Real World AI in Cybersecurity

• Overcome antivirus limits in threat detection, classify suspicious user activity, and use fraud detection algorithms
• Use application performance monitoring (APM) tools and improve spam detection with advanced filtering techniques
• Pick the right Python libraries for AI
• Categorize advanced persistent threats (APT), zero-days, and malware samples
• Use python tools to turn logs into datasets for analysis, predict network intrusions, and spot fake logins and fake accounts
• Apply algorithms from AI for cybersecurity including decision trees, Bayesian classification, least squares regression and more
• Use Jupyter notebooks and the key tools including MLBase.jl, cikitLearn.jl, MachineLearning.jl and Mocha.jl
• Test data using AI to assess incident response

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