Cloud-based machine learning for the detection of anonymous web proxies

Miller, Shane; Curran, Kevin; Lunney, Tom

doi:10.1109/issc.2016.7528443

Cited by 11 publications

(4 citation statements)

References 12 publications

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“…Azure ML (Microsoft, Redmond, USA) is a cloud service that enables the execution of ML processes. The Azure ML Studio (Microsoft, Redmond, USA) is available as a workspace to help users build and test predictive models [18]. We built a supervised ML classification model using the Azure ML platform by performing the following: (1) data editing, (2) data splitting, (3) model training, (4) model scoring, and (5) model evaluation (Figure 1).…”

Section: Machine Learning (Ml)mentioning

confidence: 99%

A Predictive Model for Distant Metastasis in Patients With Breast Cancer Based on Machine Learning

Kwon,

Lee,

Joo

et al. 2023

J Breast Dis

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Purpose: Breast cancer starts as a local disease, but can metastasize to distant organs. In this study, we described an easy-to-use tool for predicting distant metastases based on clinical characteristics and gene expression profiles. Methods: We performed a retrospective chart review of 326 patients with breast cancer who underwent surgery and CancerSCAN<sup>TM</sup> between January 2001 and December 2014 at the Samsung Medical Center. Additional retrospective data for 83 patients during 2015 were used for internal validation. CancerSCAN<sup>TM</sup>, a next-generation sequencing-based targeted deep sequencing analysis, was used for gene analysis, and Azure Machine Learning (ML) was used for the ML processes. Results: The no-distant metastasis group comprised 267 patients, while the distant metastasis group comprised 59. Using the Azure ML platform, a predictive model was developed with 326 cases. The area under the curve of the receiver operating characteristic curve for predictive value was 0.917. Based on the internal validation performed using 83 patients, the true-negative was 81 and the true-positive was two when a threshold value of 0.5 was applied. Conclusion: Patients with breast cancer are at risk of metastasis and experience fear throughout their lives. Our predictive model is a valuable and easy-to-access tool for identifying patients with distant metastasis and it presents a way for each institution to achieve optimal results using its variables. Further evaluation with a larger patient population will improve the reliability of this model.

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Section: Machine Learning (Ml)mentioning

confidence: 99%

A Predictive Model for Distant Metastasis in Patients With Breast Cancer Based on Machine Learning

Kwon,

Lee,

Joo

et al. 2023

J Breast Dis

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“…A convention suite called IPsec was acquainted with give further developed confirmation and privacy over the PPP strategies [12]. The first PPP techniques utilized by L2TP were viewed as helpless against a Refusal of Service (Dos) assault which included sending a solicitation to stop the association utilizing the right ID to end the VPN session [20]. This was a weakness that was addressed in a refreshed adaptation of L2TP called L2TP form 3 (L2TPv3).…”

Section: Iiii L2tpmentioning

confidence: 99%

Detection of VPNS Using Machine Learning

Srivastava¹,

Harigovind²,

Modi³

et al. 2022

IJEAST

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The nature of the tools used by criminal actors to hide their identities has made it is very challenging to detect unauthorized users. Network security is any move an association makes to forestall vindictive use or coincidental harm to the organization's private information, its clients, or their gadgets. The objective of organization security is to keep the organization running and valid for every authentic client. Since there are such countless ways that an organization’s security can be compromised, network security includes a wide scope of rehearses. Most of normal assaults against networks are intended to acquire admittance to data, by keeping an eye on the correspondences and information of clients, rather than to harm the actual organization. However, assailants can do more than taking the information. They might have the option to harm clients' gadgets or control frameworks to acquire actual admittance to offices. This leaves the association's property and individuals in danger of damage. Network security extensively comprises of approaches, cycles and practices embraced to forestall, recognize and screen unapproved access, abuse, change, or disavowal of organization available assets. This work presents computational model that can detect the use of virtual private networks to gain unauthorized access

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“…Azure Machine Learning (Azure ML; Microsoft, Redmond, WA, USA) is a cloud service that enables the execution of machine learning processes. The Azure Machine Learning Studio (Microsoft, Redmond, WA, USA) is also available as a workspace to help users build and test predictive models [13]. We built a supervised machine learning classification model using the Azure ML platform.…”

Section: Machine Learningmentioning

confidence: 99%

A Predictive Model for Pathologic Complete Response in Breast Cancer Patients Treated with Neoadjuvant Chemotherapy Using Machine Learning

Kim¹,

Lee²,

Lee³

et al. 2021

ABCR

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Background: In patients with breast cancer after Neoadjuvant Chemotherapy (NAC), pathological Complete Response (pCR) was associated with better long-term outcomes. We here attempted to predict pCR using machine learning. Patients and Methods: From 2008 to 2017, 1308 breast cancer patients underwent NAC before surgery, of whom 377 patients underwent Cancer SCAN TM for gene data. Of 377, 238 were analyzed here, with 139 excluded due to incomplete medical data. Results: The pCR (−) vs. (+) group had 200 vs. 38 patients. In our predictive model with gene data, the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve was 0.909 and accuracy was 0.875. In another model without gene data, the AUC of ROC curve was 0.743 and accuracy was 0.800. We also conducted internal validation with 72 patients undergoing NAC and Cancer SCAN TM during July 2017 and April 2018. When we applied a 0.4 threshold value, accuracy was 0.806 and 0.778 in the predictive model with vs. without gene profiles, respectively. Conclusion: The present predictive model may be a useful and easy-toaccess tool for pCR-prediction in breast cancer patients treated with NAC.

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Cloud-based machine learning for the detection of anonymous web proxies

Cited by 11 publications

References 12 publications

A Predictive Model for Distant Metastasis in Patients With Breast Cancer Based on Machine Learning

A Predictive Model for Distant Metastasis in Patients With Breast Cancer Based on Machine Learning

Detection of VPNS Using Machine Learning

A Predictive Model for Pathologic Complete Response in Breast Cancer Patients Treated with Neoadjuvant Chemotherapy Using Machine Learning

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