Intrusion Prevention through Optimal Stopping

We study automated intrusion prevention using reinforcement learning. Following a novel approach, we formulate the problem of intrusion prevention as an (optimal) multiple stopping problem. This formulation gives us insight into the structure of optimal policies, which we show to have threshold properties. For most practical cases, it is not feasible to obtain an optimal defender policy using dynamic programming. We therefore develop a reinforcement learning approach to approximate an optimal threshold policy. We introduce T-SPSA, an efficient reinforcement learning algorithm that learns threshold policies through stochastic approximation. We show that T-SPSA outperforms state-of-the-art algorithms for our use case. Our overall method for learning and validating policies includes two systems: a simulation system where defender policies are incrementally learned and an emulation system where statistics are produced that drive simulation runs and where learned policies are evaluated. We show that this approach can produce effective defender policies for a practical IT infrastructure.Comment: Preprint; Submitted to IEEE for review. major revision 1/4 2022. arXiv admin note: substantial text overlap with arXiv:2106.0716

Learning Intrusion Prevention Policies through Optimal Stopping

We study automated intrusion prevention using reinforcement learning. In a novel approach, we formulate the problem of intrusion prevention as an optimal stopping problem. This formulation allows us insight into the structure of the optimal policies, which turn out to be threshold based. Since the computation of the optimal defender policy using dynamic programming is not feasible for practical cases, we approximate the optimal policy through reinforcement learning in a simulation environment. To define the dynamics of the simulation, we emulate the target infrastructure and collect measurements. Our evaluations show that the learned policies are close to optimal and that they indeed can be expressed using thresholds.Comment: 10 page

Optimal Observation-Intervention Trade-Off in Optimisation Problems with Causal Structure

We consider the problem of optimising an expensive-to-evaluate grey-box objective function, within a finite budget, where known side-information exists in the form of the causal structure between the design variables. Standard black-box optimisation ignores the causal structure, often making it inefficient and expensive. The few existing methods that consider the causal structure are myopic and do not fully accommodate the observation-intervention trade-off that emerges when estimating causal effects. In this paper, we show that the observation-intervention trade-off can be formulated as a non-myopic optimal stopping problem which permits an efficient solution. We give theoretical results detailing the structure of the optimal stopping times and demonstrate the generality of our approach by showing that it can be integrated with existing causal Bayesian optimisation algorithms. Experimental results show that our formulation can enhance existing algorithms on real and synthetic benchmarks

Early drug use of dapagliflozin prescribed by general practitioners and diabetologists in Germany.

OBJECTIVES: Dapagliflozin is an inhibitor of the human sodium-glucose co-transporter 2 (SGLT2) that has been shown to improve glycaemic control in patients with type 2 diabetes mellitus (T2DM). This study aimed to evaluate the characteristics and treatment patterns of dapagliflozin users in comparison to users of other anti-diabetic (AD) treatments in Germany. METHODS: Data from patients with T2DM initiating at least one prescription for dapagliflozin or other AD therapy between November 2012 and April 2014 were collected from the IMS German Disease Analyzer database. RESULTS: The use of dapagliflozin combination therapy (n=1034; 74%) was more common than monotherapy (n=371; 26%). In comparison with other AD therapy users, a higher percentage of dapagliflozin users were ⩽64years of age (62.3% vs. 36.4%), and a higher proportion were male (59.1% vs. 53.6%). The average duration of diabetes was comparable between dapagliflozin patients and other AD therapy users (5.7yearsvs. 5.5years), however higher levels of HbA1c were found in dapagliflozin users (8.2% (66mmol/mol) vs. 7.5% (58mmol/mol). For the vast majority (71.5% of 10mg dapagliflozin users and 88.9% of 5mg users), dapagliflozin was prescribed in combination with other AD therapy. CONCLUSIONS: Patients starting on dapagliflozin differed in several demographic and health-related respects to patients starting another AD therapy during the same period. Dapagliflozin was predominantly used as a component of combination therapy, adding on to existing therapy. After initiation, switching to other AD treatments or adding to therapy was comparatively rare during the first year

Deep Text Mining of Instagram Data Without Strong Supervision

With the advent of social media, our online feeds increasingly consist of short, informal, and unstructured text. This textual data can be analyzed for the purpose of improving user recommendations and detecting trends. Instagram is one of the largest social media platforms, containing both text and images. However, most of the prior research on text processing in social media is focused on analyzing Twitter data, and little attention has been paid to text mining of Instagram data. Moreover, many text mining methods rely on annotated training data, which in practice is both difficult and expensive to obtain. In this paper, we present methods for unsupervised mining of fashion attributes from Instagram text, which can enable a new kind of user recommendation in the fashion domain. In this context, we analyze a corpora of Instagram posts from the fashion domain, introduce a system for extracting fashion attributes from Instagram, and train a deep clothing classifier with weak supervision to classify Instagram posts based on the associated text. With our experiments, we confirm that word embeddings are a useful asset for information extraction. Experimental results show that information extraction using word embeddings outperforms a baseline that uses Levenshtein distance. The results also show the benefit of combining weak supervision signals using generative models instead of majority voting. Using weak supervision and generative modeling, an F1 score of 0.61 is achieved on the task of classifying the image contents of Instagram posts based solely on the associated text, which is on level with human performance. Finally, our empirical study provides one of the few available studies on Instagram text and shows that the text is noisy, that the text distribution exhibits the long-tail phenomenon, and that comment sections on Instagram are multi-lingual.Comment: 8 pages, 5 figures. Pre-print for paper to appear in conference proceedings for the Web Intelligence Conferenc

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