Review: Boosting Classifiers For Intrusion Detection
作者： Richa Rawat , Anurag Jain
DOI:
关键词: Decision tree 、 Classifier (UML) 、 Data mining 、 Web service 、 Hacker 、 Intrusion detection system 、 Boosting (machine learning) 、 Support vector machine 、 Machine learning 、 Anomaly-based intrusion detection system 、 Engineering 、 Artificial intelligence
摘要: ABSTRACT— Network and host intrusion detection systems monitor malicious activities the management station is a technique that generates reports. Security for all networks becoming big problem. Hackers intruders higher number of successful efforts to bring down company's web services. Intrusion system, availability an attack protect integrity data used attacks. In this paper, we use variety feature reduction techniques system (IDS) compare performance classifiers. There are two phases in certain ways, first phase will improve decision tree SVM classifiers best results second boost both classifiers, detect more than single class classifier system. Keywords— Detection System (IDS), Boosting, support vector machine (SVM)
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