Anomaly Detection Using Real-Valued Negative Selection

By Fabio A. González, Dipankar Dasgupta. Genetic Programming and Evolvable Machines, 2003

349 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Biomedical Engineering, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/2ee4c0887bfb95efdc237210f1ffced790eab0a0

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. Charu C. Aggarwal
2. Vipin Kumar
3. Don R. Hush
4. Clint Scovel
5. Ingo Steinwart
6. Jing Gao
7. Jiawei Han
8. Arindam Banerjee

In Biomedical Engineering

1. Charu C. Aggarwal
2. Julie Greensmith
3. Uwe Aickelin
4. Zhou Ji
5. Christopher Leckie
6. Kingsly Leung
7. Arindam Banerjee
8. Vipin Kumar

In Computer Networks and Communications

1. Charu C. Aggarwal
2. Jiawei Han
3. Vipin Kumar
4. Christopher Leckie
5. Kingsly Leung
6. Zhou Ji
7. Julie Greensmith
8. Uwe Aickelin

Citations in the corpus

• Outlier Detection for Temporal Data: A Survey (2014, methodology): “Besides the dictionaries for normal sequences, anomaly dictionaries can also be created [34], [50], [65]–[67].”
• Real-Valued Negative Selection Algorithm with Variable-Sized Detectors (2004, methodology): “On the other hand, this work and some other works [9][ 10 ] demonstrated that despite the intrinsic difficulty of real-valued representation, it can also provide unique opportunity in dealing with higher dimensionality.”
• A Classification Framework for Anomaly Detection (2005, methodology): “A well-known heuristic (see e.g. Fan et al., 2001; González and Dagupta, 2003; Yu et al., 2004; Theiler and Cai., 2003) for anomaly detection is to generate a labeled data set by assigning one label to the original unlabeled data and another label to a set of artificially generated data, and then ap”
• Unsupervised Anomaly Detection in Network Intrusion Detection Using Clusters (2005, methodology): “Another approach uses the normal data to generate abnormal data and uses it as input for a classi(cid:12)cation algorithm (Gonzalez & Dasgupta 2003).”
• Anomaly Detection for Discrete Sequences: A Survey (2012, methodology): “If S contains only normal sequences, a random anomaly generation approach is used [9], [39], [42].”
• A review of novelty detection (2014, methodology): “Later, the authors propose a real-valued negative selection algorithm [101].”
• Immune system approaches to intrusion detection – a review (2004, background): “In [14], Gonzalez and Dasgupta build an anomaly detector that only requires positive samples, not negative ones, and compare this to a self-organising map (SOM) approach.”
• Fundamentals of natural computing: an overview (2007, neutral): “, [104,120,141]).”