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Found 7 papers, 2 papers with code
Utilising Flow Aggregation to Classify Benign Imitating Attacks
The features used in these studies are broadly similar and have demonstrated their effectiveness in situations where cyber-attacks do not imitate benign behaviour.
Leveraging Siamese Networks for One-Shot Intrusion Detection Model
Supervised ML is based upon learning by example, demanding significant volumes of representative instances for effective training and the need to re-train the model for every unseen cyber-attack class.
Improving SIEM for Critical SCADA Water Infrastructures Using Machine Learning
Like other Internet of Things (IoT) implementations, SCADA systems are vulnerable to cyber-attacks, therefore, a robust anomaly detection is a major requirement.
A Taxonomy of Network Threats and the Effect of Current Datasets on Intrusion Detection Systems
This manuscript aims to pinpoint research gaps and shortcomings of current datasets, their impact on building Network Intrusion Detection Systems (NIDS) and the growing number of sophisticated threats.
Machine Learning Approach for Detection of nonTor Traffic
A study to compare the reliability and efficiency of Artificial Neural Network and Support vector machine in detecting nonTor traffic in UNB-CIC Tor Network Traffic dataset is presented in this paper.
Threat analysis of IoT networks Using Artificial Neural Network Intrusion Detection System
The Internet of things (IoT) is still in its infancy and has attracted much interest in many industrial sectors including medical fields, logistics tracking, smart cities and automobiles.
Shallow and Deep Networks Intrusion Detection System: A Taxonomy and Survey
Moreover, a taxonomy and survey of shallow and deep networks intrusion detection systems is presented based on previous and current works.
