SACM - United States of America
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9668
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Item Restricted ELEMENT AND EVENT-BASED TEST SUITE REDUCTION FOR ANDROID TEST SUITES GENERATED BY REINFORCEMENT LEARNING(University of North Texas, 2024-06-12) Alenzi, Abdullah Sawdi M; Bryce, ReneeAndroid stands as one of the most popular operating systems on a global scale. Given the popularity and the tremendous use of Android apps and the necessity of developing robust and reliable apps, it is crucial to create efficient and effective testing tools while addressing real-world time and budget constraints. Recently, automated test generation with Reinforcement Learning algorithms have shown promise, but there is room for improvement as these algorithms often produce test suites with redundant coverage. Fine tuning parameters of RL algorithms may assist, however, this comes with trade-offs and requires time consuming and careful consideration of the characteristics of the application under test and its environment. Therefore, devising cost-effective tools and techniques is imperative to mitigate this redundancy. Instead of exploring parameters of RL algorithms, we looked at minimizing test suites that have already been generated based on SARSA algorithms. In this dissertation, we hypothesize that there is room for improvement by introducing novel hybrid approaches that combine SARSA-generated test suites with greedy reduction algorithms following the principle of HGS approach. In addition, we apply an empirical study on Android test suites that reveals the value of these new hybrid methods. Our novel approaches focus on post-processing test suites by applying greedy reduction algorithms. To reduce Android test suites, we utilize different coverage criteria including Event-Based Criterion (EBC), Element-Based Criterion (ELBC), and Combinatorial-Based Sequences Criteria (CBSC) that follow the principle of combinatorial testing to generate sequences of events and elements. The proposed criteria effectively decreased the test suites generated by SARSA and revealed a high performance in maintaining code coverage. These findings suggest that test suite reduction using these criteria is particularly well suited for SARSA-generated test suites of Android apps.52 0Item Restricted Sample Efficient Reinforcement Learning with Partial Dynamics Knowledge(Massachusetts Institute of Technology, 2024-05-17) Alharbi, Meshal; Roozbehani, MardavijThe problem of sample complexity of online reinforcement learning is often studied in the literature without taking into account any partial knowledge about the system dynamics that could potentially accelerate the learning process. In this thesis, we study the sample complexity of online Q-learning methods when some prior knowledge about the dynamics is available or can be learned efficiently. We focus on systems that evolve according to an additive disturbance model where the underlying dynamics are described by a deterministic function of states and actions, along with an unknown additive disturbance that is independent of states and actions. In the setting of finite Markov decision processes, we present an optimistic Q-learning algorithm that achieves Õ(√T) regret without polynomial dependency on the number of states and actions under perfect knowledge of the dynamics function. This is in contrast to the typical Õ(√SAT) regret for existing Q-learning methods. Further, if only a noisy estimate of the dynamics function is available, our method can learn an approximately optimal policy in a number of samples that is independent of the cardinalities of state and action spaces. The sub-optimality gap depends on the approximation error of the noisy estimate, as well as the Lipschitz constant of the corresponding optimal value function. Our approach does not require modeling of the transition probabilities and enjoys the same memory complexity as model-free methods.39 0Item Restricted Artificial Intelligence Applied To Cybersecurity And Health Care(NDSU, 2023-09-21) Alenezi, Rafa; Ludwig, SimoneNowadays, artificial intelligence is being considered a potential solution for various problems, including classification and regression optimization, in different fields such as science, technology, and humanities. It can also be applied in areas such as cybersecurity and healthcare. With the increasing complexity and impact of cybersecurity threats, it is essential to develop mechanisms for detecting new types of attacks. Hackers often target the Domain Name Server (DNS) component of a network architecture, which stores information about IP addresses and associated domain names, to gain access to a server or compromise network connectivity. Machine learning techniques can be used not only for cyber threat detection but also for other applications in various fields. In this dissertation, the first research investigates the use of classification models, including Random Forest classifiers, Keras Sequential algorithms, and XGBoost classification, for detecting attacks. Additionally, Tree, Deep, and Kernel of Shapley Additive Explanations (SHAP) can be used to interpret the results of these models. The second research focuses on detecting DNS attacks using appropriate classifiers to enable quick and effective responses. In the medical field, there is a growing trend of using algorithms to identify diseases, particularly in medical imaging. Deep learning models have been developed to detect pneumonia, but their accuracy is not always optimal and they require large data sets for training. Two studies were conducted to develop more accurate detection models for pneumonia in chest X-ray images. The third study developed a model based on Reinforcement Learning (RL) with Convolutional Neural Network (CNN) and showed improved accuracy values. The fourth study used Teaching Learning Based Optimization (TLBO) with Convolutional Neural Network (CNN) to improve pneumonia detection accuracy, which resulted in high-level accuracy rates. Overall, all these studies provide insights into the potential of artificial intelligence in improving disease detection and cyber treat detection.37 0