INTRUSION DETECTION SYSTEM
Keywords:
IDS,NIDS,HIDS,KDD cup 1999 data set, neural networkAbstract
Intrusion detection system has become a very necessity in computer network era. Now a days it is expanding day by day.security has become a vital issue for modern computer systems.Exchange of data in any communication process is very essential and its security is very important in any network because of the increase in unauthorized accesses and attacks. Intrusion Detection system plays a major role in computer security that can be classified as Host- based Intrusion Detection System (HIDS), which protects a certain host or system or an application, Network-based Intrusion detection system (NIDS), which protects a network of hosts and systems,Anomaly based Intrusion detection system and Signature based misuse. There are several Intrusion detection system techniques for both anomaly and misuse intrusion detection. In this paper we described many research on various types of attacks and different kinds of intrusion detection system.
References
1. O’.I.Begimov, T.M.Bo‘riboyev / Extracting tagging from exocardiographic images via machine learning algorithmics // Analysis of world scientific views International Scientific Journal Vol 2 Issue 1 IF(Impact Factor)8.2 / 2023
2. O’.I.Begimov, T.M.Bo‘riboyev / General Theory About the Traditional Methods and Algorithms of Machine Learning // AMERICAN Journal of Public Diplomacy and International Studies Volume 02, Issue 04, 2024 ISSN (E):2993-2157.
3. T.M.Bo‘riboyev / Hetnet tizimi asosida avtonobillaring harakat trafigini boshqarish va tahlil qilish // Nejmettin, 03-06 Ekim 2023 tarihlerinde Erbakan Üniversitesi ve Alfraganus üniversitesi öncülüğünde düzenlenen “ipek Yolunun Ötesinde kongreler dizisi: Bir Yol, Bir Kuşak: Göç, turizm ve ekonomi politik Kongresi (SIRCON 2023)” programına sertifika almak için katıldı. (Sayfa 320-324)
4. NIST AI 100-2 E2023 Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations https://csrc.nist.gov/pubs/ai/100/2/e2023/final Проверено: 15.07.2024
5. Perdisci, Roberto, et al. "Misleading worm signature generators using deliberate noise injection." 2006 IEEE Symposium on Security and Privacy (S&P'06). IEEE, 2006.
6. 14 Risks and Dangers of Artificial Intelligence (AI) https://builtin.com/artificial-intelligence/risks-of-artificial- intelligence Проверено 15.08.2024.
7. Song, Junzhe, and Dmitry Namiot. "A survey of the implementations of model inversion attacks." International Conference on Distributed Computer and Communication Networks. Cham: Springer Nature Switzerland, 2022.
8. NIST SP 800-53 Rev. 5 Security and Privacy Controls for Information Systems and Organizations https://csrc.nist.gov/pubs/sp/800/53/r5/upd1/final Проверено 15.08.2024.
9. OWASP https://owasp.org/ Проверено 15.08.2024.
