Network traffic anomaly detection based on packet bytes

By Matthew V. Mahoney. ACM Symposium on Applied Computing, 2003

309 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/b8ff9337fb38f8895ceacbaa255cad1596c639f3

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In Artificial Intelligence

In Computer Networks and Communications

Citations in the corpus

Software Defined Networking Architecture, Security and Energy Efficiency: A Survey (2017, background): “4) Network Advertisement (NETAD) [117] is a two staged process.”
Comparing Anomaly Detection Techniques for HTTP (2007, unknown): “In spite of these limitations, Wang and Stolfo [23], Mahoney [24], and Mahoney and Chan [25] Vargiya and Chan [26] used one or both of these data sets for testing their IDSs, at least a portion of which were for protecting web servers.”
An Analysis of the 1999 DARPA/Lincoln Laboratory Evaluation Data for Network Anomaly Detection (2003, methodology): “The most serious questions are raised by the large number of attacks detected by anomalous source (remote) addresses by NETAD, and two other systems, ALAD [22] and LERAD [21].”
Detecting Unknown Network Attacks Using Language Models (2006, methodology): “Recently, techniques of anomaly-based network intrusion detection have been proposed that analyze packet and connection payloads (11; 16; 17; 18; 10; 9).”
Revisiting Traffic Anomaly Detection Using Software Defined Networking (2011, methodology): “Both Maximum Entropy and NETAD use baseline models of network traffic using predefined attributes and detect deviations from these attributes.”
Anomalous Payload-Based Network Intrusion Detection (2004, methodology): “Some of the systems reconstruct the network packets and extract features that describe the higher level interactions between end hosts like MADAMID [9], Bro [15], EMERALD [18], STAT [24], ALAD [13], etc.”
A hybrid intrusion detection system design for computer network security (2009, methodology): “PHAD [17] and NETAD [18] statistical methods are chosen as the anomaly-based intrusion detection systems in this paper.”
Using an Ensemble of One-Class SVM Classifiers to Harden Payload-based Anomaly Detection Systems (2006, methodology): “Accuracy and hardness of evasion are obtained by constructing our anomaly-based IDS using an ensemble of one-class SVM classifiers that work on different feature spaces.”