An analysis of intrusion detection classification using supervised machine learning algorithms on NSL-KDD dataset / Sarthak Rastogi ... [et al.]
From the past few years, Intrusion Detection Systems (IDS) are employed as a second line of defence and have shown to be a useful tool for enhancing security by detecting suspicious activity. Anomaly based intrusion detection is a type of intrusion detection system that identifies anomalies. Convent...
محفوظ في:
| المؤلفون الرئيسيون: | , , , |
|---|---|
| التنسيق: | مقال |
| اللغة: | English |
| منشور في: |
UiTM Cawangan Perlis
2022
|
| الموضوعات: | |
| الوصول للمادة أونلاين: | https://ir.uitm.edu.my/id/eprint/60675/1/60675.pdf https://ir.uitm.edu.my/id/eprint/60675/ https://crinn.conferencehunter.com/ |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| الملخص: | From the past few years, Intrusion Detection Systems (IDS) are employed as a second line of defence and have shown to be a useful tool for enhancing security by detecting suspicious activity. Anomaly based intrusion detection is a type of intrusion detection system that identifies anomalies. Conventional IDS are less accurate in detecting anomalies because of the decision taking based on rules. The IDS with machine learning method improves the detection accuracy of the security attacks. To this end, this paper studies the classification analysis of intrusion detection using various supervised learning algorithms such as SVM, Naive Bayes, KNN, Random Forest, Logistic Regression and Decision tree on the NSL-KDD dataset. The findings reveal which method performed better in terms of accuracy and running time. |
|---|