scholarly journals Improving vulnerability remediation through better exploit prediction

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Jay Jacobs ◽  
Sasha Romanosky ◽  
Idris Adjerid ◽  
Wade Baker
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Small Body ◽  
Low Risk ◽  
Machine Learning Techniques ◽  
Outcome Variable ◽  
Remediation Strategies ◽  
Learning Techniques ◽  
In The Wild ◽  
Cyber Risk

Abstract Despite significant innovations in IT security products and research over the past 20 years, the information security field is still immature and struggling. Practitioners lack the ability to properly assess cyber risk, and decision-makers continue to be paralyzed by vulnerability scanners that overload their staff with mountains of scan results. In order to cope, firms prioritize vulnerability remediation using crude heuristics and limited data, though they are still too often breached by known vulnerabilities for which patches have existed for months or years. And so, the key challenge firms face is trying to identify a remediation strategy that best balances two competing forces. On one hand, it could attempt to patch all vulnerabilities on its network. While this would provide the greatest ‘coverage’ of vulnerabilities patched, it would inefficiently consume resources by fixing low-risk vulnerabilities. On the other hand, patching a few high-risk vulnerabilities would be highly ‘efficient’, but may leave the firm exposed to many other high-risk vulnerabilities. Using a large collection of multiple datasets together with machine learning techniques, we construct a series of vulnerability remediation strategies and compare how each perform in regard to trading off coverage and efficiency. We expand and improve upon the small body of literature that uses predictions of ‘published exploits’, by instead using ‘exploits in the wild’ as our outcome variable. We implement the machine learning models by classifying vulnerabilities according to high- and low-risk, where we consider high-risk vulnerabilities to be those that have been exploited in actual firm networks.

Fetal Birth Weight Estimation in High-Risk Pregnancies Through Machine Learning Techniques

2019 ◽  
Author(s):  
Mario W. L. Moreira ◽  
Joel J. P. C. Rodrigues ◽  
Vasco Furtado ◽  
Constandinos X. Mavromoustakis ◽  
Neeraj Kumar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Birth Weight ◽  
High Risk ◽  
Machine Learning Techniques ◽  
Weight Estimation ◽  
Learning Techniques ◽  
Fetal Birth Weight

scholarly journals Symptom clusters among cancer survivors: what can machine learning techniques tell us?

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Koen I. Neijenhuijs ◽  
Carel F. W. Peeters ◽  
Henk van Weert ◽  
Pim Cuijpers ◽  
Irma Verdonck-de Leeuw
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Cancer Survivors ◽  
Well Being ◽  
Physical Symptoms ◽  
Symptom Clusters ◽  
Machine Learning Techniques ◽  
Risk Scores ◽  
Learning Techniques ◽  
Patient Reported

Abstract Purpose Knowledge regarding symptom clusters may inform targeted interventions. The current study investigated symptom clusters among cancer survivors, using machine learning techniques on a large data set. Methods Data consisted of self-reports of cancer survivors who used a fully automated online application ‘Oncokompas’ that supports them in their self-management. This is done by 1) monitoring their symptoms through patient reported outcome measures (PROMs); and 2) providing a personalized overview of supportive care options tailored to their scores, aiming to reduce symptom burden and improve health-related quality of life. In the present study, data on 26 generic symptoms (physical and psychosocial) were used. Results of the PROM of each symptom are presented to the user as a no well-being risk, moderate well-being risk, or high well-being risk score. Data of 1032 cancer survivors were analysed using Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) on high risk scores and moderate-to-high risk scores separately. Results When analyzing the high risk scores, seven clusters were extracted: one main cluster which contained most frequently occurring physical and psychosocial symptoms, and six subclusters with different combinations of these symptoms. When analyzing moderate-to-high risk scores, three clusters were extracted: two main clusters were identified, which separated physical symptoms (and their consequences) and psycho-social symptoms, and one subcluster with only body weight issues. Conclusion There appears to be an inherent difference on the co-occurrence of symptoms dependent on symptom severity. Among survivors with high risk scores, the data showed a clustering of more connections between physical and psycho-social symptoms in separate subclusters. Among survivors with moderate-to-high risk scores, we observed less connections in the clustering between physical and psycho-social symptoms.

scholarly journals The emergence of cooperation by evolutionary generalization

2021 ◽  
Vol 288 (1951) ◽  
pp. 20210338
Author(s):  
Félix Geoffroy ◽  
Jean-Baptiste André
Keyword(s):  
Machine Learning ◽  
Evolutionary Model ◽  
Machine Learning Techniques ◽  
Cooperative Behaviour ◽  
Human Species ◽  
Learning Techniques ◽  
In The Wild ◽  
Emergence Of Cooperation ◽  
Evolutionary Analogy ◽  
Evolutionarily Stable

In principle, any cooperative behaviour can be evolutionarily stable as long as it is incentivized by a reward from the beneficiary, a mechanism that has been called reciprocal cooperation. However, what makes this mechanism so powerful also has an evolutionary downside. Reciprocal cooperation faces a chicken-and-egg problem of the same kind as communication: it requires two functions to evolve at the same time—cooperation and response to cooperation. As a result, it can only emerge if one side first evolves for another reason, and is then recycled into a reciprocal function. Developing an evolutionary model in which we make use of machine learning techniques, we show that this occurs if the fact to cooperate and reward others’ cooperation become general abilities that extend beyond the set of contexts for which they have initially been selected. Drawing on an evolutionary analogy with the concept of generalization, we identify the conditions necessary for this to happen. This allows us to understand the peculiar distribution of reciprocal cooperation in the wild, virtually absent in most species—or limited to situations where individuals have partially overlapping interests, but pervasive in the human species.

scholarly journals Prediction models for high risk of suicide in Korean adolescents using machine learning techniques

PLoS ONE ◽  
2019 ◽  
Vol 14 (6) ◽  
pp. e0217639 ◽  
Author(s):  
Jun Su Jung ◽  
Sung Jin Park ◽  
Eun Young Kim ◽  
Kyoung-Sae Na ◽  
Young Jae Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Prediction Models ◽  
Machine Learning Techniques ◽  
Korean Adolescents ◽  
Learning Techniques

scholarly journals The emergence of cooperation by evolutionary generalization

2021 ◽  
Author(s):  
Félix Geoffroy ◽  
Jean-Baptiste André
Keyword(s):  
Machine Learning ◽  
Evolutionary Model ◽  
Machine Learning Techniques ◽  
Cooperative Behaviour ◽  
Human Species ◽  
Learning Techniques ◽  
In The Wild ◽  
Emergence Of Cooperation ◽  
Evolutionary Analogy ◽  
Evolutionarily Stable

In principle, any cooperative behaviour can be evolutionarily stable as long as it is incentivized by a reward from the beneficiary, a mechanism that has been called reciprocal cooperation. However, what makes this mechanism so powerful also has an evolutionary downside. Reciprocal cooperation faces a chicken-and-egg problem of the same kind as communication: it requires two functions to evolve at the same time –cooperation and response to cooperation. As a result, it can only emerge if one side first evolves for another reason, and is then recycled into a reciprocal function. Developping an evolutionary model in which we make use of machine learning techniques, we show that this occurs if the fact to cooperate and reward others’ cooperation become general abilities that extend beyond the set of contexts for which they have initially been selected. Drawing on an evolutionary analogy with the concept of generalization, we identify the conditions necessary for this to happen. This allows us to understand the peculiar distribution of reciprocal cooperation in the wild, virtually absent in most species –or limited to situations where individuals have partially overlapping interests, but pervasive in the human species

scholarly journals Can we screen for pancreatic cancer? Identifying a sub-population of patients at high risk of subsequent diagnosis using machine learning techniques applied to primary care data

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0251876
Author(s):  
Ananya Malhotra ◽  
Bernard Rachet ◽  
Audrey Bonaventure ◽  
Stephen P. Pereira ◽  
Laura M. Woods
Keyword(s):  
Machine Learning ◽  
Primary Care ◽  
Pancreatic Cancer ◽  
High Risk ◽  
Pancreatic Cancer Patient ◽  
Population Based ◽  
Machine Learning Techniques ◽  
Public Health Burden ◽  
Learning Techniques ◽  
Number Of Patients

Background Pancreatic cancer (PC) represents a substantial public health burden. Pancreatic cancer patients have very low survival due to the difficulty of identifying cancers early when the tumour is localised to the site of origin and treatable. Recent progress has been made in identifying biomarkers for PC in the blood and urine, but these cannot be used for population-based screening as this would be prohibitively expensive and potentially harmful. Methods We conducted a case-control study using prospectively-collected electronic health records from primary care individually-linked to cancer registrations. Our cases were comprised of 1,139 patients, aged 15–99 years, diagnosed with pancreatic cancer between January 1, 2005 and June 30, 2009. Each case was age-, sex- and diagnosis time-matched to four non-pancreatic (cancer patient) controls. Disease and prescription codes for the 24 months prior to diagnosis were used to identify 57 individual symptoms. Using a machine learning approach, we trained a logistic regression model on 75% of the data to predict patients who later developed PC and tested the model’s performance on the remaining 25%. Results We were able to identify 41.3% of patients < = 60 years at ‘high risk’ of developing pancreatic cancer up to 20 months prior to diagnosis with 72.5% sensitivity, 59% specificity and, 66% AUC. 43.2% of patients >60 years were similarly identified at 17 months, with 65% sensitivity, 57% specificity and, 61% AUC. We estimate that combining our algorithm with currently available biomarker tests could result in 30 older and 400 younger patients per cancer being identified as ‘potential patients’, and the earlier diagnosis of around 60% of tumours. Conclusion After further work this approach could be applied in the primary care setting and has the potential to be used alongside a non-invasive biomarker test to increase earlier diagnosis. This would result in a greater number of patients surviving this devastating disease.

scholarly journals Individual prediction of long-term outcome in adolescents at ultra-high risk for psychosis: Applying machine learning techniques to brain imaging data

2016 ◽  
Vol 38 (2) ◽  
pp. 704-714 ◽  
Author(s):  
Sanne de Wit ◽  
Tim B. Ziermans ◽  
M. Nieuwenhuis ◽  
Patricia F. Schothorst ◽  
Herman van Engeland ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Machine Learning Techniques ◽  
Imaging Data ◽  
Term Outcome ◽  
Long Term Outcome ◽  
Learning Techniques ◽  
Ultra High Risk ◽  
Brain Imaging Data

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques
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