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Machine Learning and Emerging Technologies in Cybersecurity · LearnSpace
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courseraIT и технологии

Machine Learning and Emerging Technologies in Cybersecurity

Курс от Johns Hopkins University
Средний≈ 39.1 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

The course "Machine Learning and Emerging Technologies in Cybersecurity" offers an in-depth exploration of machine learning applications in cybersecurity, focusing on techniques for threat detection and prevention. Participants will gain a solid grounding in machine learning fundamentals, including neural networks, clustering, and support vector machines, tailored specifically for cybersecurity contexts. Unique to this course is the integration of machine learning with Intrusion Detection Systems (IDS), equipping learners with practical skills to enhance threat detection capabilities. Additionally, the course examines Tor networking, providing insights into secure and anonymous communication systems, as well as the critical role of IDS within Cyber Security Incident Response Teams (CSIRTs) in enterprise environments. By the end of the course, learners will not only understand how to apply advanced machine learning techniques but also be proficient in tools like RapidMiner and Security Onion. This blend of theory and hands-on application ensures that participants leave with the skills needed to tackle real-world cybersecurity challenges effectively, making this course a vital resource for those looking to advance their careers in cybersecurity and data science.

Навыки, которые вы освоите

Applied Machine LearningIntrusion Detection and PreventionMachine Learning AlgorithmsNetwork ArchitectureAnomaly DetectionMachine LearningAI SecurityThreat DetectionCryptographic ProtocolsModel EvaluationComputer Security Incident ManagementCyber Threat IntelligenceCybersecurityModel TrainingMachine Learning MethodsPredictive ModelingContinuous MonitoringNetwork SecurityIncident ResponseRouting Protocols

Программа курса

5 модулей · 67 учебных материалов

01Course Introduction4 материалов
Specialization Introduction Video: Intrusion DetectionВидеоCourse OverviewЧтениеInstructor Biography - Jason CrosslandЧтениеResources & ReferencesЧтение
02Machine Learning I16 материалов

Учитесь у экспертов

Jason Crossland

Senior CyberSecurity Engineer

Machine Learning and Emerging Technologies in Cybersecurity
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Обучение откроется на Coursera
в новой вкладке

Обучение на Coursera

≈ 39.1 ч

5 модулей

Язык: Английский

Субтитры: Узбекский, Казахский, Малайский

Часть программы вашего университета

Introduction to Machine Learning and Data Mining

IntroductionВидеоIntroduction to Machine Learning Concepts ВидеоNeural NetworksВидеоAssurance for Machine Learning VideoPLUGINReading ReferencesЧтениеIntroduction to Machine Learning and Data MiningЗадание

Implementing and Evaluating IBM Watson for Fraud Detection

ClusteringВидеоHelp Vector MachinesВидеоRapidMiner ExamplePLUGINDeep Learning Using Keras – Training Neural NetworkPLUGINReading ReferencesЧтениеImplementing and Evaluating IBM Watson for Fraud DetectionЗадание

Module-end Assessments

Self-Reflective Reading: AI, Ethics, and Military CollaborationЧтениеMachine Learning IЗаданиеPractice Lab: Building and Training a Neural Network with KerasЛабораторнаяPractice lab: Implementing and Tuning a Perceptron in PythonЛабораторная
03Machine Learning II15 материалов

Types of Machine Learning Algorithms and Their Application in IDS

IntroductionВидеоChoosing an ML AlgorithmВидеоApplying ML to IDВидеоEnigma Talk by Jeremy Howard on Deep Learning (Optional)PLUGINReading ReferencesЧтениеTypes of Machine Learning Algorithms and their Application in IDSЗадание

Challenges in Implementing ML in IDS and Evaluation Techniques

Data PreparationPLUGINApplying the ModelPLUGINBuilding the ModelPLUGINValidating a ModelPLUGINReading ReferencesЧтениеChallenges in Implementing ML in IDS and Evaluation TechniquesЗадание

Module-end Assessments

Self-Reflective Reading: Exploring Machine Learning in Intrusion Detection SystemsЧтениеSelf-Reflective Reading: Analyzing Netflow Data and Machine Learning Models in CybersecurityЧтениеMachine Learning IIЗадание
04ToR Networking10 материалов

Introduction to ToR Architectures and Node Types

ToR Networking VideoPLUGINReading ReferencesЧтениеIntroduction to ToR Architectures and Node TypesЗадание

ToR Relays, Security Concerns, and Data Anonymization

DJ Ware - Discussion of "The Onion Router" ToRPLUGINReading ReferencesЧтениеToR History & Data AnonymizationЧтениеToR Relays, Security Concerns, and Data AnonymizationЗадание

Module-end Assessments

Self-Reflective Reading: Exploring Neural Networks and Intrusion Detection in ToR NetworksЧтениеSelf-Reflective Reading: Understanding Tor Network Anonymity and SecurityЧтениеToR NetworkingЗадание
05IDS in Context22 материалов

Forming and Managing a CSIRT

IntroductionВидеоForming a CSIRTВидеоIDS Response ProcessВидеоInformation SharingВидеоReading ReferencesЧтениеForming and Managing a CSIRTЗадание

Executing IDS Response Processes with Security Onion

Future Applications of IDS/IPSВидеоConcrete Steps for Implementing an Information Security ProgramЧтениеCrisis Communications During a Security IncidentЧтениеReading ReferencesЧтениеExecuting IDS Response Processes with Security OnionЗадание

Emerging Trends and Future Challenges in Intrusion Detection and Prevention Systems (IDS/IPS)

Technical Challenges Yet to Be ResolvedВидеоWhere to Get Information on the Future of IDS/IPS?ВидеоLearning Rules for Anomaly Detection PLUGINThe New Fundamentals of Security - Mike Fey - RSA Conference US 2013 KeynotePLUGINBig Data Redefines Security - Arthur Coviello, Jr. - RSA Conference US 2013 Keynote (14E)PLUGINTech Talk on How to Detect Intruders Already in Your System PLUGIN

Module-end Assessments

Self-Reflective Reading: Analyzing the Cyber Intelligence Sharing and Protection Act (CISPA)ЧтениеSelf-Reflective Reading: Integrating IDS and CSIRT in Cybersecurity and Evaluating CISAЧтениеIDS in ContextЗадание
Reading ReferencesЧтение
Emerging Trends and Future Challenges in Intrusion Detection and Prevention Systems (IDS/IPS)Задание