The significant increase in technology development over the internet makes network security a crucial issue. An intrusion detection system (IDS) shall be introduced to protect the networks from various attacks. Even with the increased amount of works in the IDS research, there is a lack of studies that analyze the available IDS datasets. Therefore, this study presents a comprehensive analysis of the relevance of the features in the KDD99 and UNSW-NB15 datasets. Three methods were employed: a rough-set theory (RST), a back-propagation neural network (BPNN), and a discrete variant of the cuttlefish algorithm (D-CFA). First, the dependency ratio between the features and the classes was calculated, using the RST. Second, each feature in the datasets became an input for the BPNN, to measure their ability for a classification task concerning each class. Third, a feature-selection process was carried out over multiple runs, to indicate the frequency of the selection of each feature. From the result, it indicated that some features in the KDD99 dataset could be used to achieve a classification accuracy above 84%. Moreover, a few features in both datasets were found to give a high contribution to increasing the classification’s performance. These features were present in a combination of features that resulted in high accuracy; the features were also frequently selected during the feature selection process. The findings of this study are anticipated to help the cybersecurity academics in creating a lightweight and accurate IDS model with a smaller number of features for the developing technologies.

Cuttlefish algorithm (CFA) is a metaheuristic bio-inspired algorithm that mimics the color-changing behavior by the cuttlefish. It is produced by light reflected from different layers of cells and involves two processes, i.e., reflection and visibility. The reflection process simulates the light reflection mechanism, while the visibility process simulates the visible appearance of the matching pattern used by the cuttlefish. There is no cooperation strategy between the solutions of the CFA’s sub-populations. The strategy can improve the capabilities of local exploitation and global exploration in terms of solution diversity and quality during the search process. This paper introduces two schemes to improve the performance of the cuttlefish algorithm in continuous optimization problems. Firstly, a migration strategy is employed between the multi-population cuttlefish to increase solutions diversity during the search process. Secondly, one of the exploitation strategies of the standard cuttlefish is replaced with a new exploitation strategy based on short-term memory. The test demonstrates that the proposed algorithm outperforms the standard cuttlefish algorithm. Besides, the performance of the proposed algorithm was investigated using the CEC2013 benchmarking test functions. Comparisons with several state-of-the-art algorithms were performed, and the outcomes indicated that the proposed method offers a competitive performance advantage over the alternatives.

This article introduces a comprehensive dataset for investigating the ex-ante determinants of Initial Public Offerings (IPOs) first-day initial returns, investor demand, and the information gap experienced by investors for listed firms on Bursa Malaysia over the period 2004–2021. The final sample comprises 350 IPOs priced using the fixed-price method, which were hand-collected from multiple sources. The data in this dataset plays an important role in providing insightful decision-making tips for prospective investors operating in an environment characterized by high information asymmetry. Furthermore, the dataset opens avenues for future research, such as cross-country studies exploring the impact of country-specific factors on IPO initial return outcomes.