A C-SVM Based Anomaly Detection Method for Multi-Dimensional Sequence over Data Stream

Bao, Han; Wang, Yijie

doi:10.1109/icpads.2016.0127

Cited by 4 publications

(1 citation statement)

References 16 publications

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“…To overcome the anomaly-based IDS weaknesses, various artificial and computational intelligence algorithms either integrating with meta-heuristic optimization approaches or without them are investigated, such as fuzzy logic Karami & Guerrero-Zapata (2015b); Feizollah et al (2013), Support Vector Machine (SVM) Kabir et al (2017); Bao & Wang (2016), Radial Basis Function (RBF) Karami & Guerrero-Zapata (2015c); Bi et al (2009), Artificial Neural Network (ANN) Subba et al (2016); Hodo et al (2016), Self-Organizing Map (SOM) la Hoz et al 2015; Karami & Guerrero-Zapata (2014); dong Wang et al (2007), Adaptive Neuro-Fuzzy Inference System (ANFIS) Devi et al (2017); Karami & Guerrero-Zapata (2015a), and Principle Component Analysis (PCA) An & Weber (2017); Khalid et al (2015). Nevertheless, the major drawbacks of anomaly-based IDSs exist in terms of the lower detection precision and the higher false positive rate in presence of low-frequent patterns called outliers, resulting in weaker detection stability Karami & Guerrero-Zapata (2015b); Jabez & Muthukumar (2015); Luo & Xia (2014).…”

Section: Accepted Manuscriptmentioning

confidence: 99%

An anomaly-based intrusion detection system in presence of benign outliers with visualization capabilities

Karami

2018

Expert Systems with Applications

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Abnormal network traffic analysis through Intrusion Detection Systems (IDSs) and visualization techniques has considerably become an important research topic to protect computer networks from intruders. It has been still challenging to design an accurate and a robust IDS with visualization capabilities to discover security threats due to the high volume of network traffic. This research work introduces and describes a novel anomaly-based intrusion detection system in presence of long-range independence data called benign outliers, using a neural projection architecture by a modified Self-Organizing Map (SOM) to not only detect attacks and anomalies accurately, but also provide visualized information and insights to end users. The proposed approach enables better analysis by merging the large amount of network traffic into an easy-to-understand 2D format and a simple user interaction. To show the performance and validate the proposed visualization-based IDS, it has been trained and tested over synthetic and real benchmarking datasets (NSL-KDD, UNSW-NB15, AAGM and VPN-nonVPN) that are widely applied in this domain. The results of the conducted experimental study confirm the advantages and effectiveness of the proposed approach.

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