About: Towards Network Anomaly Detection Using Graph Embedding

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Towards Network Anomaly Detection Using Graph Embedding
Creator	Liu, Jian Jiang, Zhengwei Wang, Quiyun Wang, Xuren Xiao, Qingsai Yao, Yepeng
Source	PMC
abstract	In the face of endless cyberattacks, many researchers have proposed machine learning-based network anomaly detection technologies. Traditional statistical features of network flows are manually extracted and rely heavily on expert knowledge, while classifiers based on statistical features have a high false-positive rate. The communications between different hosts forms graphs, which contain a large number of latent features. By combining statistical features with these latent features, we can train better machine learning classifiers. Therefore, we propose a novel network anomaly detection method that can use latent features in graphs and reduce the false positive rate of anomaly detection. We convert network traffic into first-order and second-order graph. The first-order graph learns the latent features from the perspective of a single host, and the second-order graph learns the latent features from a global perspective. This feature extraction process does not require manual participation or expert knowledge. We use these features to train machine learning algorithm classifiers for detecting network anomalies. We conducted experiments on two real-world datasets, and the results show that our approach allows for better learning of latent features and improved accuracy of anomaly detection. In addition, our method has the ability to detect unknown attacks.
has issue date	2020-05-23(xsd:dateTime)
bibo:doi	10.1007/978-3-030-50423-6_12
has license	no-cc
sha1sum (hex)	74e0152a128a1acc2429473d50a7f3b7e16887d3
schema:url	https://doi.org/10.1007/978-3-030-50423-6_12
resource representing a document's title	Towards Network Anomaly Detection Using Graph Embedding
has PubMed Central identifier	PMC7303711
schema:publication	Computational Science - ICCS 2020
resource representing a document's body	covid:74e0152a128a1acc2429473d50a7f3b7e16887d3#body_text
is schema:about of	named entity 'ATTACKS' named entity 'FACE' named entity 'LEARNING' named entity 'REAL' named entity 'NOVEL' named entity 'MACHINE LEARNING' named entity 'HOST' named entity 'THESE' named entity 'PROCESS' named entity 'FALSE' named entity 'HIGH' named entity 'ABILITY TO' named entity 'TRAFFIC' named entity 'OUR' named entity 'USE' named entity 'classifiers' named entity 'machine learning algorithm' named entity 'graph' named entity 'graph' named entity 'anomaly detection' named entity 'Anomaly Detection' named entity 'Graph Embedding' named entity 'hypergraph' named entity 'Scikit-learn' named entity 'anomaly detection' named entity 'training set' named entity 'conditional distribution' named entity 'DoS' named entity 'clustering algorithm' named entity 'database' named entity 'IP addresses' named entity 'vector space' named entity 'DARPA' named entity 'MySQL' named entity 'server' named entity 'XSS' named entity 'neural network' named entity 'IP addresses' named entity 'anomaly detection' named entity 'IP addresses' named entity 'feature extraction' named entity 'random forest' named entity 'deep learning' named entity 'intrusion detection' named entity 'traffic logs' named entity 'g++' named entity 'machine learning algorithm' named entity 'integer' named entity 'slowloris' named entity 'training set' named entity 'traffic logs' named entity 'CNN' named entity 'test set' named entity 'clustering algorithms' named entity 'IP address' named entity 'deep neural networks' named entity 'Internet' named entity 'anomaly detection' named entity 'attack traffic' named entity 'graph embedding' named entity 'machine learning' named entity 'conditional distribution' named entity 'anomaly detection' named entity 'NetFlow' named entity 'source port' named entity 'linux' named entity 'optimization methods' named entity 'fog computing' named entity 'attack traffic' named entity 'network protocol' named entity 'DoS' named entity 'stolen data' named entity 'IP addresses' named entity 'DARPA' named entity 'Algorithm' named entity 'machine learning' named entity 'Bhattacharyya distance' named entity 'network devices'

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