 91 91769 66446tt<br>Email: info@phdassistance.com

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An overview of cyber security data science from a perspective of machine learning

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  1. AnOverviewofCyber SecurityDataScience fromaPerspectiveof MachineLearning Copyright©2022 PhdAssistance.Allrights reserved Journalsupport |Dissertation support| Analysis |Data collection| Coding& Algorithms |Editing &Peer- Reviewing

  2. Copyright©2022 PhdAssistance.Allrights reserved Introduction Machinelearningtasksincyber security Supervisedlearning Unsupervisedlearning Neuralnetworksanddeep learning Conclusionandfuturework Today Discussion Journalsupport |Dissertation support| Analysis |Data collection| Coding& Algorithms |Editing &Peer- Reviewing

  3. Introduction Theinformationandcommunicationtechnology(ICT)sectorhas advanced significantly over the past fifty years and is now pervasiveandtightlyintertwinedwithourcontemporarysociety. Asaresult,thesecuritypolicymakershaverecentlyshownagreat dealofworryovertheprotectionofICTapplicationsandsystems fromcyber-attacks. Cybersecurityiscurrentlyatermusedtodescribetheprocessof defendingICTsystemsfrommultiplecyberthreatsorattacks. The analysis of various cyber-attacks and the development of defensetechniquesthatpreserveseveralqualitiesdescribedas below are the main issues with cyber security (Alhayani et al., 2021). Copyright©2022 PhdAssistance.Allrights reserved Journalsupport |Dissertation support| Analysis |Data collection| Coding& Algorithms |Editing &Peer- Reviewing

  4. Copyright©2022 PhdAssistance.Allrights reserved 01. 02. Information access and disclosure tounauthorizedparties,systems,or entities are prevented by the confidentialityattribute. Integrityisaqualitythathelpsto stopanyunauthorizedchangesto ordeletionsofdata. 03. A property called availability is used to guarantee prompt and dependableaccesstodataassets andsystemsforadesignated entity. Journalsupport |Dissertation support| Analysis |Data collection| Coding& Algorithms |Editing &Peer- Reviewing

  5. Copyright©2022 PhdAssistance.Allrights reserved Theword"cybersecurity"referstoarangeofsituations, including commercial and mobile computers, and can be brokendownintoanumberofstandardcategories. Theseincludeinformationsecurity,whichprimarilyfocuseson thesecurityandprivacyofpertinentdata,applicationsecurity, which considers keeping software and devices free of risks or cyber-threats, network security, which primarily focuses on protectingacomputersystemfromcyberattackersor intruders, and operational security, which also includes the proceduresforhandlingandprotectingdataassets. Networksecuritydevicesandcomputersecuritysystemswith a firewall, antivirus programme, or intrusion detection system makeuptypicalcybersecuritysystems. Journalsupport |Dissertation support| Analysis |Data collection| Coding& Algorithms |Editing &Peer- Reviewing

  6. Machinelearningtasks incybersecurity Machinelearning(ML)issometimesregardedasasubsetof "ArtificialIntelligence,"anditisstronglyrelatedtodatascience, datamining,andcomputationalstatistics. Itfocusesonteachingcomputerstorecognizepatternsfromdata. Machine learningmodels, which could be crucial in the field of cybersecurity,oftenconsistofacollectionofrules,techniques,or intricate "transfer functions" that can be used to uncover interestingdatapatternsortorecognizeoranticipatebehavior. Here,we'llgothroughvariousapproachesforhandlingmachine learning problems and how they relate to cyber security issues (Assistance,2022). Copyright©2022 PhdAssistance.Allrights reserved Journalsupport |Dissertation support| Analysis |Data collection| Coding& Algorithms |Editing &Peer- Reviewing

  7. Copyright©2022 PhdAssistance.Allrights reserved Supervisedlearning Whenspecifiedgoalsareestablishedtoachievefromaparticular set of inputs, or when using a task-driven approach, supervised learningiscarriedout. Regression and classification methods are the most widely used supervised learning techniques in the field of machine learning. Thesemethodsarefrequentlyusedtocategorizeorforecastthe futureofaspecificsecurityissue. For instance, classification methods can be utilized in the cyber securityfieldtoforecastdenial-of-serviceattacks(yes,no),orto recognizevariousclassesofmaliciousactivitieslikescanningand spoofing. Thewell-knownclassificationmethodsareZeroR,OneR,Navies Bayes, Decision Tree, K-nearest neighbors, Support Vector Machines,AdaptiveBoosting,andLogisticRegression. Journalsupport |Dissertation support| Analysis |Data collection| Coding& Algorithms |Editing &Peer- Reviewing

  8. Unsupervisedlearning Findingpatterns,frameworks,orknowledgeinunlabeleddata,or using a data-driven strategy, are the main objective in unsupervisedlearningproblems. Malware, a form of cyber-attack, hides itself in some ways, changingitsbehaviorconstantlyandautonomouslytoevade detection. Unsupervised learning methods like clustering can be used to extracthiddenstructuresandpatternsfromdatasetstofindclues tosuchcomplexattacks. Similar to this, clustering approaches can be helpful in locating anomalies, finding and removing rules breaches, and noisy examplesindata.Thewell-likedhierarchicalclusteringtechniques employed in numerous application domains include single linkage orcompletelinkages,K-means,andK-medoids. Copyright©2022 PhdAssistance.Allrights reserved Journalsupport |Dissertation support| Analysis |Data collection| Coding& Algorithms |Editing &Peer- Reviewing

  9. Copyright©2022 PhdAssistance.Allrights reserved Neuralnetworksanddeep learning Deeplearningisatypeofmachinelearning,asubsetofartificial intelligencethattakescuesfrombiologicalneuralnetworksseen inthehumanbrain. The most widely used neural network algorithm is back propagation,andartificialneuralnetworks(ANN)areextensively employedindeeplearning(Aversanoetal.,2021). Itexecuteslearningonaninputlayer,oneormorehiddenlayers, andanoutputlayerofamulti-layerfeed-forwardneuralnetwork. Deep learning performs better as the volume of security data increases, which is the primary distinction between it and traditionalmachinelearning. Typically,deeplearningalgorithmsworkbestwithvastamounts ofdata,whereasmachinelearningtechniquesworkwellwith smallerdatasets. Journalsupport |Dissertation support| Analysis |Data collection| Coding& Algorithms |Editing &Peer- Reviewing

  10. Conclusionandfuture work The implementation of a strong framework that allows data- drivendecisionmakingisthemostcrucialtaskforasmartcyber securitysystem(Assistance,2021). Tomakesuchaframeworkcapableofminimizingtheseproblems and offering automated and intelligent security services, enhanced data analytics based on machine learning approaches mustbetakenintoaccount. Asaresult,developingadata-drivensecuritymodelforaspecific securityissueaswellasrelatedempiricalevaluationtogaugethe model's efficacy and efficiency and determine its suitability for useinactualapplicationdomainsmaybefutureworks. Furtherinordertodevelopaprofessionalresearchproposalor dissertationincybersecurityapplicationskindlygetintouchwith PhDassistanceforabestandstandardservice. Copyright©2022 PhdAssistance.Allrights reserved Journalsupport |Dissertation support| Analysis |Data collection| Coding& Algorithms |Editing &Peer- Reviewing

  11. References Alhayani, B., Jasim Mohammed, H., Zeghaiton Chaloob, I. & Saleh Ahmed, J. 2021. WITHDRAWN: EffectivenessofartificialintelligencetechniquesagainstcybersecurityrisksapplyofITindustry.Materials Today:Proceedings. Assistance,P.2021.ScopeAndSignificanceOfDataScienceInCybersecurity. Assistance,P.2022.TheContributionofMachineLearninginCybersecurity. Aversano,L.,Bernardi,M.L.,Cimitile,M.&Pecori,R.2021.AsystematicreviewonDeepLearningapproaches forIoTsecurity.ComputerScienceReview.(40).pp.100389. Copyright©2022 PhdAssistance.Allrights reserved Journalsupport |Dissertation support| Analysis |Data collection| Coding& Algorithms |Editing &Peer- Reviewing

  12. Copyright©2022 PhdAssistance.Allrights reserved ContactUs UK:+447537144372 INDIA:+91-9176966446 info@phdassistance.com Journalsupport |Dissertation support| Analysis |Data collection| Coding& Algorithms |Editing &Peer- Reviewing

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