Loading article...
Loading article...
Stream/Area:
-
Subject:
-
Paper ID:
IJSREM65037
Volume:
10 |
6 |
Jun 2026
Pages:
1-3
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Master of computer application
UBDT college of Engineering
Master of computer application
UBDT college of Engineering
The widespread adoption of digital technologies across various sectors has increased the risk of cyberattacks targeting computer networks and information systems. Organizations face numerous security challenges, including malware, phishing, ransomware, data breaches, and unauthorized access attempts. Traditional security solutions mainly rely on predefined rules and known attack signatures, making them less effective against emerging and previously unseen threats. As cyber threats become more sophisticated, the demand for intelligent and adaptive security mechanisms continues to grow. This study explores the role of artificial intelligence in cyber threat detection.
KEYWORDS
The increasing number of cyber threats has created a strong need for advanced security solutions capable of protecting modern digital systems. Conventional security methods are often limited in their ability to identify emerging and sophisticated attacks, making intelligent detection mechanisms more important than ever. The overall analysis shows that AI-based threat detection systems can enhance the identification of malicious activities, support faster decision-making, and improve the efficiency of cybersecurity defenses. Although challenges such as data quality and evolving attack methods continue to exist, future developments in Artificial Intelligence are expected to further improve the effectiveness of cyber threat detection and contribute to a more secure digital environment.
[1] S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 4th Edition, Pearson, 2021. [2] W. Stallings, Network Security Essentials: Applications and Standards, Pearson Education, 2022. [3] V. Kumar and S. Gupta, "Machine Learning Approaches for Intrusion Detection Systems," International Journal of Cyber Security, vol. 15, no. 2, pp. 45–58, 2023. [4] A. Sharma and R. Patel, "Artificial Intelligence in Cybersecurity: Opportunities and Challenges," Journal of Information Security Research, vol. 12, no. 1, pp. 23–36, 2024. [5] J. Brown, "Deep Learning Techniques for Network Intrusion Detection," IEEE Access, vol. 11, pp. 10215–10230, 2023.
Publication Date
6/19/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
Documents
Article Views
0
Total views since publication
PDF Downloads
5
Total downloads since publication
Geographic Reach
5
Digital Preservation: This article is permanently archived on Zenodo and complies with all data preservation standards across major indexing platforms.
Plagiarism Screening
This manuscript has been checked for plagiarism using TurnitinPlagiarismChecker software.
Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
Related research in the same subject area
Loading similar articles...