Feature Selection For Intrusion Detection Systems: Using data mining techniques - Gulshan Kumar,Krishan Kumar,Yogesh Kumar
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Toimitus 12-18 arkipäivässä
30 päivän palautusoikeus
Network security is a serious global concern. The increasing prevalence of malware and incidents of attacks hinders the utilization of the Internet to its greatest benefit and incur significant economic losses. The traditional approaches in securing systems against threats are designing mechanisms that create a protective shield, almost always with vulnerabilities. This has created Intrusion Detection Syste ... Täydellinen kuvaus
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Kuvaus
Network security is a serious global concern. The increasing prevalence of malware and incidents of attacks hinders the utilization of the Internet to its greatest benefit and incur significant economic losses. The traditional approaches in securing systems against threats are designing mechanisms that create a protective shield, almost always with vulnerabilities. This has created Intrusion Detection Systems to be developed that complement traditional approaches. However, with the advancement of computer technology, the behavior of intrusions has become complex that makes the work of security experts hard to analyze and detect intrusions. In order to address these challenges, data mining techniques have become a possible solution. However, the performance of data mining algorithms is affected when no optimized features are provided. This is because, complex relationships can be seen as well between the features and intrusion classes contributing to high computational costs in processing tasks, subsequently leads to delays in identifying intrusions. Feature selection is thus important in detecting intrusions by allowing the data mining system to focus on what is really important.
Lisätietoja
| Kirjoittaja | Gulshan Kumar, Krishan Kumar, Yogesh Kumar |
|---|---|
| Julkaisija | LAP LAMBERT Academic Publishing |
| Julkaisuvuosi | 2014 |
| Kannen tyyppi | Pehmeäkantinen |
| EAN | 9783659515101 |