A simulation-based procedure to estimate base rates from Covid-19 antibody test results I: Deterministic test reliabilities

We design a procedure (the complete Python code may be obtained at https://github.com/abhishta91/antibody_montecarlo) using Monte Carlo (MC) simulation to establish the point estimators described below and confidence intervals for the base rate of occurence of an attribute (e.g., antibodies against Covid-19) in an aggregate population (e.g., medical care workers) based on a test. The requirements for the procedure are the test’s sample size (N) and total number of positives (X), and the data on test’s reliability.The modus generates the largest frequency of observations in the MC simulation with precisely the number of test positives (maximum- likelihood estimator). The median is the upper bound of the set of priors accounting for half of the total relevant observations in the MC simulation with numbers of positives identical to the test’s number of positives.Our rather preliminary findings are:• The median and the confidence intervals suffice universally.• The estimator X/N may be outside of the two-sided 95% confidence interval.• Conditions such that the modus, the median and another promising estimator which takes the reliability of the test into account, are quite close.• Conditions such that the modus and the latter estimator must be regarded as logically inconsistent.• Conditions inducing rankings among various estimators relevant for issues concerning over- or underestimation

Understanding Digital Sovereignty: A Textual Analysis Of EU Policy Documents

The rapid growth of digital transformations globally has compelled European policymakers to discuss the possibility of a “Digitally Sovereign” model of the Internet. However, both policymakers and researchers have yet to solidify the concept behind such an ecosystem. While recent policies on digital issues often center around "Digital Sovereignty," there remains a lack of clear distinction between this term and similar concepts such as "Data Sovereignty" and "Technological Sovereignty". This extended abstract proposes a thorough textual analysis of EU policy documents to grasp the values and principles behind Digital Sovereignty.<br/

Deception in double extortion ransomware attacks:An analysis of profitability and credibility

Ransomware attacks have evolved with criminals using double extortion schemes, where they signal data exfiltration to inflate ransom demands. This development is further complicated by information asymmetry, where victims are compelled to respond to ambiguous and often deceptive signals from attackers. This study explores the complex interactions between criminals and victims during ransomware attacks, especially focusing on how data exfiltration is communicated. We use a signaling game to understand the strategies both parties use when dealing with uncertain information. We identify five distinct equilibria, each characterized by the criminals' varied approaches to signaling data exfiltration, influenced by the strategic parameters inherent in each attack scenario. Calibrating the game parameters with real-world like values, we identify the most probable equilibrium, offering insights into anticipated ransom amounts and corresponding payoffs for both victims and criminals. Our findings suggest criminals are likely to claim data exfiltration, true or not, highlighting a strategic advantage for intensifying attack efforts. The study underscores the need for victims' caution towards criminals' claims and highlights the unintended consequences of policies making false claims costlier for criminals.</p

Business Model of a Botnet

Botnets continue to be an active threat against firms or companies and individuals worldwide. Previous research regarding botnets has unveiled information on how the system and their stakeholders operate, but an insight on the economic structure that supports these stakeholders is lacking. The objective of this research is to analyse the business model and determine the revenue stream of a botnet owner. We also study the botnet life-cycle and determine the costs associated with it on the basis of four case studies. We conclude that building a full scale cyber army from scratch is very expensive where as acquiring a previously developed botnet requires a little cost. We find that initial setup and monthly costs were minimal compared to total revenue.Comment: Proceedings of 2018, 26th Euromicro International conference on Parallel, Distributed, and Network-Based Processing (PDP

Comparative Analytics on Chilli Plant Disease using Machine Learning Techniques

This thesis concerns the detection of diseases in chilli plants using machine learning techniques. Three algorithms, viz., Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Multi-Layer Perceptron (MLP), and their variants have been employed. Chilli-producing countries, India, Mexico, China, Indonesia, Spain, the United States, and Turkey. India has the world’s largest chilli production of about 49% (according to 2020). Andhra Pradesh (Guntur) is the largest market in India, where their varieties are more popular for pungency and color. This study classifies five kinds of diseases that affect the chilli, namely, leaf spot, whitefly, yellowish, healthy, and leaf curl. A comparison among deep learning techniques CNN, RNN, MLP, and their variants to detect the chilli plant disease. 400 images are taken from the Kaggle dataset, classified into five classes, and used for further analytics. Each image is analyzed with CNN (with three variants), RNN (with three variants), and MLP (with two variants). Comparative analytics shows that the higher number of epochs implies a higher execution time and vice versa for lower values. The research implies that MLP-1 (36.08 in seconds) technique is the fastest, requiring 15 epochs. More hidden layers imply higher execution time. This research implies that the MLP-1 technique yields the lowest number of hidden layers. Thereby giving the highest execution time (349.1 in seconds) for RNN-3. Lastly, RNN and MLP have the highest accuracy of 80% (for all variants). The inferences are that these approaches could be used for disease management in terms of the use of proper pesticides in the right quantity using proper spraying techniques. Based on these conclusions, an agricultural scientist can propose a set of right regulations and guidelines

How Attackers Determine the Ransom in Ransomware Attacks

Ransomware may lead to massive economic damage to victims [13]. However, it is still unclear how attackers determine the amount of ransom. In this poster we empirically study the ransom requested by attackers in ransomware attacks. We analysed 371 ransomware attacks reported to the Dutch Police between 2019 and 2021. Our results indicate that attacker’s effort and opportunity are important predictors for the ransom requested. The goal of the poster is to invite other researchers for collaboration