- All Journals
- Al-Qadisiyah University
- Basrah University
- Kirkuk University
- Mosul University
- The Iraqi Borad for Medical Specialization
- University of Anbar
- University of Technology
- Al Mustafa University
- Al-Zahraa University for Women
- Alnoor University
- Kunooz University
- Misan University
- Ninevah University
- Tikrit University
- University of Al-Hamdaniya
- University of Kerbala
Machine Learning to Enhance Security Systems | ||
| IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING | ||
| Volume 24, Issue 3, September 2024, Pages 31-45 PDF (799.17 K) | ||
| Document Type: Review Paper | ||
| DOI: 10.33103/uot.ijccce.24.3.3 | ||
| Author | ||
| Saja A. Talib* | ||
| Majeed | ||
| Abstract | ||
| This research explores the integration of machine learning into security systems, addressing the growing challenges posed by cyber threats. It traces the historical context of cybersecurity measures and the development of machine learning in intrusion detection, anomaly detection, and behavioral analysis. By examining traditional security approaches and their limitations, the study highlights the transformative potential of machine learning. Case studies provide concrete examples of successful applications, demonstrating how machine learning reduces cyber threats. Ethical considerations, implementation challenges, biases, and regulatory aspects are discussed, highlighting the complexities of integrating machine learning into security frameworks. Furthermore, the research explores emerging technologies in cybersecurity and offers insights into the future of machine learning in security. In conclusion, the importance of continuous research is emphasized, positioning machine learning as a dynamic force shaping the future of digital defense and overcoming various challenges in the cybersecurity landscape. | ||
| Keywords | ||
| Machine Learning; Security Systems; Cybersecurity | ||
|
Statistics Article View: 479 PDF Download: 78 |
||