Despite the widespread availability and increasing use of cyberlearning environments, there remains a need for more research about their usefulness in undergraduate education, particularly in STEM education. The process of evaluating the usefulness of a cyberlearning environment is an essential measure of its success and is useful in assisting the design process and ensuring user satisfaction. Unfortunately, there are relatively few empirical studies that provide a comprehensive test of the usefulness of cyberlearning in education. Additionally, there is a lack of standards upon whose usefulness evaluators agree. In this research, we present multiple user studies that can be used to assess the usefulness of a cyberlearning environment used in Computer Science and Software Engineering courses through testing its usability and measuring its utility using user interface and user experience evaluations. Based on these assessments, we propose an evaluation framework to evaluate cyberlearning environments. To help illustrate the framework utility and usability evaluations, we explain them through an example SEP-CyLE (Software Engineering and Programming Cyberlearning Environment). The evaluation techniques used are cognitive walkthroughs with a think-aloud protocol and a heuristic evaluation survey. We further use a network-based analysis to find the statistically significant correlated responses in the heuristic evaluation survey with regard to the students' perceptions of using SEP-CyLE. Our goal is to improve cyberlearning practice and to emphasize the need for evaluating cyberlearning environments with respect to its designated tasks and its users using UI/UX evaluations. Our experiments demonstrated participants were able to utilize SEP-CyLE efficiently to accomplish the tasks we posed to them and to enhance their software development concepts, specifically, software testing. We discovered areas of improvement in the visibility and navigation of SEP-CyLE's current design. We provide our recommendations for improving SEP-CyLE and provide guidance and possible directions for future research on designing cyberlearning environments for computer education.

An efficient lightweight forward static slicing tool is presented. The tool is implemented on top of srcML, an XML representation of source code. The approach does not compute the full program dependence graph but instead dependency information is computed as needed while computing the slice on a variable. The result is a list of line numbers, dependent variables, aliases, and function calls that are part of the slice for a given variable. The tool produces the slice for all variables in a system. The approach is highly scalable and can generate the slices for all variables of the Linux kernel in less than 15 minutes. A demonstration video is at: https://youtu.be/McvFUVSGg-g

The design of existing machine-learning-based DoS detection systems in software-defined networking (SDN) suffers from two major problems. First, the proper time window for conducting network traffic analysis is unknown and has proven challenging to determine. Second, it is unable to detect unknown types of DoS saturation attacks. An unknown saturation attack is an attack that is not represented in the training data. In this paper, we evaluate three supervised classifiers for detecting a family of DDoS flooding attacks (UDP, TCP-SYN, IP-Spoofing, TCP-SARFU, and ICMP) and their combinations using different time windows. This work represents an extension of the runner-up best-paper award entitled ‘Detecting Saturation Attacks in SDN via Machine Learning’ published in the 2019 4th International Conference on Computing, Communications and Security (ICCCS). The results in this paper show that the trained supervised models fail in detecting unknown saturation attacks, and their overall detection performance decreases when the time window of the network traffic increases. Moreover, we investigate the performance of four semi-supervised classifiers in detecting unknown flooding attacks. The results indicate that semi-supervised classifiers outperform the supervised classifiers in the detection of unknown flooding attacks. Furthermore, to further increase the possibility of detecting the known and unknown flooding attacks, we propose an enhanced hybrid approach that combines two supervised and semi-supervised classifiers. The results demonstrate that the hybrid approach has outperformed individually supervised or semi-supervised classifiers in detecting the known and unknown flooding DoS attacks in SDN.

Spatial data clustering has long been used to facilitate the knowledge discovery process. Several approaches have been proposed in the literature for detecting and understanding hidden patterns. These approaches are based on different perspectives and can be roughly categorized into several main categories, including centroid-based, density-based, grid-based, and hierarchy-based clustering. In spite of being a very mature research area, existing spatial clustering techniques usually depend on user parameters and continue to utilize distances between objects as their similarity measure. In turn, clustering approaches are generally suffering from performance and scalability issues. To address this problem, we propose ACUTE, an efficient and scalable approach that detects both synthetic and real-world spatial clusters. ACUTE ascertains both the intra-cluster compactness (similarities) and inter-cluster connectedness (dissimilarities) of spatial objects by assessing the topological relations of their corresponding spatial points. While conventional methods conduct clustering according to pairwise comparisons of the distances between objects, our approach focuses on leveraging topological relations that reduce the distances required to be calculated. This in turn minimizes the number of comparisons required, thus enhancing the efficiency and increasing the scalability. To evaluate the accuracy of ACUTE, it has been extensively tested against twelve (12) synthetic datasets and five (5) actual datasets including one location-based (network) dataset. Results show that ACUTE has great performance when compared with state-of-the-art clustering techniques in terms of several evaluation metrics, including precision, recall, and error rates.