Anomaly Based Network Intrusion Detection with Unsupervised Outlier Detection

By Jiong Zhang, Mohammad Zulkernine. 2006

274 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Anomaly & Outlier Detection, Privacy & Security

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Nicolas Papernot
2. Nicholas Carlini
3. Patrick Drew McDaniel
4. Michael P. Wellman
5. Arunesh Sinha
6. Dawn Song
7. Cho-Jui Hsieh
8. Bo Li

In Computer Networks and Communications

1. Nicolas Papernot
2. Patrick Drew McDaniel
3. Mohammad Ali Salahuddin
4. Sara Ayoubi
5. Andrew J. Clark
6. K. Ceylan
7. Raouf Boutaba
8. Michael P. Wellman

In Anomaly & Outlier Detection

1. Patrick Drew McDaniel
2. Andrew J. Clark
3. K. Ceylan
4. Nicolas Papernot
5. André Costa Drummond
6. Nicholas Carlini
7. Jonathan J. Davis
8. Paulo Angelo Alves Resende

In Privacy & Security

1. Patrick Drew McDaniel
2. Andrew J. Clark
3. K. Ceylan
4. Michael P. Wellman
5. Nicolas Papernot
6. Arunesh Sinha
7. André Costa Drummond
8. Nicholas Carlini

Citations in the corpus

• Towards the Science of Security and Privacy in Machine Learning (2016, neutral): “The training data could consist of TCP dumps [18].”
• SoK: Security and Privacy in Machine Learning (2018, background): “ble clearly marked as malicious or benign [17]. Second, consider the task of extracting a pattern representative of normal activity in a computer network. The training data could consist of TCP dumps [18]. Such a scenario is commonly encountered in anomaly-based network intrusion detection [19]. Sin”
• Evaluation and Design of Robust Neural Network Defenses (2018, background): “And in the space of intrusion detection, there is a similar story of research on detecting intrusions [137, 138, 54, 47] (and again, even using neural networks [71]) and evading intrusion detection [120, 30].”
• Data preprocessing for anomaly based network intrusion detection: A review (2011, methodology): “Rather than using the standard class label provided in the dataset for each connection, Jiong and Mohammad (2006) used the “service type” feature as the label instead.”
• A Review of Anomaly based Intrusion Detection Systems (2011, methodology): “Jiong Zhang et al [4] proposed a new framework of unsupervised anomaly NIDS based on the outlier detection technique in random forests algorithm.”
• A hybrid intrusion detection system design for computer network security (2009, methodology): “Pros and cons of anomaly-based approach are as follows [1,21,26]:”
• A Survey of Random Forest Based Methods for Intrusion Detection Systems (2018, methodology): “They used the outlier-ness function defined in Equation (2), similarly to Zhang and Zulkernine (2006a).”
• A comprehensive survey on machine learning for networking: evolution, applications and research opportunities (2018, background): “Other interesting applications of unsupervised learning for anomaly detection is RF [495] and an ensemble of single-class classifiers [165].”