Anomaly Detection in Atm-Grade Software Defined Networks

Lellek, Philipp; Leydold, Peter; Vojnoski, Igor; Eier, Dieter

doi:10.1109/icns52807.2021.9441630

Cited by 1 publication

(2 citation statements)

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“…Due to the area between ROC curve and x coordinate axis, namely, AUC, the larger the AUC is, the better the model classi cation performance is. As can be seen from Figure 10, when the dimensions of the three hidden layers of the encoder are 8, 7, and 4, respectively, the AUC is the largest, which is 0.89. erefore, the dimension of the hidden layers of the encoder is determined to be (8,7,4).…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…is fragile environment brings a new attack medium to ATM. Moreover, the connection of some legacy systems of ATM system to the network will produce serious threats and vulnerabilities [4]. Aiming at the information security research of ATM system, Chivers [5] proposed a control consistency as a method to check and exchange control set information in the management system and studied the systematic defects of ATM security.…”

Section: Related Workmentioning

confidence: 99%

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Feature Extraction Method Based on Sparse Autoencoder for Air Traffic Management System Security Situation Awareness

Bai

Zhang

et al. 2022

Security and Communication Networks

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In wide-area distributed scenarios, it is particularly important to carry out information security situational awareness for the air traffic management (ATM) system with integrated air-ground structure. The operation data of the communication, navigation and surveillance (CNS) equipment of ATM system have the characteristics of multi-dimension, complexity, and strong correlation. In the process of situation awareness feature extraction, there are problems such as poor model accuracy, weak feature expression ability, and low classification performance. A feature association algorithm is designed to solve the above problems. Based on this algorithm, a deep-related sparse autoencoder (DRSAE) model based on improved sparse autoencoder is established. In DRSAE model, L1 regularization and Kullback–Leibler divergence (KLD) sparsity terms are used to penalize the parameters of the encoder network, and the quantity of hidden layers is increased to allow the model to optimize the global encoder network by iteratively training a single encoder. Moreover, the proposed DRSAE model and other feature extraction models such as principal component analysis (PCA), autoencoder (AE), and sparse autoencoder (SAE) are compared and evaluated by using the support vector machine (SVM) classifier. Compared with other feature extraction models, it is found that the proposed DRSAE model has good robustness in feature extraction of ATM system, and the obtained features have strong expression ability, which enhances the classification performance of the model and is convenient for situation awareness.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%