Abstracts Track 2021

Nr: 3

IoT Natural Gas Monitoring


Hsi-Jen J. Yeh, William D. Cook, Natalie Palos, Chelsea Yeh and Kyle Yeh

Abstract: We developed a system to reduce the cost of monitoring potential gas leaks and streamline the process of gathering/analyzing data collected. In 2015, Aliso Canyon in the United States was the center of the largest natural gas leak in history: releasing 97,000 tons of methane and 7,000 tons of ethane. Currently, gas monitoring systems are either not very robust or very expensive. To reduce the risk of a major natural gas leak incident, our team developed a low-cost Internet of Things (IoT) Monitoring System. We create a flammable/toxic gas monitoring system that is low-cost, can be deployed over a wide area, has robust communication, and can be provisioned quickly and easily. Essentially, the installer can simply place and secure a self-contained portable monitoring unit in any appropriate location, push/tap a button on their smartphone/tablet, and the new node will configure itself to be part of the monitoring system. Our IoT gas monitoring system consists of a network of sensor nodes placed around potential containers, transport facilities, pipelines, or openings that might leak. The first phase of the system involves sensor node modules, a communications network for the nodes, and a central interface. The nodes are built with the relevant gas sensor (methane, ethane, etc.), solar panels and batteries for power, and inexpensive communication modules. The system provides connectivity for each node’s data using a variety of communication standards such as Bluetooth, WiFi, or LoRa, depending on the distance between the nodes. Each node’s data is relayed toward the central node and aggregated to the cloud database. The user interface is built to work on a multitude of platforms (Android, iOS, Windows, macOS), providing data access to the cloud database and thus the geographic reading of each node. In this way, we provide real-time data on sudden changes in gas storage and transportation facilities and provide rapid response times. While this system is targeted toward the natural gas industry, it severs as a template for low-cost monitoring systems in various other industries and fields.

Area 1 - Internet of Things (IoT) Applications

Nr: 7

In situ: Chip under Your Skin?


Ulrike Hugl

Abstract: Non-medical human chips are en vogue. Human chips – also known as chip implants, injectable or implantable ID chips, as well as human RFID chips – has been commercially available in manifold kinds since the 1970s. They mainly are based on RFID (radio frequency identification)-technology and build a new branch of the RFID, so-called NFC (near field communication). In the U.S., patented in the late 90s by the Food and Drug Administration (FDA), such a human implant is normally a tiny two-way radio, in the size of a rice grain, able to contain some types of information. Smart phones as NFC devices can act as electronic identity keycards or documents, for example, used for contactless mobile or other payment systems, they may also supplement systems such as electronic ticket smart cards or credit cards. Other applications in the “hobbyist”-field are for example starting computers, cars, manage smart home applications and so on. Such a human chip typically contains a unique 16-digit ID number; it can be used to retrieve information contained in an external database (e.g. personal identification, allergies, contact information and others). Furthermore, implants can be easily implanted by interested users. Meanwhile, costs are low and range mainly from 30 to 150 – but also up to about 200 Dollars. Today, worldwide about 50,000 people have elected to have a non-medical chip implant. In Europe, for example, estimated 5,000 Swedes are using it and are replacing keycards for implants to use it for e-ticketing, for medical (emergency contact) or other reasons. This contribution will answer the following questions: What are the main commercial players in the field? What about the related scholarly Human-Computer-Interaction (HCI) community debate? What trends and potential triggers for human chipping can be identified from diverse fields like the biohacking scene, the military field, so-called implant parties, and others? What happened resp. currently happens in the employees’ human chip ‘market’? And: What security, privacy and ethical issues related to chip implants should be discussed?

Area 2 - Security, Privacy and Trust

Nr: 8

A Solution to Minimise the Success of Phishing Attempts using the Effects of Human Behaviour and Emotions on Falling into a Phishing Scam


Hossein Abroshan, Jan Devos, Geert Poels and Eric Laermans

Abstract: Phishing is a social engineering scam that can cause data loss, reputational damages, identity theft, money loss, and many other damages to people and organisations. Multiple studies showed the effects of human behaviour, such as risk-taking and decision making, on Internet users' security behaviour. Researchers also investigated how email users' behaviour can influence the success of a phishing attempt. Moreover, the number of phishing attempts has been increased rapidly since the beginning of the COVID-19 outbreak. Several studies demonstrated the effects of the COVID-19 pandemic on human behaviour, impacting phishing attempts' success. Organisations can use the results of these studies to find potential high-risk users by measuring the users' behaviour and emotions, which are associated with falling into a phishing scam. In this study, we have developed a solution and guideline using previous studies to identify risky users (i.e., those at risk of clicking on phishing links). The solution will then suggest or assigns proper mitigation actions for those users. The system contains measurement (psychological scales), scoring (machine learning), and mitigation modules that can become more mature and accurate over time. Furthermore, specific situations, such as the pandemic, is also considered in the solution- that is, when a situation like the COVID-19 pandemic happens, the solution will consider the impacted human emotions in finding the high-risk users and might suggest other types of mitigations. We have used regression models for the machine learning module. The proposed solution will help organisations focus more on high-risk users and reduce cyber risks. This solution, however, should be used in combination with technical anti-phishing systems and cybersecurity awareness training campaigns to achieve better results.