In modern automobiles, the infotainment system is crucial for enhancing driver and passenger capabilities, offering advanced features such as music, navigation, communication, and entertainment. Leveraging Wi-Fi, cellular networks, NFC, and Bluetooth, the system ensures continuous internet connectivity, providing seamless access to information. However, the increasing complexity of IT connectivity in vehicles raises significant cybersecurity concerns, including potential data breaches and exposure of sensitive information. To enhance security in infotainment systems, this study applied component-level threat modeling to a proposed infotainment system using the Microsoft STRIDE model. This approach illustrates potential component-level security issues impacting privacy and security concerns. The study also assessed these impacts using SAHARA and DREAD risk assessment methodologies. The threat modeling process identified 34 potential security threats, each accompanied by detailed information. Moreover, a comparative analysis is performed to compute risk values for prioritizing treatment, followed by recommending mitigation strategies for each identified threat. These identified threats and associated risks require careful consideration to prevent potential cyberattacks before deploying the infotainment system in automotive vehicles.

Cloud computing refers to a sophisticated technology that deals with the manipulation of data in internetbased servers dynamically and efficiently. The utilization of the cloud computing has been rapidly increased because of its scalability, accessibility, and incredible flexibility. Dynamic usage and process sharing facilities require task scheduling which is a prominent issue and plays a significant role in developing an optimal cloud computing environment. Round robin is generally an efficient task scheduling algorithm that has a powerful impact on the performance of the cloud computing environment. This paper introduces a new approach for round robin based task scheduling algorithm which is suitable for cloud computing environment. The proposed algorithm determines time quantum dynamically based on the differences among three maximum burst time of tasks in the ready queue for each round. The concerning part of the proposed method is utilizing additive manner among the differences, and the burst times of the processes during determining the time quantum. The experimental results showed that the proposed approach has enhanced the performance of the round robin task scheduling algorithm in reducing average turnaround time, diminishing average waiting time, and minimizing number of contexts switching. Moreover, a comparative study has been conducted which showed that the proposed approach outperforms some of the similar existing round robin approaches. Finally, it can be concluded based on the experiment and comparative study that the proposed dynamic round robin scheduling algorithm is comparatively better, acceptable and optimal for cloud environment.

Plagiarism is a major problem in education, especially in higher education environments. To address this problem, a comprehensive detection method is proposed, utilizing cutting-edge models like Bidirectional Encoder Representations from Transformers (BERT) and cosine similarity for detecting direct copy and semantic changes. The methodology involves evaluating the BERT model and cosine similarity first, then performing pairwise comparisons to determine how similar a student’s work is to a reference text. If the semantic changes are higher, the content is classified as paraphrased; otherwise, it is considered direct copy in cases where the similarity threshold is greater than 30%. When the similarity threshold is less than or equal to 30%, the method moves to online plagiarism detection. This includes web scraping, generating search queries, and employing Term Frequency-Inverse Document Frequency (TF-IDF) vectorization with cosine similarity to assess the similarities between students’ content and online content. As a result, the study detects online plagiarism and distinguishes between plagiarizing and authentic content. The purpose is to provide an effective method to maintain academic integrity and enhance originality in student work. In similarity testing on MIT plagiarism detection datasets, this study demonstrated an average recall of 80%, precision of 68%, accuracy of 71%, and f1-score of 74%. With a threshold of 0.2, the average precision for online plagiarism detection reached 65%, accompanied by a 56% recall, an accuracy of 60%, and an f1-score of 60%. The results of the study demonstrate the efficiency of the strategy in enhancing plagiarism detection accuracy while reducing false positives.