Machine Learning for Real-Time Data-Driven Security Practices

The risk of cyber-attacks exploiting vulnerable organisations has increased significantly over the past several years. These attacks may combine to exploit a vulnerability breach within a system's protection strategy, which has the potential for loss, damage or destruction of assets. Consequently, every vulnerability has an accompanying risk, which is defined as the "intersection of assets, threats, and vulnerabilities" [1]. This research project aims to experimentally compare the similarity-based ranking of cyber security information utilising a recommendation environment. The Memory-Based Collaborative Filtering technique was employed, specifically the User-Based and Item-Based approaches. These systems utilised information from the National Vulnerability Database, specifically for the identification and similarity-based ranking of cyber-security vulnerability information, relating to hardware and software applications. Experiments were performed using the Item-Based technique, to identify the optimum system parameters, evaluated through the AUC evaluation metric. Once identified, the Item-Based technique was compared with the User-Based technique which utilised the parameters identified from the previous experiments. During these experiments, the Pearson's Correlation Coefficient and the Cosine similarity measure was used. From these experiments, it was identified that utilised the Item-Based technique which employed the Cosine similarity measure, an AUC evaluation metric of 0.80225 was achieved.