A new feature popularity framework for detecting cyberattacks using popular features

Abstract

Abstract We propose a novel feature popularity framework, and introduce this new framework to the cybersecurity domain. Feature popularity has not yet been used in machine learning or data mining, and we implement it with three web attacks from the CSE-CIC-IDS2018 dataset: Brute Force, SQL Injection, and XSS web attacks. Feature popularity is based upon ensemble Feature Selection Techniques (FSTs) and allows us to more easily understand common and important features between different cyberattacks. Three filter-based and four supervised learning-based FSTs are used to generate feature subsets for each of our three different web attack datasets, and then our feature popularity frameworks are applied. Classification performance for feature popularity is mostly similar as compared to when “all features” are evaluated (with feature popularity subsets having better performance in 5 out of 15 experiments). Our feature popularity technique effectively builds an ensemble of ensembles by first building an ensemble of FSTs for each dataset, and then building another ensemble across a dataset agreement dimension. The Jaccard similarity is also employed with our feature popularity framework in order to better identify which attack classes should (or should not) be grouped together when applying feature popularity. The four most popular features across all three web attacks from this experiment are: Flow_Bytes_s, Flow_IAT_Max, Fwd_IAT_Std, and Fwd_IAT_Total. When only using these four features as input to our models, classification performance is not seriously degraded. This feature popularity framework granted us new and previously unseen insights into the web attack detection process with CSE-CIC-IDS2018 big data, even though we had intensely studied it previously. We realized these four particular features cannot properly identify our three web attacks, as they operate mainly from the time dimension and NetFlow features from layers 3 and 4 of the OSI model. Conversely, our three web attacks operate in the application layer (7) of the OSI model and should not leave signatures in these four features. Feature popularity produces easier to explain models which provide domain experts better visibility into the problem, and can also reduce the complexity of implementing models in real-world systems.

Keywords

• Feature popularity
• Feature similarity
• Feature selection
• Intrusion detection
• Web attacks