The Internet of Things (IoT) devices have been integrated into almost all everyday applications of human life such as healthcare, transportation and agriculture. This widespread adoption of IoT has opened a large threat landscape to computer networks, leaving security gaps in IoT-enabled networks. These resource-constrained devices lack sufficient security mechanisms and become the weakest link in our in computer networks and jeopardize systems and data. To address this issue, Intrusion Detection Systems (IDS) have been proposed as one of many tools to mitigate IoT related intrusions. While IDS have proven to be a crucial tools for threat detection, their dependence on labeled data and their high computational costs have become obstacles to real life adoption. In this work, we present IoT-PRIDS, a new framework equipped with a host-based anomaly-based intrusion detection system that leverages “packet representations” to understand the typical behavior of devices, focusing on their communications, services, and packet header values. It is a lightweight non-ML model that relies solely on benign network traffic for intrusion detection and offers a practical way for securing IoT environments. Our results show that this model can detect the majority of abnormal flows while keeping false alarms at a minimum and is promising to be used in real-world applications.

The lack of appropriate cyber security measures deployed on Internet of Things (IoT) makes these devices prone to security issues. Consequently, the timely identification and detection of these compromised devices become crucial. Machine learning (ML) models which are used to monitor devices in a network have made tremendous strides. However, most of the research in profiling and identification uses the same data for training and testing. Hence, a slight change in the data renders most learning algorithms to work poorly. In this article, we study a transferability approach based on the concept of transductive transfer learning for IoT device profiling and identification. Notably, this type of transfer learning works by explicitly assigning labels to the test data in the target domain by using the test feature space in the target domain, with training data from the source domain. Specifically, we propose a three-component system comprising: 1) the device type identification; 2) the vulnerability assessment; and 3) the visualization module. The device type identification component uses the underlying concept of transductive transfer learning where the trained model is transferred to a remote lab for testing. A variety of ML models are evaluated with respect to accuracy, precision, recall, and F1-score in order to determine which are the most suitable for the proposed transferability profiling. Furthermore, the vulnerability of the predicted device type is also assessed by using three vulnerability databases: 1) Vulners; 2) National Vulnerability Database (NVD); and 3) IBM X-Force. Finally, the results from the vulnerability assessment are visualized and displayed on a dashboard.

The proliferation of heterogeneous Internet of things (IoT) devices connected to the Internet produces several operational and security challenges, such as monitoring, detecting, and recognizing millions of interconnected IoT devices. Network and system administrators must correctly identify which devices are functional, need security updates, or are vulnerable to specific attacks. IoT profiling is an emerging technique to identify and validate the connected devices’ specific behaviour and isolate the suspected and vulnerable devices within the network for further monitoring. This article provides a comprehensive review of various IoT device profiling methods and provides a clear taxonomy for IoT profiling techniques based on different security perspectives. We first investigate several current IoT device profiling techniques and their applications. Next, we analyzed various IoT device vulnerabilities, outlined multiple features, and provided detailed information to implement profiling algorithms’ risk assessment/mitigation stage. By reviewing approaches for profiling IoT devices, we identify various state-of-the-art methods that organizations of different domains can implement to satisfy profiling needs. Furthermore, this article also discusses several machine learning and deep learning algorithms utilized for IoT device profiling. Finally, we discuss challenges and future research possibilities in this domain.

Most Human-Computer Interaction (HCI) researchers are accustomed to the process of formal ethics review for their evaluation or field trial protocol. Although this process varies by country, the underlying principles are universal. While this process is often a formality, for field research or lab-based studies with vulnerable users, formal ethics requirements can be challenging to navigate -- a common occurrence in the social sciences; yet, in many cases, foreign to HCI researchers. Nevertheless, with the increase in new areas of research such as mobile technologies for marginalized populations or assistive technologies, this is a current reality. In this paper we present our experiences and challenges in conducting several studies that evaluate interactive systems in difficult settings, from the perspective of the ethics process. Based on these, we draft recommendations for mitigating the effect of such challenges to the ethical conduct of research. We then issue a call for interaction researchers, together with policy makers, to refine existing ethics guidelines and protocols in order to more accurately capture the particularities of such field-based evaluations, qualitative studies, challenging lab-based evaluations, and ethnographic observations.

For a very large number of adults, tasks such as reading. understanding, and using everyday items are a challenge. Although many community-based organizations offer resources and support for adults with limited literacy skills. current programs have difficulty reaching and retaining those that would benefit most. In this paper we present the findings of an exploratory study aimed at investigating how a technological solution that addresses these challenges is received and adopted by adult learners. For this, we have developed a mobile application to support literacy programs and to assist low-literacy adults in today's information-centric society. ALEX© (Adult Literacy support application for Experiential learning) is a mobile language assistant that is designed to be used both in the classroom and in daily life in order to help low-literacy adults become increasingly literate and independent. Through a long-term study with adult learners we show that such a solution complements literacy programs by increasing users' motivation and interest in learning, and raising their confidence levels both in their education pursuits and in facing the challenges of their daily lives.

With the increased cost, time and potential risk or hassle involved in traveling, videoconferencing has become a popular alternative for meeting people from geographically distributed locations. Videoconferencing tools have also become widely available, and videoconferencing technologies have improved substantially over time. This paper provides a basic technical implementation guide for those involved in setting up videoconferencing in an organization. The technical infrastructure, the interaction between users and the technology, group dynamics, and the organization of the content of the videoconference, are the four key factors towards a participatory videoconferencing session. This paper examines one of the four variables, the technical infrastructure, which is a necessary condition for a successful session. This paper aims to provide a practical guide for those who are given the task to obtain, set up or improve a multi-site videoconference system. It outlines a list of required technical components and potential issues that need to be addressed when setting up a multi-site videoconference. This paper starts with a checklist of requirements, followed by an introduction of different types of videoconferencing systems, the basic technical components, and some related issues in selecting and implementing a multi-site videoconferencing system.

For decades after its introduction, videoconferencing remained a marginal communications medium, used primarily by corporate businesses. However videoconferencing has been taken up by a wide range of individuals, groups and communities. Videoconferencing occurs when people at geographically dispersed sites communicate with each other by transmitting audio and visual data through videoconferencing systems. Group videoconferencing - or multi-site videoconferencing - refers to linking individuals or groups of people in three or more sites using videoconference systems. This unique method of communicating face-to-face without being there in-person is currently being used for education and learning, health and medicine, meetings and conferences, personal communication and community-building. Group videoconferencing does not necessarily lead to participation and knowledge retention; for this to occur it must be used thoughtfully and strategically. Based on the work of researchers and practitioners in the field and an analysis of participatory videoconferencing literature, this paper suggests potential good practices for increasing participation during group videoconferences.