With the increasing sophistication of Advanced Persistent Threats (APTs), the demand for effective detection and mitigation strategies and methods has escalated. Program execution leaves traces in the system audit log, which can be analyzed to detect malicious activities. However, collecting and analyzing large volumes of audit logs over extended periods is challenging, further compounded by insufficient labeling that hinders their usability. Addressing these challenges, this paper introduces SAGA (Synthetic Audit log Generation for APT campaigns), a novel approach for generating find-grained labeled synthetic audit logs that mimic real-world system logs while embedding stealthy APT attacks. SAGA generates configurable audit logs for arbitrary duration, blending benign logs from normal operations with malicious logs based on the definitions the MITRE ATT&CK framework. Malicious audit logs follow an APT lifecycle, incorporating various attack techniques at each stage. These synthetic logs can serve as benchmark datasets for training machine learning models and assessing diverse APT detection methods. To demonstrate the usefulness of synthetic audit logs, we ran established baselines of event-based technique hunting and APT campaign detection using various synthetic audit logs. In addition, we show that a deep learning model trained on synthetic audit logs can detect previously unseen techniques within audit logs.

A robust observer-based fuzzy incremental sliding mode control approach is proposed in this paper for a class of uncertain underactuated systems. The observer is utilized to estimate the system states that are unavailable for measurement. The first-level sliding surface is defined at first, and then one of the left states is employed to construct the next sliding surfaces until the last sliding surface is constructed. The fuzzy logic system is applied to approximate the unknown nonlinear functions. Moreover, some adaptive laws are introduced to estimate the upper bounds of unknown disturbances. The stability of the underactuated system is analyzed based on Lyapunov stability theorem. The proposed controller ensures the robust stability of the underactuated system. Finally, simulation example is illustrated to demonstrate the effectiveness of this proposed control approach.

Designing a high-quality assessment system is a challenge for college-level educators. An effective assessment system requires mastering the professional knowledge and skills involved. The field of assessment and evaluation has developed many important concepts, principles, and methods to guide practice. This study proposes an intelligent assessment management system for the summative assessment of college courses. It integrates a virtual online assessment management system and in-classroom examinations. Before an exam, the scope of the examination is selected by teachers, and students who will take the exam are listed. According to the number of students, the system configures the range of examination rooms. Students can go to any of the examination rooms to take the exam during the exam period. Each table has a near-field communication (NFC)-enabled tablet for student identification via a student APP. The identification process uses digital certificates to ensure uniqueness and to avoid substitute testers. Invigilators use an exam management APP to access information about the students taking the exam in the exam room, and give feedback regarding the real-time situation to the system. Instructors use the assessment management APP to check students' situation after the exam. The system ensures that all students receive fair treatment during the exam. The proposed system improves the effectiveness of assessment.

The booming of information technology has influenced the evolution of learning patterns and the integration of information technology into teaching has became one of the mainstream methods. The teaching and delivery of knowledge are no longer restricted to traditional lectures. As Learning Management System (LMS) at the present stage can accommodate a diversity of learning requirements and functions, there are many literatures to study on the application of the LMS. However, as the development of relevant industries and technologies, users’ requirements on the LMS may be influenced and changed. How will these changes affect the development of the LMS? What are the changes and developments of the LMS required for addressing the current and future requirements of the users? To address these questions this study aims to perform a comprehensive investigation on the technologies, applications, and analysis of the LMS to realize the developments of the LMS.

In this paper, we present a simple but efficient algorithm for integrating a collection of heterogeneous itembanks, called heterogeneous itembanks integrator (HIBI). This algorithm is motivated by the desire to integrate itembanks provided by publishers to the users of an e-learning system, which generally use different content structures. The proposed algorithm starts off with one of the itembanks as a reference itembank. All the items on the other itembanks are integrated into the reference itembank to create the so-called meta-itembank. Moreover, by treating the metaitembank thus created as the reference itembank, it can be easily extended by using exactly the same algorithm. We also use the concept of ontology to share itemsets with other systems. The experimental results showed that the proposed algorithm can provide an extremely high quality result in terms of both the relevance of items and the computation time in Chinese itemsets.