Encryption is a fundamental security measure to safeguard data during transmission to ensure confidentiality while at the same time posing a great challenge for traditional packet and traffic inspection. In response to the proliferation of diverse network traffic patterns from Internet-of-Things devices, websites, and mobile applications, understanding and classifying encrypted traffic are crucial for network administrators, cybersecurity professionals, and policy enforcement entities. This paper presents a comprehensive survey of recent advancements in machine-learning-driven encrypted traffic analysis and classification. The primary goals of our survey are two-fold: First, we present the overall procedure and provide a detailed explanation of utilizing machine learning in analyzing and classifying encrypted network traffic. Second, we review state-of-the-art techniques and methodologies in traffic analysis. Our aim is to provide insights into current practices and future directions in encrypted traffic analysis and classification, especially machine-learning-based analysis.

Recent research has shown that in-network observers of WiFi communication (i.e., observers who have joined the WiFi network) can obtain much information regarding the types, user identities, and activities of Internet-of-Things (IoT) devices in the network. What has not been explored is the question of how much information can be inferred by an out-of-network observer who does not have access to the WiFi network. This attack scenario is more realistic and much harder to defend against, thus imposes a real threat to user privacy. In this paper, we investigate privacy leakage derived from an out-of-network traffic eavesdropper on the encrypted WiFi traffic of popular IoT devices. We instrumented a testbed of 12 popular IoT devices and evaluated multiple machine learning methods for fingerprinting and inferring what IoT devices exist in a WiFi network. By only exploiting the WiFi frame header information, we have achieved 95% accuracy in identifying the devices and often their working status. This study demonstrates that information leakage and privacy attack is a real threat for WiFi networks and IoT applications.

Educational Data Mining is the process of extracting information from datasets with educational relevance. This process of information extraction can be extremely insightful and useful for policy decision making but also small scale interventions. In our research, we optimize model configurations and hyperparameters of student performance prediction pipelines. Our models target to predict student performance based on data gathered in Portuguese schools pertaining to the subjects of Mathematics and Portuguese. The target variable is either cast in two or five bins and we train separate models for each of the tasks. We search model configurations amongst three different feature selection algorithms and 7 different classifiers implemented in scikit-learn. Our search spans 2000 iterations overall different experiment setup permutations. We successfully develop novel model configurations that perform exceptionally well. Furthermore, we set an important precedent showing the utility of hyperparameter selection and model search for Educational Data Mining.