Random Forest in Whitelist-Based ATM Security

Accelerated by the COVID-19 pandemic, the trend of highly-sophisticated logical attacks on Automated Teller Machines (ATMs) is ever-increasing nowadays. Due to the nature of attacks, it is common to use zero-day protection for the devices. The most secure solutions available are using whitelist-based policies, which are extremely hard to configure. This article presents the concept of a semi-supervised decision support system based on the Random forest algorithm for generating a whitelist-based security policy using the ATM usage data. The obtained results confirm that the Random forest algorithm is effective in such scenarios and can be used to increase the security of the ATMs.

Random Forest in Whitelist-Based ATM Security

Accelerated by the COVID-19 pandemic, the trend of highly-sophisticated logical attacks on Automated Teller Machines (ATMs) is ever-increasing nowadays. Due to the nature of attacks, it is common to use zero-day protection for the devices. The most secure solutions available are using whitelist-based policies, which are extremely hard to configure. This article presents the concept of a semi-supervised decision support system based on the Random forest algorithm for generating a whitelist-based security policy using the ATM usage data. The obtained results confirm that the Random forest algorithm is effective in such scenarios and can be used to increase the security of the ATMs. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Social Engineering Exploits in Automotive Software Security: Modeling Humantargeted Attacks with SAM

Security cannot be implemented into a system retrospectively without considerable effort, so security must be taken into consideration already at the beginning of the system development. The engineering of automotive software is by no means an exception to this rule. For addressing automotive security, the AUTOSAR and EAST-ADL standards for domain-specific system and component modeling provide the central foundation as a start. The EASTADL extension SAM enables fully integrated security modeling for traditional feature-targeted attacks. Due to the COVID-19 pandemic, the number of cyber-attacks has increased tremendously and of these, about 98 percent are based on social engineering attacks. These social engineering attacks exploit vulnerabilities in human behaviors, rather than vulnerabilities in a system, to inflict damage. And these social engineering attacks also play a relevant but nonetheless regularly neglected role for automotive software. The contribution of this paper is a novel modeling concept for social engineering attacks and their criticality assessment integrated into a general automotive software security modeling approach. This makes it possible to relate social engineering exploits with feature-related attacks. To elevate the practical usage, we implemented an integration of this concept into the established, domain-specific modeling tool MetaEdit+. The tool support enables collaboration between stakeholders, calculates vulnerability scores, and enables the specification of security objectives and measures to eliminate vulnerabilities. © ESREL 2021. Published by Research Publishing, Singapore.

Putting Security on the Table: The Digitalisation of Security Tabletop Games and its Challenging Aftertaste

IT-Security Tabletop Games for developers have been available in analog format;with the COVID-19 pandemic, interest in collaborative remote security games has increased. In this paper, we propose a methodology to evaluate the impact of a (remote) security game-based intervention on developers. The study design consists of the respective intervention, three questionnaires, and a small open interview guide for a focus group. A validated self-efficacy scale is used as a proxy for measuring effects on participants' ability to develop secure software. We tested this design with 9 participants (expert and novice developers and security experts) as part of a small feasibility study to understand the challenges and limitations of remote tabletop games. We describe how we selected and digitalised three security tabletop games, and report the qualitative findings from our evaluation. Setting up and running the virtual tabletop games turned out to be more challenging and complex for both moderator and participants than we expected. Completing the games required patience and persistence, and social interaction was limited. Our findings can be helpful in building and evaluating a better, more comprehensive, technically sound and issue-specific game-based training measure for developers. The methodology can be used by researchers to evaluate existing and new game designs. © 2022 IEEE.

Supercomputing Enabled Deployable Analytics for Disaster Response

First responders and other forward deployed essential workers can benefit from advanced analytics. Limited network access and software security requirements prevent the usage of standard cloud based microservice analytic platforms that are typically used in industry. One solution is to precompute a wide range of analytics as files that can be used with standard preinstalled software that does not require network access or additional software and can run on a wide range of legacy hardware. In response to the COVID-19 pandemic, this approach was tested for providing geo-spatial census data to allow quick analysis of demographic data for better responding to emergencies. These data were processed using the MIT SuperCloud to create several thousand Google Earth and Microsoft Excel files representative of many advanced analytics. The fast mapping of census data using Google Earth and Microsoft Excel has the potential to give emergency responders a powerful tool to improve emergency preparedness. Our approach displays relevant census data (total population, population under 15, population over 65, median age) per census block, sorted by county, through a Microsoft Excel spreadsheet (xlsx file) and Google Earth map (kml file). The spreadsheet interface includes features that allow users to convert between different longitude and latitude coordinate units. For the Google Earth files, a variety of absolute and relative colors maps of population density have been explored to provide an intuitive and meaningful interface. Using several hundred cores on the MIT SuperCloud, new analytics can be generated in a few minutes. © 2021 IEEE.