Aug 2026· International Conference on Circuit, Power and Computing Technologies· pp. 27-32· 0 citations· 26 references
Abstract
Cyberattacks are becoming more frequent and sophisticated in today’s digital world, rendering conventional security measures inadequate. In order to increase the accuracy of cyber threat detection, this study investigates the application of deeplearning methods to increase the accuracy of cyber threat detection. A cybersecurity dataset was used to test four classification models: Artificial Neural Networks (ANN), Random Forest, XGBoost, and Logistic Regression. The models were evaluated using the key 95.32. The performance of Artificial Neural Networks, Random Forest, XGBoost, and Logistic Regression was examined. These findings imply that learning-based and ensemble models are better at spotting intricate and changing attack patterns. In general, the study highlights the significance of clever, data-driven methods for creating cybersecurity defence systems that are quicker, more dependable, and more resilient.
The rapid increase in the number of cyberattacks in the digital age has reduced the effectiveness of conventional cybersecurity systems. Traditional methods of cybersecurity face considerable difficulty when detecting new, sophisticated attacks and advanced exploitation techniques swiftly. This research addresses critical cybersecurity concerns by developing an AI-driven Intrusion Detection System (IDS), which employs Random Forest (RF) algorithms to detect cyberattacks efficiently. The evaluation of the model was conducted using three publicly available datasets: CICIDS2017 (692,703 records), NSL-KDD (148,517 records), and UNSW-NB15 (257,673 records) with various attack backgrounds and network configurations. A set of evaluation metrics, including accuracy, precision, recall, and F1-score, was employed to assess the performance of the cyberattack detection prototype. Across the three datasets, the model attained an average accuracy of 99.85%, precision of 99.83%, recall of 99.91%, and an F1-score of 99.87%, while maintaining low error rates, with an average false positive rate of 0.25% and a false negative rate of 0.10%. The results indicate that Random Forest is an effective solution for cyberattack detection in data-driven environments. The model was developed with lightweight and easy-to-deploy criteria, but the evaluation reported in this study was done under benchmark test conditions. This work improves the effectiveness of machine learning-based intrusion detection systems and serves as a stepping stone for future research on operational and real-time deployment of machine learning-based intrusion detection systems.
Phathutshedzo Cyprin Ramuhovhi, Naume Sonhera, T. Zuva· Latin American Journal of Co...· 0 citations
The role of artificial intelligence, innovations, challenges, and prospects for its development in the field of cybersecurity, the advantages and challenges of its implementation, as well as potential opportunities and threats to information security are examined.
A. B. Temirova, Kyadai B. Tsechoeva· EKONOMIKA I UPRAVLENIE: PROB...· 0 citations
Cybersecurity is an increasingly critical concern due to the growing complexity and frequency of cyberattacks, particularly against critical infrastructures. Conventional security techniques such as intrusion detection and anomaly detection are often insufficient in addressing sophisticated threats like advanced persistent threats. This paper introduces a novel hybrid approach that combines LogBERT Transform for feature extraction, support vector machine for classification, and the Archimedes optimization algorithm for parameter tuning. This unique integration overcomes the limitations of traditional methods, improving detection accuracy while reducing false alarms. The proposed methodology is shown to outperform conventional systems, achieving 98.36% classification accuracy, 97.77% precision, 96.45% recall, and 97.10% F1-score. The model's low false positive and false negative rates demonstrate its practical feasibility in cybersecurity applications. This approach also enhances model interpretability and scalability, making it well-suited for deployment in large-scale networks and IoT systems, with further improvements in scalability and interpretability being key future directions.
Mingyan Liu, Xu Chao· International Journal of Inf...· 0 citations
The results show that XAI can improve the transparency, trustworthiness and effectiveness of AI-based cybersecurity systems, in addition to highlighting a range of privacy, adversarial robustness, scalability and evaluation challenges that warrant further research to ensure reliable deployment in the real world.
Raman Kumar· International Journal of Adv...· 0 citations
The sophistication of cyber threats is growing, and there is a growing need for timely detection and response to security threats, which is now possible with the help of artificial intelligence (AI) based Intrusion Detection System (IDS). While the accuracy of detection has increased with the implementation of more sophisticated machine learning and deep learning models, those models tend to be opaque and complicated, making it difficult for cybersecurity professionals to understand, verify and believe automated predictions. The study explores how XAI can enhance the understanding and accuracy of artificial intelligence (AI) intrusion detection systems (IDSs). The study is carried out using the qualitative method which examines the application of the existing techniques of XAI such as feature attribution, local or global explanation models, visualization techniques and rule based interpretations for explaining the techniques and gaining enhanced confidence of the analyst and informed security decisions. The secondary data used in this research was obtained from scholarly articles, cybersecurity frameworks, industry reports, and case studies to identify real-world applications, problems in implementation, as well as the current trends of the explainable AI for cyber defense. The results showed that embedding explainability in an IDS enhances the human-AI partnership, allowing security analysts to confirm the results of their IDS, mitigate false-positive ambiguity, optimize incident response, and meet regulatory and ethical obligations. Other challenges remain such as: maintaining the explainability attribute while obtaining the predictive performance, handling large traffic density, avoiding adversarial manipulation on the explanation mechanisms, and scalability. The study finds explainable AI to be an important milestone on the path towards trustworthy and responsible cybersecurity systems. By enabling organizations to make their security operations more resilient, boost the trust in automated cyber defense, and enhance transparency without compromising detection, XAI can help organizations achieve these goals. The study provides valuable insights for practitioners in the cybersecurity industry, AI developers, decision makers and organizations developing intrusion detection systems that are transparent, reliable and ethically responsible in the dynamic digital landscape.
Christian Manna Guimma· Scriptora International Jour...· 0 citations
The review finds that artificial intelligence enables proactive threat detection, anomaly identification, and the automation of analysis and response at a scale beyond human capacity, yet its effectiveness is constrained by adversarial machine learning, data quality and drift, false positives, opacity, and the dual use of generative models by attackers.
N. Hussain· WORLD JOURNAL OF INNOVATION...· 0 citations
