An overview of anomaly detection techniques: Existing solutions and latest technological trends

By Animesh Patcha, Jung-Min "Jerry" Park. Comput. Networks, 2007

1,749 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/7c3870e4c6f75aefeb7801c75026cefb512304f6

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Citations in the corpus

A Critical Analysis on the Security Concerns of Internet of Things (IoT) (2015, methodology): “There are different existing intrusion detection techniques [34] including the data mining approach [35] and anomaly detection.”
Low-Rate DDoS Attacks Detection and Traceback by Using New Information Metrics (2011, methodology): “Shannon’s entropy and Kullback–Leibler’s divergence methods have both been regarded as effective methods for detecting abnormal traffic based on IP address-distribution statistics or packet size-distribution statistics [10]–[12].”
Data preprocessing for anomaly based network intrusion detection: A review (2011, methodology): “Hence Patcha and Park (2007a) use an adaptive sampling technique to balance the requirements of accuracy and resource overheads.”
Generalized Out-of-Distribution Detection: A Survey (2021, neutral): “To alleviate the problem, shallow methods implement feature engineering to reduce the dimensionality [326], [327].”
Progress in Outlier Detection Techniques: A Survey (2019, background): “The most comprehensive ones [28], [33], [41], despite containing a lot of insights, they do not review most of the primary state-of-the-art methods, with most published at least five years ago.”
Video-Based Abnormal Human Behavior Recognition—A Review (2012, background): “Recently, in [28] and [29] general surveys have been published on the subject of anomaly detection.”
TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data (2022, methodology): “Other classes of methods use Principal Component Analysis (PCA), process regression or hidden Markov chains to model time-series data [41].”
A survey of multiple classifier systems as hybrid systems (2014, background): “Machine learning methods in security applications (like spam filters or IDS/IPS) [158] or decision support systems for marketing departments [159] require to take into account new training data with potentially different statistical properties [116].”
Survey of computational intelligence as basis to big flood management: challenges, research directions and future work (2018, background): “According to the inspiring study of Patcha and Park (2007), the drawback in BNs is their requirement for massive computational effort.”