Non-zero-sum, Adversarial Detection Games in Network Security

In this dissertation we propose two novel non-zero-sum, adversarial detection games motivated by problems in network security. First we consider a local mean field, interdependent detection game between a network of defenders and a strategic attacker. Each defender chooses a detection threshold to test for the presence of a botnet infection, which can propagate between defenders if undetected. In order to avoid detection, the attacker balances stealth and aggression in his strategic utilization of the compromised network. We compare selfish, decentralized defenders to centrally planned defenders in order to examine the effects of network externalities on detection strategies. It is found that for fixed attack strategies, decentralized defenders choose thresholds that are either too low or too high than is socially optimal. When the attacker is strategic and the defenders are homogeneous, we prove the existence of a pure Nash equilibrium in both decentralized and centralized games. Through numerical approximations of the equilibria, we find that decentralized defenders can outperform a central planner in such games. It is observed that pure Nash equilibria often fail to exist when defenders are heterogeneous in their cost functions. In this case sufficient conditions are given to guarantee a Stackelberg equilibria. Next a two-player, non-zero-sum, sequential detection game based on Wald's SPRT is presented. A defender seeks to sequentially detect the presence of an attacker via the drift of a stochastic process. The detection process is complicated by the attacker's ability to strategically choose the drift of the observed stochastic process. We prove the existence of pure Nash equilibria and give sufficient conditions for the existence of Stackelberg equilibria with the defender as leader. It is shown that both low false positive costs and high prior probabilities of intrusion lead to an infinite number of Nash equilibria in which the defender makes no observations. Conversely both high false positive costs and low prior probabilities of intrusion lead to a finite number of non-trivial Nash equilibria. Through numerical examples we see that it is possible for the defender to do better using a Stackelberg equilibrium strategy than a Nash equilibrium strategy

Stochastic Gradient-Based Distributed Bayesian Estimation in Cooperative Sensor Networks

Distributed Bayesian inference provides a full quantification of uncertainty offering numerous advantages over point estimates that autonomous sensor networks are able to exploit. However, fully-decentralized Bayesian inference often requires large communication overheads and low network latency, resources that are not typically available in practical applications. In this paper, we propose a decentralized Bayesian inference approach based on stochastic gradient Langevin dynamics, which produces full posterior distributions at each of the nodes with significantly lower communication overhead. We provide analytical results on convergence of the proposed distributed algorithm to the centralized posterior, under typical network constraints. We also provide extensive simulation results to demonstrate the validity of the proposed approach

Improving five-year survival prediction via multitask learning across HPV-related cancers.

Oncology is a highly siloed field of research in which sub-disciplinary specialization has limited the amount of information shared between researchers of distinct cancer types. This can be attributed to legitimate differences in the physiology and carcinogenesis of cancers affecting distinct anatomical sites. However, underlying processes that are shared across seemingly disparate cancers probably affect prognosis. The objective of the current study is to investigate whether multitask learning improves 5-year survival cancer patient survival prediction by leveraging information across anatomically distinct HPV related cancers. Data were obtained from the Surveillance, Epidemiology, and End Results (SEER) program database. The study cohort consisted of 29,768 primary cancer cases diagnosed in the United States between 2004 and 2015. Ten different cancer diagnoses were selected, all with a known association with HPV risk. In the analysis, the cancer diagnoses were categorized into three distinct topography groups of varying specificity. The most specific topography grouping consisted of 10 original cancer diagnoses differentiated by the first two digits of the ICD-O-3 topography code. The second topography grouping consisted of cancer diagnoses categorized into six distinct organ groups. Finally, the third topography grouping consisted of just two groups, head-neck cancers and ano-genital cancers. The tasks were to predict 5-year survival for patients within the different topography groups using 14 predictive features which were selected among descriptive variables available in the SEER database. The information from the predictive features was shared between tasks in three different ways, resulting in three distinct predictive models: 1) Information was not shared between patients assigned to different tasks (single task learning); 2) Information was shared between all patients, regardless of task (pooled model); 3) Only relevant information was shared between patients grouped to different tasks (multitask learning). Prediction performance was evaluated with Brier scores. All three models were evaluated against one another on each of the three distinct topography-defined tasks. The results showed that multitask classifiers achieved relative improvement for the majority of the scenarios studied compared to single task learning and pooled baseline methods. In this study, we have demonstrated that sharing information among anatomically distinct cancer types can lead to improved predictive survival models

sj-pdf-1-smm-10.1177_09622802221122390 - Supplemental material for Hierarchical continuous-time inhomogeneous hidden Markov model for cancer screening with extensive followup data

Supplemental material, sj-pdf-1-smm-10.1177_09622802221122390 for Hierarchical continuous-time inhomogeneous hidden Markov model for cancer screening with extensive followup data by Rui Meng, Braden Soper, Herbert KH Lee, Jan F Nygård, and Mari Nygård in Statistical Methods in Medical Research</p

Gerry Richter, Groundfish Advisory Panel

A review of data-moderate assessments was conducted by a STAR Review Panel (Panel) at th

COVID-19 outcomes in patients with cancer: Findings from the University of California health system database.

BackgroundThe interaction between cancer diagnoses and COVID-19 infection and outcomes is unclear. We leveraged a state-wide, multi-institutional database to assess cancer-related risk factors for poor COVID-19 outcomes.MethodsWe conducted a retrospective cohort study using the University of California Health COVID Research Dataset, which includes electronic health data of patients tested for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) at 17 California medical centers. We identified adults tested for SARS-CoV-2 from 2/1/2020-12/31/2020 and selected a cohort of patients with cancer. We obtained demographic, clinical, cancer type, and antineoplastic therapy data. The primary outcome was hospitalization within 30d after the first positive SARS-CoV-2 test. Secondary outcomes were SARS-CoV-2 positivity and severe COVID-19 (intensive care, mechanical ventilation, or death within 30d after the first positive test). We used multivariable logistic regression to identify cancer-related factors associated with outcomes.ResultsWe identified 409,462 patients undergoing SARS-CoV-2 testing. Of 49,918 patients with cancer, 1781 (3.6%) tested positive. Patients with cancer were less likely to test positive (RR 0.70, 95% CI: 0.67-0.74, p&nbsp;&lt;&nbsp;0.001). Among the 1781 SARS-CoV-2-positive patients with cancer, BCR/ABL-negative myeloproliferative neoplasms (RR 2.15, 95% CI: 1.25-3.41, p&nbsp;=&nbsp;0.007), venetoclax (RR 2.96, 95% CI: 1.14-5.66, p&nbsp;=&nbsp;0.028), and methotrexate (RR 2.72, 95% CI: 1.10-5.19, p&nbsp;=&nbsp;0.032) were associated with greater hospitalization risk. Cancer and therapy types were not associated with severe COVID-19.ConclusionsIn this large, diverse cohort, cancer was associated with a decreased risk of SARS-CoV-2 positivity. Patients with BCR/ABL-negative myeloproliferative neoplasm or receiving methotrexate or venetoclax may be at increased risk of hospitalization following SARS-CoV-2 infection. Mechanistic and comparative studies are needed to validate findings