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Cyber Security: Application of AI · LearnSpace
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courseraПрограммирование

Cyber Security: Application of AI

Курс от Macquarie University
Средний≈ 17.6 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

• Watch our course introduction video before you enroll! (copy and paste into browser) https://vimeo.com/1176025320 AI for Cyber Security: Defend Smarter, Not Harder Artificial intelligence (AI) and machine learning (ML) are essential for modern cyber defense. This course provides a hands-on guide to understanding how AI and ML detect, disrupt, and defend against cyber threats. This program focuses on practical applications needed by organizations. Key topics include: • Build foundational AI and ML concepts, including model training, learning types, and accuracy. • Apply ML tools and models to security problems like malware analysis, fraud detection, and network monitoring. • Analyze network traffic using anomaly detection with supervised and unsupervised ML methods (e.g., k-nearest neighbors, one-class SVM). • Experiment with ML-driven analysis to identify malware and apply artificial neural networks for detection. • Understand adversarial machine learning, including poisoning and evasion attacks, and how to build resilient systems. Basic familiarity with Python programming is recommended for practical activities and labs. This course is designed for cyber security professionals, SOC analysts, engineers, data scientists, and tech leaders seeking to enhance security strategies with intelligent automation and machine-driven defense.

Навыки, которые вы освоите

Model TrainingArtificial Intelligence and Machine Learning (AI/ML)Machine Learning MethodsCyber AttacksCybersecurityThreat ModelingCyber Threat IntelligencePython ProgrammingIntrusion Detection and PreventionCyber Security Strategy

Программа курса

5 модулей · 60 учебных материалов

01AI and Machine Learning Concepts16 материалов
Topic overviewPLUGINIntroductionPLUGINAn Industry PerspectivePLUGINConcepts and definitions of Machine LearningPLUGINLearning tasks - classification and regression

Учитесь у экспертов

Matt Bushby

Преподаватель курса

Cyber Security: Application of AI
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Обучение на Coursera

≈ 17.6 ч

5 модулей

Язык: Английский

Часть программы вашего университета
PLUGIN
Accuracy of machine learning modelsPLUGIN
Attacks on machine learning - an overviewPLUGIN
Inference attacksPLUGIN
Adversarial input attacksPLUGIN
Poisoning attacksPLUGIN
Model stealingPLUGIN
The Right Tool for the Job: When Machine Learning Makes Sense in CybersecurityDIALOGUE
SummaryPLUGIN
ReferencesPLUGIN
End of module practice quizЗадание
End of module quizЗадание
02Machine Learning Applications in Cyber Security15 материалов
Topic overviewPLUGINIntroductionPLUGINMalware analysisPLUGINNetwork anomaly detectionPLUGINDeep packet inspectionPLUGINFraud detectionPLUGINLoading, viewing and preprocessing datasetsPLUGINTraining and testing a classification modelPLUGINTraining and testing a regression modelPLUGINSummaryPLUGINReferencesPLUGINCyber Threat Detective: Choosing the Right ML ApproachDIALOGUEDefending Your Model: The Classification Approach ReviewDIALOGUEEnd of module practice quizЗаданиеEnd of module quizЗадание
03Machine Learning for Network Traffic Analysis8 материалов
Topic overviewPLUGINMalware binariesPLUGINMalware typesPLUGINMalware analysis techniquesPLUGINUsing machine learningPLUGINArtificial neural networksPLUGINMalware Analysis Lab: From Bytes to BehaviorDIALOGUEEnd of module quizЗадание
04Machine Learning for Network Anomaly Detection12 материалов
Topic overviewPLUGINNetwork anomaly detectionPLUGINK nearest neighboursPLUGINK nearest neighbours for outlier detectionPLUGINNetwork anomaly detection using machine learningPLUGINOutlier detection using K nearest neighboursPLUGINOutlier detection using one class SVMPLUGINDetecting normal and attack trafficPLUGINAnomaly or False Positive? Interpreting Network Detection ResultsDIALOGUEThe SOC Brief: Communicating Anomaly Findings to LeadershipDIALOGUEEnd of module quizЗаданиеFalse Positive Fallout: Managing Stakeholder FrustrationDIALOGUE
05Attacks on Machine Learning and Defences9 материалов
Topic overviewPLUGINThreat modelPLUGINAdversarial inputsPLUGINGenerating adversarial examplesPLUGINPoisoning attacksPLUGINAI Security Architect: Defending Against the Invisible EnemyDIALOGUEThe Insider Threat: Investigating Suspected Data PoisoningDIALOGUEEnd of module quizЗаданиеCongratulations and next stepsЧтение