The Internet of Vehicles (IoV) is envisioned to improve road safety, reduce traffic congestion, and minimize pollution. However, the connectedness of IoV entities increases the risk of cyber attacks, which can have serious consequences. Traditional intrusion detection systems (IDS) transfer large amounts of raw data to central servers, leading to potential privacy concerns. Also, training IDS on resource-constrained IoV devices generally can result in slower training times and poor service quality. To address these issues, we propose a split learning-based privacy-preserving IDS that deploys IDS on edge devices without sharing sensitive raw data. In addition, we propose a regret minimization-based adaptive offloading technique that reduces the training time on resource-constrained devices. Our approach effectively detects anomalous behavior while preserving data privacy and reducing training time, making it a practical solution for IoV. Experimental results show the effectiveness of our approach and its potential to enhance the security of the IoV network.

In this paper, we provide a sentiment analysis of conversations surrounding Covid-19 vaccine adoption on Twitter. We focus on key regions of the US, particularly urban areas with high African American populations. We utilize machine learning models such as logistic regression, Support Vector Machines, and Naive Bayes to provide baseline models. Furthermore, we develop fined-tuned Transformer-based language models that provide a classification of sentiments with high accuracy. The results from our analysis show that fine-tuning our dataset on a Transformer-based model, Covid-BERT v2, performs better than our baseline models however the accuracy is still relatively low. This might be as a result of the very limited training dataset. Future work will explore the use of a higher quality dataset and also evaluate other transformer-based models.

The Internet of Things (IoT) has become commonplace in our lives. From smart refrigerators to thermostats, the heterogeneous connectivity it presents has allowed for the automation of tasks and ease of use. However, it has also led to security challenges introducing new attack vectors that are atypical to traditional computing systems. This also complicates tracing a point of system fault in case of an anomalous event or intrusion. In this paper, we propose a visualization framework that can be used to aid the detection of anomalous system events in an IoT ecosystem. This can assist in uncovering valuable insights from data interactions. The proposed framework provides a visual representation of system events in an IoT device. This can be beneficial for use in digital forensic analysis and uncovering system fault or intrusion. Also, it can be used by system administrators or consumers for situational awareness. We discuss our implementation details using a smart home system as a use case and provide future research directions.