Feature Selection For Intrusion Detection Systems - Kumar, Yogesh; Kumar, Krishan; Kumar, Gulshan; - Prospero Internetes Könyváruház

Feature Selection For Intrusion Detection Systems: Using data mining techniques
 
A termék adatai:

ISBN13:9783659515101
ISBN10:3659515108
Kötéstípus:Puhakötés
Terjedelem:100 oldal
Méret:220x150 mm
Nyelv:angol
0
Témakör:

Feature Selection For Intrusion Detection Systems

Using data mining techniques
 
Kiadó: LAP Lambert Academic Publishing
Megjelenés dátuma:
 
Normál ár:

Kiadói listaár:
EUR 54.90
Becsült forint ár:
23 865 Ft (22 728 Ft + 5% áfa)
Miért becsült?
 
Az Ön ára:

22 671 (21 592 Ft + 5% áfa )
Kedvezmény(ek): 5% (kb. 1 193 Ft)
A kedvezmény csak az 'Értesítés a kedvenc témákról' hírlevelünk címzettjeinek rendeléseire érvényes.
Kattintson ide a feliratkozáshoz
 
Beszerezhetőség:

Megrendelésre a kiadó utánnyomja a könyvet. Rendelhető, de a szokásosnál kicsit lassabban érkezik meg.
Nem tudnak pontosabbat?
 
  példányt

 
Hosszú leírás:
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.