Currently, Machine learning is the hottest skill in the job market. Like many other domains, Machine Learning has many use cases in Cyber Security domain. Most of Security Operation engineers deal with massive amount of data in terms logs getting generated by Firewall, IPS, Anti-Virus, Web Servers, Desktop etc. It is humanly not possible to analyze these data and tell what is going on in the network. One needs to be aware of machine learning sophisticated algorithms to find the needle in a haystack. The best part of Machine learning is not only it helps one Cyber Security Professional to perform reactive analysis but also perform predictive analysis.
Big data skills are required for those you want to understand each deadly attack in its entirety. SOC engineers are required to find out the root cause of any outages and this is where Machine learning skills will be most helpful. If you are working in IRT (Incident Response Team) then you will consume a large amount of data and come up with your own theory about the incident. Those who are responsible for protecting the critical networks are required to have some really good Machine Learning skills in order to predict the attack/outage.
- Introduction to Machine Learning.
- Supervised and Unsupervised Learning.
- Classification Tools and Techniques.
- Clustering Analysis.
- Working with Decision trees.
- Bayesian Algorithms.
- Hands-on lab with Python, Spark and Machine Learning Library.
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Machine Learning and Python
- Working with Python ML Libraries.
- Writing basic ML programs using Python.
- Classification and Regression.
- Generalization, Over/Under fitting.
- Challenges in Supervised Learning.
- Types of Unsupervised learning.
- Challenges in Unsupervised learning.
Thoughts on Model Improvement.