Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- Anomaly detection (1)
- Approximate inference (1)
- Autoencoder (1)
- Bayes (1)
- Bayesian (1)
-
- Bayesian Algorithms (1)
- Bayesian Neural Networks (1)
- Cyber security (1)
- Cybersecurity (1)
- Deep learning (1)
- Dimensionality reduction (1)
- Efficient ML training (1)
- Intrusion detection (1)
- Intrusion prevention (1)
- LSTM (1)
- Long short term memory (1)
- Machine learning (1)
- NIDS (1)
- Network intrusion detection (1)
- Networks defense (1)
- Neural Networks (1)
- Neural network (1)
- Semi unsupervised learning (1)
- Unsupervised learning (1)
Articles 1 - 2 of 2
Full-Text Articles in Computer Sciences
Self-Learning Algorithms For Intrusion Detection And Prevention Systems (Idps), Juan E. Nunez, Roger W. Tchegui Donfack, Rohit Rohit, Hayley Horn
Self-Learning Algorithms For Intrusion Detection And Prevention Systems (Idps), Juan E. Nunez, Roger W. Tchegui Donfack, Rohit Rohit, Hayley Horn
SMU Data Science Review
Today, there is an increased risk to data privacy and information security due to cyberattacks that compromise data reliability and accessibility. New machine learning models are needed to detect and prevent these cyberattacks. One application of these models is cybersecurity threat detection and prevention systems that can create a baseline of a network's traffic patterns to detect anomalies without needing pre-labeled data; thus, enabling the identification of abnormal network events as threats. This research explored algorithms that can help automate anomaly detection on an enterprise network using Canadian Institute for Cybersecurity data. This study demonstrates that Neural Networks with Bayesian …
Comparative Study Of Deep Learning Models For Network Intrusion Detection, Brian Lee, Sandhya Amaresh, Clifford Green, Daniel Engels
Comparative Study Of Deep Learning Models For Network Intrusion Detection, Brian Lee, Sandhya Amaresh, Clifford Green, Daniel Engels
SMU Data Science Review
In this paper, we present a comparative evaluation of deep learning approaches to network intrusion detection. A Network Intrusion Detection System (NIDS) is a critical component of every Internet connected system due to likely attacks from both external and internal sources. A NIDS is used to detect network born attacks such as Denial of Service (DoS) attacks, malware replication, and intruders that are operating within the system. Multiple deep learning approaches have been proposed for intrusion detection systems. We evaluate three models, a vanilla deep neural net (DNN), self-taught learning (STL) approach, and Recurrent Neural Network (RNN) based Long Short …