IoTVT Model: A Model Mapping IoT Sensors to IoT Vulnerabilities and Threats

The Internet of Things (IoT), which has accelerated the digital transformation technology revolution, has enabled cyber-physical digital transformation strategies and accelerated business automation. In a Covid-19 related Harvard Business Review study, 95 per cent of executives agreed that digital transformation strategies had become increasingly important. This highlights the critical importance of being prepared for IoT vulnerabilities and attacks. Mapping IoT devices to identify their vulnerabilities and attack allows academics and practitioners to identify, analyze, and mitigate IoT-related concerns. In this paper, we categorize IoT sensors, their IoT related vulnerabilities, and the IoT attacks that affect them and propose a model that maps the relationships among them. Our model provides valuable insights into IoT attack vectors and associated vulnerabilities with consumer IoT devices

Evaluating Machine Learning Methods for Intrusion Detection in IoT

Cyber-attacks in IoT enabled devices have grown at an alarming rate since the start of the Covid-19 pandemic due to cyber physical digital transformation enabled through widespread deployment of low cost sensor embedded IoT devices in consumer and industrial IOT, as well as increase in computing power. Consequently, this adoption trend had led to 1.51 billion breaches on IoT devices during the first half of 2021 alone. This highlights the critical importance of being prepared for IoT vulnerabilities (IoT manufacturing and deployment sector) and attacks (malicious actors). In this respect machine learning (ML) especially deep learning (DL) strategies has emerged as the preferred methods to secure IoT devices from attacks. In this paper, we propose three deep learning algorithms for IoT intrusion detection based on mapping of IoT attacks to ML/DL methods. Our paper thus provides two contributions. First, we present a model that maps extant research on the application of ML/DL to specific IoT attacks. Second, through an optimal selection of the mapping, we present three algorithms (naïve Bayes, convolu- tional neural network and autoencoder) for detection of intrusion in IoT attacks. This provides a review of research opportunities and research gaps in the IoT IDS domain

Systems Dynamics Modeling for Evaluating Socio-Technical Vulnerabilities in Advanced Persistent Threats

The paper focus on the application of Systems Dynamics Modelling (SDM) for simulating socio-technical vulnerabilities of Advanced Persistent Threats (APT) to unravel Human Computer Interaction (HCI) for strategic visibility of threat actors. SDM has been widely applied to analyze nonlinear, complex, and dynamic systems in social sciences and technology. However, its application in the cyber security domain especially APT that involve complex and dynamic human computer interaction is a promising but scant research domain. While HCI deals with the interaction between one or more humans and between one or more computers for greater usability, this same interactive process is exploited by the APT actor. In this respect, using a data breach case study, we applied the socio-technical vulnerabilities classification as a theoretical lens to model socio and technical vulnerabilities on systems dynamics using Vensim software. The variables leading to the breach were identified, entered into Vensim software, and simulated to get the results. The results demonstrated an optimal interactive mix of one or more of the six socio variables and three technical variables leading to the data breach. SDM approach thus provides insights into the dynamics of the threat as well as throw light on the strategies to undertake for minimizing APT risks. This can assist in the reduction of the attack surface and reinforce mitigation efforts (prior to exfiltration) should an APT attack occur. In this paper, we thus propose and validate the application of system dynamics approach for designing a dynamic threat assessment framework for socio-technical vulnerabilities of APT

A system dynamics approach to evaluate advanced persistent threat vectors.

Cyber-attacks targeting high-profile entities are focused, persistent, and employ common vectors with varying levels of sophistication to exploit social-technical vulnerabilities. Advanced persistent threats (APTs) deploy zero-day malware against such targets to gain entry through multiple security layers, exploiting the dynamic interplay of vulnerabilities in the target network. System dynamics (SD) offers an alternative approach to analyze non-linear, complex, and dynamic social-technical systems. This research applied SD to three high-profile APT attacks - Equifax, Carphone, and Zomato - to identify and simulate socio-technical variables leading to breaches. By modeling APTs using SD, managers can evaluate threats, predict attacks, and reduce damage by mitigating specific socio-technical cues. This study provides valuable insights into the dynamics of cyber threats, making it the first to apply SD to APTs

Security Risk Assessment of Blockchain-Based Patient Health Record Systems

Blockchain technology is receiving greater attention for enhancing the security of patient records systems; however, it is not a panacea, as many security risks have been found in these healthcare applications. This study conducts a state-of-the-art analysis of emerging risks in blockchain-based patient health record systems, their severity level, impact, and the corresponding countermeasures against them. In addition, we conclude our observations and indicate how blockchain security vulnerabilities may develop in the future. This study aims to promote more research on blockchain security challenges by offering researchers insights into future security and privacy developments in blockchain-based patient health record systems

Evaluating Onsite and Online Internship Mode Using Consumptive Metrics

The paper aims to assess the effectiveness between onsite and online internship mode by measuring the critical components of learning through the Kirkpatrick\u27s ‘consumptive metrics\u27 model. The primary goal of internship is to assist university students in their progression from the academic to a professional work environment. However, the COVID-19 pandemic has disrupted this process where it temporarily moved to online mode. Hence, the authors use Kirkpatrick\u27s ‘consumptive metrics\u27 (CM) for evaluating the learning resources consumed using two constructs namely ‘reaction\u27 and ‘learning\u27. Using 21 onsite and 20 online intern reports, researchers objectively measured the difference in alignment of theory with practice between onsite and online mode. The research revealed that while the CM components namely ‘course satisfaction\u27 and ‘training relevance\u27 on the interns are similar for both modes, there is a considerable reduction in the effectiveness of internship in terms of the CM components namely the ‘training environment\u27, ‘knowledge gained\u27, and ‘career advancement\u27 in an online mode

A systematic survey on multimodal emotion recognition using learning algorithms

Emotion recognition is the process to detect, evaluate, interpret, and respond to people's emotional states and emotions, ranging from happiness to fear to humiliation. The COVID- 19 epidemic has provided new and essential impetus for emotion recognition research. The numerous feelings and thoughts shared and posted on social networking sites throughout the COVID-19 outbreak mirrored the general public's mental health. To better comprehend the existing ecology of applied emotion recognition, this work presents an overview of different emotion acquisition tools that are readily available and provide high recognition accuracy. It also compares the most widely used emotion recognition datasets. Finally, it discusses various machine and deep learning classifiers that can be employed to acquire high level features for classification. Different data fusion methods are also explained in detail highlighting their benefits and limitations

How Can Blockchain Technology Be Used to Manage the COVID-19 Vaccine Supply Chain? A Systematic Literature Review and Future Research Directions

To ensure the success of the COVID-19 vaccination program, vaccine supply networks must become more efficient, secure, and dependable. This paper provides a systematic literature review of current academic work on blockchain-based COVID-19 supply chain management (CVSCM), addressing the role of blockchain in CVSCM and its challenges. The paper\u27s objectives are to comprehensively analyze the literature on blockchain solutions in the CVSCM and propose a future research agenda based on gaps in the present literature. The systematic literature review involved 34 peer-reviewed journal and conference publications published between 2019 and 2022. Using a thematic analysis, we observed that the public blockchain is the most often-used blockchain platform for constructing the CVSCM frameworks. The supply chain data privacy and security are major driving factors. Blockchain technology significantly affects CVSCM by allowing for distributed transaction execution and verification. Blockchain technology enables traceability, digitalization, disintermediation of the supply chain, and enhanced data privacy. However, several challenges were identified, including privacy worries, excessive energy consumption, latency, transactional throughput, and scalability. Our results provide the groundwork for future research aimed at increasing technical integration in blockchain supply chain solutions, cross-chain interoperability, and scalability, the feasibility of commercial applications in real-world industrial settings, data security, and privacy. Future research might also closely monitor emerging technologies in CVSCM, such as edge computing, virtual reality, machine learning, artificial intelligence, and blockchain advancements, and provide more impartial support to the many research potentials discussed