Are individuals capable of recognizing misleading advice? Does the AI assistance delivery mechanism impact user reliance and decision-making? We conduct a large-scale, randomized human subject study to gauge how different AI assistance delivery mechanisms affect decision-making processes. We find that subjects who received assistance only on request demonstrated a more critical engagement with the advice and were less likely to be misled compared to subjects who received unsolicited assistance. This finding has large downstream consequences as unsolicited auto-complete is the normal user experience for many AI applications. We provide actionable insights for designing AI tools, emphasizing the need for mechanisms that support user autonomy in order to increase cognitive engagement. We make our experimental platform open-source to support future research on AI-human interaction and decision-making.

ABSTRACT Narrowband Internet of Things (NB‐IoT) is LPWAN operating using narrowband spectrum in IoTs, requiring low data rates and long battery life. Since NB‐IoT does not support handover, the requirement to sustain network connectivity during mobility may result in fake base station connections, hence applications are limited to stationary use‐cases. Researchers propose extending NB‐IoT in mobile applications, owing to higher signal quality and battery life, despite DoS attacks due to low bandwidth. As NB‐IoT is resource‐constrained, resources must be allocated based on application with data processing offloaded to optimise performance. Cloud servers, being centralised and memory‐intensive, may result in increased computational delay, lower throughput, and DoS attacks in NB‐IoT. Hence, in this paper, we implement fog computing, a decentralised technology, providing scalability, reduced bandwidth, and enhanced privacy, to provide distributed processing. Additionally, we develop secure handover protocols for private and service‐provider‐controlled fog networks under normal and cell‐splitting conditions to minimise fake base station attacks and provide seamless handover. We introduce reputation‐based mechanisms to determine device integrity and differentiate faulty behaviour and attacks. Further, we implement real‐time application‐aware resource allocation and QoS‐based load‐balancing using deep learning to distribute data processing between devices, fog and cloud servers. We simulate and prototype protocols on iFogSim2 and Raspberry Pi 4. Security of the fog computing framework is validated against various attacks and formally verified using Scyther. Evaluation shows that our approach consumes 12% and 43.75% lower power and communication overhead and approximately 6 and 16 times lower execution time and memory compared with existing solutions, thus making our approach lightweight.

In this study, we compare two leading Generative AI (GAI) tools, ChatGPT and Bard, specifically in Cybersecurity, using a robust set of standardized questions from a validated Certified Ethical Hacking (CEH) dataset. In the rapidly evolving domain of Generative AI (GAI) and large language models (LLM), a comparative analysis of tools becomes essential to measure their performance. We determine the Comprehensiveness, Clarity, and Conciseness of the AI-generated responses through a detailed questioning-based framework. The study revealed an overall accuracy rate of 80.8% for ChatGPT and 82.6% for Bard, indicating comparable capabilities and specific differences. Bard slightly outperformed ChatGPT in accuracy, while ChatGPT exhibited superiority in Comprehensiveness, Clarity, and Conciseness of responses. Introducing a confirmation query like "Are you sure?" increased accuracy for both generative AI tools, illustrating the potential of iterative query processing in enhancing GAI tools' effectiveness. The readability evaluation placed both tools at a college reading level, with Bard marginally more accessible. While evaluating certain questions, a distinct pattern emerged where Bard provided generic denials of assistance while ChatGPT referenced "ethics." This discrepancy illustrates the contrasting philosophies of the developers of these tools, with Bard possibly following stricter guidelines, especially in sensitive topics like Cybersecurity. We explore the implications and identify key areas for future research that become increasingly relevant as GAI tools see broader adoption.

The Industrial Internet of Things (IIoT) enhances the benefit of the Internet of Things (IoT) to a higher level, especially in industries where human error can lead to catastrophic effects. However, security is a major concern in IIoT as hackers can gain access to connected systems, thus potentially subjecting operations to a shutdown. Besides, the outbreak of the COVID-19 pandemic changed the operations style of organizations into a remote work model. Consequently, there has been a significant increase in cyber-attacks leveraging vulnerabilities of IoT devices connected to the Internet. Considering the above factors, we propose a method of remote user authentication combining Photo Response Non-Uniformity (PRNU) with fingerprint bio-metric, which can prevent attacks. PRNU uniquely identifies the scanner, thereby authenticates the device of the user. To prove the effectiveness of PRNU, we collect fingerprint images from various scanners prototyped using Raspberry Pi and evaluate the performance. Our performance evaluation with a set of 10 fingerprint scanners shows promising results. Moreover, our analysis shows that the proposed scheme achieves a classification accuracy of 99%.

To ensure the proper functioning and performance of Industrial grade Internet of Things devices (IIoT) in Industry 4.0 networks, it is critical to identify the capabilities and malfunctions of their component devices (e.g., sensors, actuators, and controllers) and detect potential misbehavior arising due to cyber-attacks, and misconfiguration. We envision future IoT devices embed behavioral profiles throughSecurity-by-Contract(S×C) that are easy to validate and verify against network security policies; manufacturers to provide manufacturer usage description (MUD) profiles as amanifestfor the devices to signal to the network what sort of access and network functionality they require to properly function. We design authentication in the IoT onboarding process, employ blockchains to a verifiable and immutable repository to store this network manifests, that is signed and verifiable with S×C basedsmart contractsby the device manufacturer, or industry authority. The integrated framework combines blockchains and S×C security contracts, MUD-based behavioral fingerprinting, and software-defined-networking for managing the security of IIoT ecosystems. Finally, the proposed scheme is validated in a simulated IoT environment on various performance parameters.

The technologies for real-time multimedia transmission and immersive 3D gaming applications are rapidly emerging, posing challenges in terms of performance, security, authentication, data privacy, and encoding. The communication channel for these multimedia applications must be secure and reliable from network attack vectors and data-contents must employ strong encryption to preserve privacy and confidentiality. Towards delivering secure multimedia application environment for 5G networks, we propose an SDN/NFV (Software-Defined-Networking/Network-Function-Virtualization) framework called STREK , which attempts to deliver highly adaptable Quality-of-Experience (QoE), Security, and Authentication functions for multi-domain Cloud to Edge networks. The STREK architecture consists of a holistic SDNFV dataplane, NFV service-chaining and network slicing, a lightweight adaptable hybrid cipher scheme called TREK, and an open RESTful API for applications to deploy custom policies at runtime for multimedia services. For multi-domain/small-cell deployments, the key-generation scheme is dynamic at flow/session-level, and the handover authentication scheme uses a novel method to exchange security credentials with the Access Points (APs) of neighborhood cells. This scheme is designed to improve authentication function during handover with low overhead, delivering the 5G ultra-low latency requirements. We present the experiments with both software and hardware-based implementations and compare our solution with popular lightweight cryptographic solutions, standard open source software, and SDN-based research proposals for 5G multimedia. In the microbenchmarks, STREK achieves smaller hardware, low overhead, low computation, higher attack resistance, and offers better network performance for multimedia streaming applications. In real-time multimedia use-cases, STREK shows greater level of quality distortion for multimedia contents with minimal encryption bitrate overhead to deliver data confidentiality, immunity to common cryptanalysis, and significant resistance to communication channel attacks, in the context of low-latency 5G networks.

Abstract Security vulnerabilities of the modern Internet of Things (IoT) systems are unique, mainly due to the complexity and heterogeneity of the technology and data. The risks born out of these IoT systems cannot easily fit into an existing risk framework. There are many cybersecurity risk assessment approaches and frameworks that are under deployment in many governmental and commercial organizations. Extending these existing frameworks to IoT systems alone will not address the new risks that have arisen in the IoT ecosystem. This study has included a review of existing popular cyber risk assessment methodologies and their suitability to IoT systems. National Institute of Standards and Technology, Operationally Critical Threat, Asset, and Vulnerability Evaluation, Threat Assessment & Remediation Analysis, and International Standards Organization are the four main frameworks critically analyzed in this research study. IoT risks are presented and reviewed in terms of the IoT risk category and impacted industries. IoT systems in financial technology and healthcare are dealt with in detail, given their high-risk exposure. Risk vectors for IoT and the Internet of Medical Things (IoMT) are discussed in this study. A unique risk ranking method to rank and quantify IoT risk is introduced in this study. This ranking method initiates a risk assessment approach exclusively for IoT systems by quantifying IoT risk vectors, leading to effective risk mitigation strategies and techniques. A unique computational approach to calculate the cyber risk for IoT systems with IoT-specific impact factors has been designed and explained in the context of IoMT systems.

Summary Currently, core networking architectures are facing disruptive developments, due to emergence of paradigms such as Software‐Defined‐Networking (SDN) for control, Network Function Virtualization (NFV) for services, and so on. These are the key enabling technologies for future applications in 5G and locality‐based Internet of things (IoT)/wireless sensor network services. The proliferation of IoT devices at the Edge networks is driving the growth of all‐connected world of Internet traffic. In the Cloud‐to‐Things continuum, processing of information and data at the Edge mandates development of security best practices to arise within a fog computing environment. Service providers are transforming their business using NFV‐based services and SDN‐enabled networks. The SDN paradigm offers an easily programmable model, global view, and control for modern networks, which demand faster response to security incidents and dynamically enforce countermeasures to intrusions and cyberattacks. This article proposes an autonomic multilayer security framework called Distributed Threat Analytics and Response System (DTARS) for a converged architecture of Fog/Edge computing and SDN infrastructures, for emerging applications in IoT and 5G networks. The major detection scheme is deployed within the data plane, consisting of a coarse‐grained behavioral, anti‐spoofing, flow monitoring and fine‐grained traffic multi‐feature entropy‐based algorithms. We developed exemplary defense applications under DTARS framework, on a malware testbed imitating the real‐life DDoS/botnets such as Mirai. The experiments and analysis show that DTARS is capable of detecting attacks in real‐time with accuracy more than 95% under attack intensities up to 50 000 packets/s. The benign traffic forwarding rate remains unaffected with DTARS, while it drops down to 65% with traditional NIDS for advanced DDoS attacks. Further, DTARS achieves this performance without incurring additional latency due to data plane overhead.

Peukert's constant is one of the key factors used to characterize the capacity of a battery as a function of discharge time. In order to enhance the conceptual understanding of Peukert's law and Peukert's constant amongst students, this paper describes the theory and development of simulations and interactive animations that allow students to virtually experiment with NiMH battery and measure its capacity as a function of discharge rate. One of the key contributions of this work is the characterization of battery capacity with its ambient temperature in addition to the discharge rate. The online experiments allow learning to happen without the need to set up hardware, do calibrations etc. as in the case of a physical experiment. An instrument to gauge the scientific understanding of concepts related to Peukert's equation was developed as part of this work. Study on educational outcomes from exposure of students to this virtual experiment showed three times increase in assessment scores.

Internet of Things (IoTs) offers a plethora of opportunities for remote monitoring and communication of everyday objects known as things with applications in numerous domains. The advent of blockchains can be a significant enabler for IoTs towards conducting and verifying transactions in a secure manner. However, applying blockchains to IoTs is challenging due to the resource constrained nature of the embedded devices coupled with significant delay incurred in processing and verifying transactions in the blockchain. Thus there exists a need for profiling the energy consumption of blockchains for securing IoTs and analyzing energy-performance trade-offs. Towards this goal, we profile the impact of workloads based on Smart Contracts and further quantify the power consumed by different operations performed by the devices on the Ethereum platform. In contrast to existing approaches that are focused on performance, we characterize performance and energy consumption for real workloads and analyse energy-performance trade-offs. Our proposed methodology is generic in that it can be applied to other platforms. The insights obtained from the study can be used to develop secure protocols for IoTs using blockchains.

Computer science (CS) and its enabling technologies are at the heart of this information age, yet its adoption as a core subject by senior secondary students in Indian schools is low and has not reached critical mass. Though there have been efforts to create core curriculum standards for subjects like Physics, Chemistry, Biology, and Math, CS seems to have been kept outside the purview of such efforts leading to its marginalization. As a first step, using the Darmstadt model from the ITiCSE working group that provides a systematic categorization approach to CS education in schools, we coded and analyzed the CS situation for the Indian schools. Next, we focused on the motivation category of the Darmstadt model and investigated behavioral intentions of secondary school students and teachers from 332 schools in India. Considering the CS subject as an educational innovation, using Rogers’ Theory of Diffusion of Innovations, we propose a pedagogical framework for innovation attributes that can significantly predict-adoption of the CS subject among potential-adopter students and teachers. Data was analyzed to answer research questions about student and teacher intentions, influence of gender, school management, and school location in adopting CS. Interestingly, girls, urban students, teachers, and private schools were seen favoring the adoption of CS. An important issue that needed to be addressed, however, was the interchangeable use of terms like CS, Informatics, ICT, and digital literacy. Through our article, we offer a promising picture of the educational policy directives and the academic environment in India that is rapidly growing and embracing CS as a core subject of study in schools. We also analyze the factors that influence the adoption of CS by school students and teachers and conclude that there is a very positive response for CS among educators and students in India.

Most of the critical challenges seen in the past decades have impacted citizens in a global way. Given shrinking resources, educationists find preparing students for the global market place a formidable challenge. Hence exposing students to multi-lateral educational initiatives are critical to their growth, understanding and future contributions. This paper focuses on European Union's Erasmus Mundus programs, involving academic cooperation amongst international universities in engineering programs. A phased undergraduate engineering program with multiple specializations is analyzed within this context. Based on their performance at the end of first phase, selected students were provided opportunities using scholarship to pursue completion of their degree requirements at various European universities. This paper will elaborate the impact of differing pedagogical interventions, language and cultural differences amongst these countries on students in diverse engineering disciplines. The data presented is based on on the feedback analysis from Eramus Mundus students (N=121) that underwent the mobility programs. The findings have given important insights into the structure of the initiative and implications for academia and education policy makers for internationalizing engineering education. These included considering digital interventions such as MOOCs (Massive Open Online Courses) and Virtual Laboratory (VL) initiatives for systemic reorganization of engineering education.

A major challenge in engineering education today in India is preparing students with the skill set needed for a global industry. This paper describes an innovative partnership between two institutions of higher education in India and USA and a multinational corporation to address this challenge. The collaborative manner in which the industry and institutions internationalized the curriculum is a key feature of this program. This unique approach has resulted in the employees of a multinational company in India being able to get two high quality Masters-level degrees in engineering, customized to their current and future requirements at a very affordable price point. This paper looks at cost savings, collaborative course development between industry and international academia, using technology to deliver courses and on-the-job professional development program for employees while keeping the employees motivated. But there are also interesting lessons learnt regarding teaching in English, adjusting to the local cultural context and the overall integration into the Indian academic setting. The process innovation described here would be of great interest to academia, industry and education policy makers and has the potential to be replicated in the entire field of non-engineering and interdisciplinary academic programs.

By the advent of the new millennia there have been notable changes in the way students get their education. Remote triggered labs standout among these education methodologies for their user interaction and active involvement. Remote Triggered Virtual Labs experiments enhance both theoretical and practical learning experience using real time data streaming and analysis. Real time data streaming includes plotting laboratory experiment data values to the user, showing trigger and control buttons and signals and live video streaming of the respective experiment. This paper describes the framework and methodology for the creation of a Remote Triggered Virtual Laboratory experiment for teaching Hooke's Law, a key engineering principle in the Mechanics of Solids. It describes how the experiment works from a technical perspective, how the user interacts with and conducts the experiment, and the novel strategies and methodologies used.