
Product details:
ISBN13: | 9783659515101 |
ISBN10: | 3659515108 |
Binding: | Paperback |
No. of pages: | 100 pages |
Size: | 220x150 mm |
Language: | English |
0 |
Category:
Feature Selection For Intrusion Detection Systems
Using data mining techniques
Publisher: LAP Lambert Academic Publishing
Date of Publication: 1 January 2014
Normal price:
Publisher's listprice:
EUR 54.90
EUR 54.90
Your price:
22 671 (21 592 HUF + 5% VAT )
discount is: 5% (approx 1 193 HUF off)
The discount is only available for 'Alert of Favourite Topics' newsletter recipients.
Click here to subscribe.
Click here to subscribe.
Availability:
printed on demand
Can't you provide more accurate information?
Long description:
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.