Enhancing Network Visibility and Security through Tensor Analysis

Baskaran, Muthu Manikandan; Henretty, Thomas; Ezick, James; Lethin, Richard; Bruns-Smith, David

doi:10.1016/j.future.2019.01.039

Cited by 13 publications

(2 citation statements)

References 2 publications

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“…The system satisfies the requirements for the analysis and prediction of large-scale network security conditions. The study in [ 7 ] proposed a traffic analysis tool. The tool was designed to provide scalable analysis and services for network traffic data, allowing attackers to explicitly engineer their actions or hide attacks within the broader normal activity, thereby improving network visibility and security.…”

Section: Related Workmentioning

confidence: 99%

Modeling Analysis of SM2 Construction Attacks in the Open Secure Sockets Layer Based on Petri Net

Deng

Zhang

Wang

et al. 2022

Sensors

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The detection and defense of malicious attacks are critical to the proper functioning of network security. Due to the diversity and rapid updates of the attack methods used by attackers, traditional defense mechanisms have been challenged. In this context, a more effective method to predict vulnerabilities in network systems is considered an urgent need to protect network security. In this paper, we propose a formal modeling and analysis approach based on Petri net vulnerability exploitation. We used the Common Vulnerabilities and Exposures (CVE)-2021-3711 vulnerability source code to build a model. A patch model was built to address the problems of this model. Finally, the time injected by the actual attacker and the time simulated by the software were calculated separately. The results showed that the simulation time was shorter than the actual attack time, and ultra-real-time simulation could be achieved. By modeling the network system with this method, the model can be found to arrive at an illegitimate state according to the structure of Petri nets themselves and thus discover unknown vulnerabilities. This method provides a reference method for exploring unknown vulnerabilities.

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Section: Related Workmentioning

confidence: 99%

Modeling Analysis of SM2 Construction Attacks in the Open Secure Sockets Layer Based on Petri Net

Deng

Zhang

Wang

et al. 2022

Sensors

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“…Chi and Kolda showed in [17] that under these assumptions a Poisson CP tensor model is an effective low-rank approximation of X. The Poisson CP tensor model has shown to be effective in analyzing latent patterns and relationships in count data across many application areas, including food production [13], network analysis [11,19], term-document analysis [16,29], email analysis [14], link prediction [18], geosptial analysis [22,28], web page analysis [39], and phenotyping from electronic health records [27,30,31] One numerical approach to fit low-rank Poisson CP tensor models to data, tensor maximum likelihood estimation, has proven to be effective. Computing a Poisson CP tensor model via tensor maximum likelihood estimation involves minimizing the following non-linear, nonconvex optimization problem:…”

Section: 4mentioning

confidence: 99%

A Hybrid Method for Tensor Decompositions that Leverages Stochastic and Deterministic Optimization

Myers¹,

Dunlavy²

2022

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In this paper, we propose a hybrid method that uses stochastic and deterministic search to compute the maximum likelihood estimator of a low-rank count tensor with Poisson loss via state-of-theart local methods. Our approach is inspired by Simulated Annealing for global optimization and allows for fine-grain parameter tuning as well as adaptive updates to algorithm parameters. We present numerical results that indicate our hybrid approach can compute better approximations to the maximum likelihood estimator with less computation than the state-of-the-art methods by themselves.

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TenGAN: adversarially generating multiplex tensor graphs

Shiao,

Miller,

Chan

et al. 2023

Data Min Knowl Disc

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In this work, we explore multiplex graph (networks with different types of edges) generation with deep generative models. We discuss some of the challenges associated with multiplex graph generation that make it a more difficult problem than traditional graph generation. We propose TenGAN, the first neural network for multiplex graph generation, which greatly reduces the number of parameters required for multiplex graph generation. We also propose 3 different criteria for evaluating the quality of generated graphs: a graph-attribute-based, a classifier-based, and a tensor-based method. We evaluate its performance on 4 datasets and show that it generally performs better than other existing statistical multiplex graph generative models. We also adapt HGEN, an existing deep generative model for heterogeneous information networks, to work for multiplex graphs and show that our method generally performs better.

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Enhancing Network Visibility and Security through Tensor Analysis

Cited by 13 publications

References 2 publications

Modeling Analysis of SM2 Construction Attacks in the Open Secure Sockets Layer Based on Petri Net

Modeling Analysis of SM2 Construction Attacks in the Open Secure Sockets Layer Based on Petri Net

A Hybrid Method for Tensor Decompositions that Leverages Stochastic and Deterministic Optimization

TenGAN: adversarially generating multiplex tensor graphs

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