Open AccessAnalysis of Machine Learning Techniques for Detection System for Web Applications Using Data Mining
Author(s) -
Jugnesh Kumar,
Shruti Goyal,
Pradeep Bedi,
Sunil Kumar,
Ashish Shrivastava
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1099/1/012034
Subject(s) - computer science , intrusion detection system , key (lock) , machine learning , host (biology) , artificial intelligence , baseline (sea) , intrusion , ensemble learning , network security , data mining , computer security , ecology , oceanography , geochemistry , biology , geology
Security is a key problem to each computer and computer networks. Intrusion detection System (IDS) is one of the most important research problems in community safety. IDSs are advanced to stumble on each acknowledged and unknown assaults. IDS employs many methods to secure information systems and networks against community-based and host-based threats. IDS utilises different machine learning methods. This thesis analyses IDS machine-learning methods. It also discusses several similar research completed between 2000 and 2012 and specialises in engineering techniques. Linked experiments include used unmarried, hybrid, ensemble, baseline, and datasets.