As computer attacks are becoming more and more
difficult to identify the need for better and more efficient
intrusion detection systems increases. The main problem with
current intrusion detection systems is high rate of false alarms.
Distributed Denial of Service (DDoS) attacks is large-scale
cooperative attack launched from a large number of
compromised hosts called Zombies is a major threat to internet
services. This paper presents various significant areas where data
mining techniques seems to be a strong candidate for detecting
and preventing DDOS attack. Purpose for this work is to examine
how to integrate multiple intrusion detection sensors in the order
to minimize the number of incorrect-alarms.
Published In : IJCAT Journal Volume 1, Issue 11
Date of Publication : 31 December 2014
Pages : 587 - 592
Figures :02
Tables : --
Publication Link :A Powerful Tool for Intrusion Detection &
Clustering Techniques and Methodology
Sonal R.Chakole : Computer Department, Nagpur University, Professor in Priyadarshini J. L. College of Engg.
Maharashtra , India
Vijaya Balpande : Computer Department, Nagpur University, Professor in Priyadarshini J. L. College of Engg.
Maharashtra , India
Vyenktesh Giripunge : Computer Department, Nagpur University, Professor in Priyadarshini J. L. College of Engg.
Maharashtra , India
DDOS
Intrusion
Data Mining
Zombies
IP
Traceback
DDoS attacks are quite complex methods of attacking a
Computer network, ISP, individual system makes it
ineffectual to legitimate network users. These attacks are an
aggravation at a minimum, and if they are against a
particular system, they can be brutally destroying. Loss of
network resources costs money, delays work, and interrupts
Communication between various legal network users. The
drastic consequences of a DDoS attack make it important
that strict and productive solutions and security measures
must be made to prevent these types of attacks. Detecting,
preventing, and mitigating DDoS attacks is important for
national and individual security.
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