Security and privacy of users have become significant concerns due to the involvement of the Internet of Things (IoT) devices in numerous applications. Cyber threats are growing at an explosive pace making the existing security and privacy measures inadequate. Hence, everyone on the Internet is a product for hackers. Consequently, Machine Learning (ML) algorithms are used to produce accurate outputs from large complex databases, where the generated outputs can be used to predict and detect vulnerabilities in IoT-based systems. Furthermore, Blockchain (BC) techniques are becoming popular in modern IoT applications to solve security and privacy issues. Several studies have been conducted on either ML algorithms or BC techniques. However, these studies target either security or privacy issues using ML algorithms or BC techniques, thus posing a need for a combined survey on efforts made in recent years addressing both security and privacy issues using ML algorithms and BC techniques. In this article, we provide a summary of research efforts made in the past few years, from 2008 to 2019, addressing security and privacy issues using ML algorithms and BC techniques in the IoT domain. First, we discuss and categorize various security and privacy threats reported in the past 12 years in the IoT domain. We then classify the literature on security and privacy efforts based on ML algorithms and BC techniques in the IoT domain. Finally, we identify and illuminate several challenges and future research directions using ML algorithms and BC techniques to address security and privacy issues in the IoT domain.
Results of recent studies have suggested that intensive methods of delivery might improve engagement, attendance, and achievement for students from diverse backgrounds. Contributing to this area of inquiry, this study assesses how students perceived their experience studying a certificate course that was delivered in an online intensive block mode and flipped classroom (BMFC), pedagogy amidst COVID-19 restrictions. The subjects were students enrolled at Melbourne Institute of Technology between July 2021 and January 2022 across four certificate courses, three at postgraduate and one at undergraduate level. These certificate courses differed from normal degree courses in several aspects: (a) a shorter 4-week (undergraduate) or 5-week (postgraduate), instead of a 12-week duration, (b) subjects were taken sequentially instead of concurrently as in a normal semester, (c) taught using an online flipped classroom rather than the in-class approach, and (d) open to both high-school leavers and mature aged students who did not study full-time. A questionnaire involving 10 perception-based questions was used to survey students’ satisfaction with the BMFC delivery, in relation to their learning and engagement experience. The mean, median, and mode calculated from the responses revealed that students regarded the BMFC approach as more satisfied than not on a 5-star rating scale in 7 out of the 10 questions. This is further supported by high correlations among the questions (the lowest at r = 0.48 and the highest at r = 0.87). Multiple regression analysis using the first nine questions as predictors of the 10th question (overall satisfaction) revealed that six of these are statistically significant predictors (p < 0.05) of the overall satisfaction, implying that an increase in the overall satisfaction can potentially be achieved by improving these key factors of the BMFC delivered certificate courses. Our findings correlate with existing research that student learning and engagement might be improved by intensive modes of delivery. Furthermore, the BMFC pedagogy proposed in our study differentiates us from existing research, where block scheduling was used only in a face-to-face delivery in pre COVID-19 environment. Our study, therefore, contributes a novel delivery method for learning and teaching that is suitable for both online and face-to-face mode in a post COVID-19 era.
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