Two primary issues have emerged in the machine learning and data mining community: how to deal with imbalanced data and how to choose appropriate features. These are of particular concern in the software engineering domain, and more specifically the field of software defect prediction. This research highlights a procedure which includes a feature selection technique to single out relevant attributes, and an ensemble technique to handle the class-imbalance issue. In order to determine the advantages of feature selection and ensemble methods we look at two potential scenarios: (1) Ensemble models constructed from the original datasets, without feature selection; (2) Ensemble models constructed from the reduced datasets after feature selection has been applied. Four feature selection techniques are employed: Principal Component Analysis (PCA), Pearson’s correlation, Greedy Stepwise Forward selection, and Information Gain (IG). The aim of this research is to assess the effectiveness of feature selection techniques using ensemble techniques. Five datasets, obtained from the PROMISE software depository, are analyzed; tentative results indicate that ensemble methods can improve the model's performance without the use of feature selection techniques. PCA feature selection and bagging based on K-NN perform better than both bagging based on SVM and boosting based on K-NN and SVM, and feature selection techniques including Pearson’s correlation, Greedy stepwise, and IG weaken the ensemble models’ performance.

Ensuring the quality of software products is important for them to be successful. Discovering errors and fixing defective software modules early in the project lifecycle (e.g. in the testing phase) can save resources and enhance software quality. Dev

Phishing attacks have witnessed a rapid increase thanks to the matured social engineering techniques, COVID-19 pandemic, and recently adversarial deep learning techniques. Even though adversarial phishing attacks are recent, attackers are crafting such attacks by considering context, testing different attack paths, then selecting paths that can evade machine learning phishing detectors. This research proposes an approach that generates adversarial phishing attacks by finding optimal subsets of features that lead to higher evasion rate. We used feature engineering techniques such as Recursive Feature Elimination, Lasso, and Cancel Out to generate then test attack vectors that have higher potential to evade phishing detectors. We tested the evasion performance of each technique then classified different evasion tests as passed or failed depending on their evasion rate. Our findings showed that our threat model has better evasion capability compared to the original Generative Adversarial Deep Neural Network (GAN) which perturbs features in a random manner.

Due to the growing interconnection and sensitive nature of IoT applications, this article provides a comprehensive overview of cybersecurity in 5G-enabled Internet of Things (IoT) networks. The review highlights the significant potential of 5G technology while also emphasizing the need for ongoing work to mitigate its weaknesses and fully harness its advantages. In this context, prospective research avenues are crucial to enhance the resilience and security of 5G-enabled systems. The future directions outlined in this document are diverse and seek to tackle significant deficiencies in 5G-IoT cybersecurity research. A primary objective is to align theoretical analyses with practical implementations by examining specific assaults in IoT contexts, thus improving the comprehension of real-world vulnerabilities. Furthermore, as the 5G standard progresses through its implementation, continuous research must remain synchronized with improvements, focusing on components and features that are currently in development. Formulating effective protection and mitigation strategies to address sophisticated threats is essential for safeguarding this integrated environment. Furthermore, enhancing standards and regulatory frameworks is essential to ensure uniform security procedures across various businesses and areas, especially considering the global nature of 5G-IoT applications. Ultimately, investigating sophisticated attack scenarios that leverage the interaction between IoT devices and 5G infrastructure would enhance understanding of vulnerabilities and aid in the development of more resilient security measures. These directives underscore the necessity for ongoing, flexible research to guarantee the robustness and safety of 5G-enabled IoT networks.