The rise of sophisticated cyber threats has spurred advancements in Intrusion Detection Systems (IDS), which are crucial for identifying and mitigating security breaches in real-time. Traditional IDS often rely on complex machine learning algorithms that lack transparency despite their high accuracy, creating a “black box” effect that can hinder the analysts’ understanding of their decision-making processes. Explainable Artificial Intelligence (XAI) offers a promising solution by providing interpretability and transparency, enabling security professionals to understand better, trust, and optimize IDS models. This paper presents a systematic review of the integration of XAI in IDS, focusing on enhancing transparency and interpretability in cybersecurity. Through a comprehensive analysis of recent studies, this review identifies commonly used XAI techniques, evaluates their effectiveness within IDS frameworks, and examines their benefits and limitations. Findings indicate that rule-based and tree-based XAI models are preferred for their interpretability, though trade-offs with detection accuracy remain challenging. Furthermore, the review highlights critical gaps in standardization and scalability, emphasizing the need for hybrid models and real-time explainability. The paper concludes with recommendations for future research directions, suggesting improvements in XAI techniques tailored for IDS, standardized evaluation metrics, and ethical frameworks prioritizing security and transparency. This review aims to inform researchers and practitioners about current trends and future opportunities in leveraging XAI to enhance IDS effectiveness, fostering a more transparent and resilient cybersecurity landscape.

Machine Learning (ML)-based Intrusion Detection Systems (IDS) are integral to securing modern IoT networks but often suffer from a lack of transparency, functioning as “black boxes” with opaque decision-making processes. This study enhances IDS by integrating Explainable Artificial Intelligence (XAI), improving interpretability and trustworthiness while maintaining high predictive performance. Using the UNSW-NB15 dataset, comprising over 2.5 million records and nine diverse attack types, we developed and evaluated multiple ML models, including Decision Trees, Multilayer Perceptron (MLP), XGBoost, Random Forest, CatBoost, Logistic Regression, and Gaussian Naive Bayes. By incorporating XAI techniques such as LIME, SHAP, and ELI5, we demonstrated that XAI-enhanced models provide actionable insights into feature importance and decision processes. The experimental results revealed that XGBoost and CatBoost achieved the highest accuracy of 87%, with a false positive rate of 0.07 and a false negative rate of 0.12. These models stood out for their superior performance and interpretability, highlighting key features such as Source-to-Destination Time-to-Live (sttl) and Destination Service Count (ct_srv_dst) as critical indicators of malicious activity. The study also underscores the methodological and empirical contributions of integrating XAI techniques with ML models, offering a balanced approach between accuracy and transparency. From a practical standpoint, this research equips human analysts with tools to better understand and trust IDS predictions, facilitating quicker responses to security threats. Compared to existing studies, this work bridges the gap between high-performing ML models and their real-world applicability by focusing on explainability. Future research directions include applying the proposed methodology to more complex datasets and exploring advancements in XAI techniques for broader cybersecurity challenges.

Background: Although computational models are advancing air quality prediction, achieving the desired performance or accuracy of prediction remains a gap, which impacts the implementation of machine learning (ML) air quality prediction models. Several models have been employed and some hybridized to enhance air quality and air quality index predictions. The objective of this paper is to systematically review machine and deep learning techniques for spatiotemporal air prediction challenges. Methods: In this review, a methodological framework based on PRISMA flow was utilized in which the initial search terms were defined to guide the literature search strategy in online data sources (Scopus and Google Scholar). The inclusion criteria are articles published in the English language, document type (articles and conference papers), and source type (journal and conference proceedings). The exclusion criteria are book series and books. The authors’ search strategy was complemented with ChatGPT-generated keywords to reduce the risk of bias. Report synthesis was achieved by keyword grouping using Microsoft Excel, leading to keyword sorting in ascending order for easy identification of similar and dissimilar keywords. Three independent researchers were used in this research to avoid bias in data collection and synthesis. Articles were retrieved on 27 July 2024. Results: Out of 374 articles, 80 were selected as they were in line with the scope of the study. The review identified the combination of a machine learning technique and deep learning techniques for data limitations and processing of the nonlinear characteristics of air pollutants. ML models, such as random forest, and decision tree classifier were among the commonly used models for air quality index and air quality predictions, with promising performance results. Deep learning models are promising due to the hyper-parameter components, which consist of activation functions suitable for nonlinear spatiotemporal data. The emergence of low-cost devices for data limitations is highlighted, in addition to the use of transfer learning and federated learning models. Again, it is highlighted that military activities and fires impact the O₃ concentration, and the best-performing models highlighted in this review could be helpful in developing predictive models for air quality prediction in areas with heavy military activities. Limitation: This review acknowledges methodological challenges in terms of data collection sources, as there are equally relevant materials on other online data sources. Again, the choice and use of keywords for the initial search and the creation of subsequent filter keywords limit the collection of other relevant research articles.

Ubiquitous sensing and unique characteristics of wireless sensor networks (WSNs) have led to an increase in application areas such as smart parking, environmental monitoring, automotive industries and sports. In recent years, WSNs have gained more significance as the foundation infrastructure for the Internet of Things (IoT), which has greatly increased the number of connected objects with instantaneous communication and data processing. However, designing energy‐efficient models for integrating WSNs into IoT is a challenging issue due to scalability and interoperability of IoT, and previous approaches designed for WSNs cannot be applied directly. This study proposes two energy‐efficient models for WSNs in the IoT environment: (i) a service‐aware clustering model where individual sensor nodes are assigned roles based on their service delivery; and (ii) an energy‐aware clustering model. Performance evaluation shows better energy efficiency, end‐to‐end delay and network load balance of the proposed models for integrating wireless sensor networks into the IoT protocol compared with low‐energy adaptive clustering hierarchy centralised protocol and fuzzy C‐means clustering protocol.

Introduction: Globally, the prevalence of mental health problems, especially depression, is at an all-time high. The objective of this study is to utilize machine learning models and sentiment analysis techniques to predict the level of depression earlier in social media users' posts.Methods: The datasets used in this research were obtained from Twitter posts. Four machine learning models, namely extreme gradient boost (XGB) Classifier, Random Forest, Logistic Regression, and support vector machine (SVM), were employed for the prediction task.Results: The SVM and Logistic Regression models yielded the most accurate results when applied to the provided datasets. However, the Logistic Regression model exhibited a slightly higher level of accuracy compared to SVM. Importantly, the logistic regression model demonstrated the advantage of requiring less execution time.Discussion: The findings of this study highlight the potential of utilizing machine learning models and sentiment analysis techniques for early detection of depression in social media users. The effectiveness of SVM and Logistic Regression models, with Logistic Regression being more efficient in terms of execution time, suggests their suitability for practical implementation in real-world scenarios.

Wireless sensor networks (WSNs) have been widely applied in many areas for real‐time event detection. They are designed using both mobile and static sensor nodes (SNs) for different applications such as smart parking, environmental monitoring, health care systems, automotive industries, sports, open space surveillance, and so on. WSNs communicate through wireless mediums and are accessible to anyone, which make SNs susceptible to different types of attacks. Distributed denial of service (DDoS) is one such attack. It wastes the limited energy of SNs and causes loss of data packets within a network. A DDoS attack launches a coordinated attack by flooding the target nodes with bogus requests, thus exhausting their resources, and forcing them to deny service to legitimate member nodes. In this study, the authors propose a message analyser scheme for WSNs. The method is capable of detecting compromised SNs vulnerable to a DDoS attack. In addition, it is able to detect all compromised messages transmitted by the attackers to the base station through the sender nodes. The proposed method is compared with other related protocols. The results show that their method can effectively detect and defend against DDoS attacks in WSNs.