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.
An AI-driven Intrusion Detection System (IDS), which employs Random Forest (RF) algorithms to detect cyberattacks efficiently, and indicates that Random Forest is an effective solution for cyberattack detection in data-driven environments.
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
A novel hybrid approach that combines LogBERT Transform for feature extraction, support vector machine for classification, and the Archimedes optimization algorithm for parameter tuning overcomes the limitations of traditional methods, improving detection accuracy while reducing false alarms.
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 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.
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...
N. Hussain· WORLD JOURNAL OF INNOVATION...· 0 citations
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