THE PROCESS OF IDENTIFYING ANLMALIES IN INFORMATION ATTACKS

Authors

  • Dildora Davlatova Nordic international university

Keywords:

anomaly detection, information attacks, cyber security, machine learning, statistical analysis, behavior-based analysis

Abstract

This article explores the process of detecting anomalies in information attacks, focusing on methodologies, tools, and technologies employed in cyber defense systems. By understanding the behaviors and patterns of legitimate activities, deviations—referred to as anomalies—can be identified, signaling potential malicious activities. The paper discusses various anomaly detection approaches, such as statistical methods, machine learning algorithms, and behavior-based techniques. Case studies highlight the application of these methods in real-world scenarios. The study underscores the importance of anomaly detection in fortifying information security against evolving cyber threats.

References

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Ahmed, M., Mahmood, A. N., & Hu, J. (2016). A survey of network anomaly detection techniques. Journal of Network and Computer Applications, 60, 19-31.

Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000). LOF: Identifying density-based local outliers. Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data.

Pimentel, M. A. F., Clifton, D. A., Clifton, L., & Tarassenko, L. (2014). A review of novelty detection. Signal Processing, 99, 215-249.

Davlatova, D. (2023). MENEJERLARNING TASHKILOT QARORLARINI QABUL QILISHDA SUN’IY INTELLEKTDAN FOYDALANISH. INNOVATIVE DEVELOPMENT IN THE GLOBAL SCIENCE, 2(7), 65-68.

Davlatova, D. (2023). MENEJERLAR UCHUN SUN’IY INTELEKTNING BILIM ASOSLARI. Interpretation and researches, 1(14).

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Published

2024-12-16

Issue

Vol. 3 No. 0003 (2024): Nordic Studies in Academic Research

Section

Scientific articles

License

Copyright (c) 2024 Dildora Davlatova

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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