activity
20172020
most citedEarly Stage Malware Prediction Using Recurrent Neural Networks

313 citations · 313 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CR2020

Vulnerability Forecasting: In theory and practice

Éireann Leverett, Matilda Rhode, Adam Wedgbury

Why wait for zero-days when you could predict them in advance? It is possible to predict the volume of CVEs released in the NVD as much as a year in advance. This can be done withi…

cs.LG2020

Adversarial Attacks on Machine Learning Cybersecurity Defences in Industrial Control Systems

Eirini Anthi, Lowri Williams, Matilda Rhode +2

The proliferation and application of machine learning based Intrusion Detection Systems (IDS) have allowed for more flexibility and efficiency in the automated detection of cyber a…

cs.CR2019

Real-time malware process detection and automated process killing

Matilda Rhode, Pete Burnap, Adam Wedgbury

Perimeter-based detection is no longer sufficient for mitigating the threat posed by malicious software. This is evident as antivirus (AV) products are replaced by endpoint detecti…

cs.HC2017

Data Capture & Analysis to Assess Impact of Carbon Credit Schemes

Matilda Rhode, Omer Rana, Tim Edwards

Data enables Non-Governmental Organisations (NGOs) to quantify the impact of their initiatives to themselves and to others. The increasing amount of data stored today can be seen a…

cs.CR2017★ 313 cited

Early Stage Malware Prediction Using Recurrent Neural Networks

Matilda Rhode, Pete Burnap, Kevin Jones

Static malware analysis is well-suited to endpoint anti-virus systems as it can be conducted quickly by examining the features of an executable piece of code and matching it to pre…