Cybersecurity Awareness Platform with Virtual Coach and Automated Challenge Assessment

Over the last years, the number of cyber-attacks on industrial control systems has been steadily increasing. Among several factors, proper software development plays a vital role in keeping these systems secure. To achieve secure software, developers need to be aware of secure coding guidelines and secure coding best practices. This work presents a platform geared towards software developers in the industry that aims to increase awareness of secure software development. The authors also introduce an interactive game component, a virtual coach, which implements a simple artificial intelligence engine based on the laddering technique for interviews. Through a survey, a preliminary evaluation of the implemented artifact with real-world players (from academia and industry) shows a positive acceptance of the developed platform. Furthermore, the players agree that the platform is adequate for training their secure coding skills. The impact of our work is to introduce a new automatic challenge evaluation method together with a virtual coach to improve existing cybersecurity awareness training programs. These training workshops can be easily held remotely or off-line.Comment: Preprint accepted for publication at the 6th Workshop On The Security Of Industrial Control Systems & Of Cyber-Physical Systems (CyberICPS 2020

Solving Data Mismatches in Bioinformatics Workflows by Generating Data Converters

International audienceHeterogeneity of data and data formats in bioinformatics entail mismatches between inputs and outputs of different services, making it difficult to compose them into workflows. To reduce those mismatches, bioinformatics platforms propose ad'hoc converters, called shims. When shims are written by hand, they are time-consuming to develop, and cannot anticipate all needs. When shims are automatically generated, they miss transformations, for example data composition from multiple parts, or parallel conversion of list elements. This article proposes to systematically detect convertibility from output types to input types. Convertibility detection relies on a rule system based on abstract types, close to XML Schema. Types allow to abstract data while precisely accounting for their composite structure. Detection is accompanied by an automatic generation of converters between input and output XML data. % We show the applicability of our approach by abstracting concrete bioinformatics types (e.g., complex biosequences) for a number of bioinformatics services (e.g., blast). We illustrate how our automatically generated converters help to resolve data mismatches when composing workflows. % We conducted an experiment on bioinformatics services and datatypes, using an implementation of our approach, as well as a survey with domain experts. The detected convertibilities and produced converters were validated as relevant from a biological point of view. Furthermore the automatically produced graph of potentially compatible services exhibited a connectivity higher than with the ad'hoc approaches. Indeed, the experts discovered unknown possible connexions