Devashish M. Patil,
Darshan B. Khairnar,
Atharva B. Jagzap,
Anand N. Gharu,
- Student, Department. of Computer Engineering, MET Bhujbal Knowledge City Institute of Engineering, Savitribai Phule Pune University, Nashik, Maharashtra, India
- Student, Department. of Computer Engineering, MET Bhujbal Knowledge City Institute of Engineering, Savitribai Phule Pune University, Nashik, Maharashtra, India
- Student, Department. of Computer Engineering, MET Bhujbal Knowledge City Institute of Engineering, Savitribai Phule Pune University, Nashik, Maharashtra, India
- Assistant Professor, Department. of Computer Engineering, MET Bhujbal Knowledge City Institute of Engineering, Savitribai Phule Pune University, Nashik, Maharashtra, India
Abstract
It is now more difficult than ever to safeguard enterprises against cyberattacks due to their fast growth and growing sophistication. Stronger cyberattack detection systems are becoming more and more necessary as hostile strategies continue to evolve in order to safeguard information, preserve corporate trust, and protect sensitive data. An overview of contemporary detection techniques is given in this study, with a focus on integrating machine learning (ML) to increase efficacy. A thorough analysis of conventional methods is conducted, including hybrid anomaly-based detection and signature-based detection, pointing out both their benefits and drawbacks. Although these techniques have been useful in recognizing known dangers, they frequently fail to identify new or developing assaults, exposing businesses to serious risks. A useful remedy for these drawbacks is offered by machine learning approaches, which provide the capacity to continuously learn, adapt, and recognize intricate attack patterns that traditional systems could miss. Additionally, ML-based models increase detection speed and accuracy while reducing human error. The operational issues with conventional systems are also covered in the study, along with cutting-edge solutions to these problems. Organizations may greatly improve cybersecurity resilience and adjust to the ever-changing threat landscape by utilizing machine learning.
Keywords: Ensemble learning, classification, machine learning, feature extraction, adaptive learning, random forest
[This article belongs to Journal Of Network security ]
Devashish M. Patil, Darshan B. Khairnar, Atharva B. Jagzap, Anand N. Gharu. A Survey on Ensemble Technique for Enhanced Cyberattack Detection. Journal Of Network security. 2025; 13(03):50-54.
Devashish M. Patil, Darshan B. Khairnar, Atharva B. Jagzap, Anand N. Gharu. A Survey on Ensemble Technique for Enhanced Cyberattack Detection. Journal Of Network security. 2025; 13(03):50-54. Available from: https://journals.stmjournals.com/jons/article=2025/view=227919
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Journal Of Network security
| Volume | 13 |
| Issue | 03 |
| Received | 12/06/2025 |
| Accepted | 05/09/2025 |
| Published | 17/09/2025 |
| Publication Time | 97 Days |
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