Teaching Cybersecurity and Machine Learning
Breaking Down the Python Barriers and Integrating With Splunk to Learn ML
Well, in teaching, you often need to innovate and try out new things. Some things will work, and others won’t. And, so, this week we introduced a lecture and lab on cybersecurity and machine learning (ML). Why? Well, ML is often taught from a data science point-of-view, and its presentation typically has no real application into cybersecurity. Students in cybersecurity and networking can then sometimes struggle to fully see the importance of the topic. Along, with this, I think machine learning is one of the least understood areas of cybersecurity, and just understanding the core concepts is a significant step forward. There’s a small barrier to get over, and it’s often just understanding the key areas of knowledge.
Another problem is that students often get presented with Python code and using an sk-Learn integration. While researchers see this as a natural way of presenting machine learning it can also provide a barrier to the understanding of the methods. The link between the core principles and the presentation of the Python code can often break-down the learning process. And, so, this week, we used Splunk to present the basic methods, and used data sets which are most relevant to cybersecurity:
