A Detailed Investigation and Analysis of Using Machine Learning Techniques for Intrusion Detection

Intrusion detection is one of the important security problems in todays cyber world. A significant number of techniques have been developed which are based on machine learning approaches. However, they are not very successful in identifying all types of intrusions. In this paper, a detailed investiga...

Mô tả chi tiết

Lưu vào:
Hiển thị chi tiết
Tác giả chính: Mishra, P.
Đồng tác giả: Varadharajan, V.
Định dạng: BB
Ngôn ngữ:en_US
Thông tin xuất bản: IEEE Explore 2020
Chủ đề:
Truy cập trực tuyến:http://tailieuso.tlu.edu.vn/handle/DHTL/9969
Từ khóa: Thêm từ khóa bạn đọc
Không có từ khóa, Hãy là người đầu tiên gắn từ khóa cho biểu ghi này!
Mô tả
Tóm tắt:Intrusion detection is one of the important security problems in todays cyber world. A significant number of techniques have been developed which are based on machine learning approaches. However, they are not very successful in identifying all types of intrusions. In this paper, a detailed investigation and analysis of various machine learning techniques have been carried out for finding the cause of problems associated with various machine learning techniques in detecting intrusive activities. Attack classification and mapping of the attack features is provided corresponding to each attack. Issues which are related to detecting low-frequency attacks using network attack dataset are also discussed and viable methods are suggested for improvement. Machine learning techniques have been analyzed and compared in terms of their detection capability for detecting the various category of attacks. Limitations associated with each category of them are also discussed. Various data mining tools for machine learning have also been included in the paper. At the end, future directions are provided for attack detection using machine learning techniques.