2026· EKONOMIKA I UPRAVLENIE: PROBLEMY, RESHENIYA· Vol 4/15, pp. 61-68· 0 citations
TL;DR
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.
Abstract
In the modern digital world, where cyber-attacks are becoming increasingly sophisticated and widespread, the use of artificial intelligence in the field of cybersecurity is extremely relevant. The issue of cybersecurity is gaining more and more importance, and conducting relevant research is a necessary aspect of ensuring security in the digital space. In this regard, we propose an article that examines the role of artificial intelligence, innovations, challenges, and prospects for its development in the field of cybersecurity. The use of artificial intelligence to automate the processes of detecting, analyzing, and responding to cyber threats, as well as its practical significance for increasing the level of security in the digital space and the effectiveness of protection against cyber threats, is also investigated. The leading approaches for this study are trend analysis and expert assessments, which allow for a comprehensive examination of the directions for the development of artificial intelligence in the field of cybersecurity, the advantages and challenges of its implementation, as well as potential opportunities and threats to information security.
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.
D. Sharma, Inderdeep Kaur, Krishika Gupta et al.· International Conference on...· 0 citations
Artificial intelligence influences the areas of our lives, including forensic investigations. The accelerated development of cutting-edge technologies has brought a series of advantages but also vulnerabilities and risks. Excessive dependence affects several aspects of the criminal process. The paper analyzes the duality of artificial intelligence in the forensic process, highlighting both its role as a security and efficiency tool for investigations, as well as its potential for risk in the context of cybercrime and ethical and legal challenges, aiming to identify a balance between the advantages of technology and the respect for fundamental rights through responsible use in forensic activity. The theme is current through the novel elements addressed. The article represents a point of view on AI and its use. The development of artificial intelligence generates new forms of crime, for which it is necessary to have a clear legislative framework, ethical control mechanisms and interdisciplinary collaboration between specialists in law, computer science, sociology and cybersecurity. The future of all humanity depends on international cooperation in combating new forms of crime and finding a set of unanimously accepted norms regarding the use of AI.
R. Moisoiu· Development Through Research...· 0 citations
A structured taxonomy is proposed to organize various dimensions of AI-driven cybersecurity; review them critically; and finally, discuss key challenges, open problems, and emerging trends.
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
Background: India faced a surge of cyber threats, from their frequency to sophistication, as the growth of Digital Technologies, Cloud Computing, Internet of Things (IoT), Artificial Intelligence (AI), and the rise of online financial services. In this regard, machine learning (ML) is a potential solution that offers intelligence, adaptability, and real-time detection and prevention of cyber threats. While the studies on ML applications in the field of cybersecurity have been conducted, a thorough synthesis study on the available evidence on cybersecurity in the Indian context has yet to be carried out.Objective: This systematic review will focus on evaluating the literature related to machine learning techniques for real-time cyber threat detection and prevention in India.Methods: This study used a systematic review design and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines were followed. A thorough literature search was performed in all major electronic databases such as IEEE Xplore, Scopus, Web of Science, ScienceDirect, SpringerLink, ACM Digital Library and Google Scholar. The articles were included if they were published in English and reviewed by peers from 2020-2026.Conclusion: Machine learning has proven to be a transformative technology for enhancing cybersecurity, with its ability to detect threats intelligently, adaptively, and automatically. Despite the progress made, there is still a need for more research and development to build easily explainable, scalable, and context-specific machine learning models that can adapt to India's growing cybersecurity challenges. The insights from this review offer tangible support for researchers, practitioners, and policymakers in their quest for enhanced AI-driven cybersecurity solutions and a more secure digital landscape.
Kartikeya Tiwari· International journal of com...· 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