Andere Kunden interessierten sich auch für
![Advances in Knowledge Discovery and Data Mining]( "Advances in Knowledge Discovery and Data Mining")
Advances in Knowledge Discovery and Data Mining75,99 €![Advances in Knowledge Discovery and Data Mining]( "Advances in Knowledge Discovery and Data Mining")
Advances in Knowledge Discovery and Data Mining75,99 €![Erkennungsmodell für Cyber-Attacken - Anwendung von Data Mining]( "Erkennungsmodell für Cyber-Attacken - Anwendung von Data Mining")
Erkennungsmodell für Cyber-Attacken - Anwendung von Data Mining39,90 €![Feature Selection for Knowledge Discovery and Data Mining]( "Feature Selection for Knowledge Discovery and Data Mining")
Feature Selection for Knowledge Discovery and Data Mining32,99 €![DoS Attacks and Intelligent Neural Network]( "DoS Attacks and Intelligent Neural Network")
DoS Attacks and Intelligent Neural Network26,99 €![Feature Selection for Cancer Classification]( "Feature Selection for Cancer Classification")
Feature Selection for Cancer Classification51,99 €![Intrusion Detection using Feature Selection and Hybridized]( "Intrusion Detection using Feature Selection and Hybridized")
Intrusion Detection using Feature Selection and Hybridized26,99 €
Intrusion detection systems (IDS) are important elements in network defenses to help protect against increasingly sophisticated cyber attacks. This project objective presents a novel anomaly detection technique that can be used to detect previously unknown attacks on a network by identifying attack features. This effects-based feature identification method uniquely combines k-means clustering; NaiveBayes feature selection and C4.5 decision tree classification for finding cyber attacks with a high degree of accuracy and it used KDD99CUP dataset as input. Basically it detects whether the attacks are there or not, like IPSWEEP, NEPTUNE, SMURF.