activity
20172024
most citedNetFlow Datasets for Machine Learning-based Network Intrusion Detection Systems

405 citations · 937 across the 18 of their papers we have counts for

collaborators
Showing 2017Show all

6 papers · 1 filter

cs.CR2017

Collaborative Anomaly Detection Framework for handling Big Data of Cloud Computing

Nour Moustafa, Gideon Creech, Elena Sitnikova +1

With the ubiquitous computing of providing services and applications at anywhere and anytime, cloud computing is the best option as it offers flexible and pay-per-use based service…

cs.CR2017

Privacy Preservation Intrusion Detection Technique for SCADA Systems

Marwa Keshk, Nour Moustafa, Elena Sitnikova +1

Supervisory Control and Data Acquisition (SCADA) systems face the absence of a protection technique that can beat different types of intrusions and protect the data from disclosure…

cs.CR2017★ 5 cited

Probability Risk Identification Based Intrusion Detection System for SCADA Systems

Thomas Marsden, Nour Moustafa, Elena Sitnikova +1

. As Supervisory Control and Data Acquisition (SCADA) systems control several critical infrastructures, they have connected to the internet. Consequently, SCADA systems face differ…

cs.CR2017

Towards Developing Network forensic mechanism for Botnet Activities in the IoT based on Machine Learning Techniques

Nickolaos Koroniotis, Nour Moustafa, Elena Sitnikova +1

The IoT is a network of interconnected everyday objects called things that have been augmented with a small measure of computing capabilities. Lately, the IoT has been affected by…

cs.CR2017★ 3 cited

RCNF: Real-time Collaborative Network Forensic Scheme for Evidence Analysis

Nour Moustafa, Jill Slay

Network forensic techniques help in tracking different types of cyber attack by monitoring and inspecting network traffic. However, with the high speed and large sizes of current n…

cs.CR2017★ 83 cited

A hybrid feature selection for network intrusion detection systems: Central points

Nour Moustafa, Jill Slay

Network intrusion detection systems are an active area of research to identify threats that face computer networks. Network packets comprise of high dimensions which require huge e…