Delay Tolerant Networking (DTN) is pioneered as
an approach in network architecture to address the technical
problems in non-homogeneous networks that may reduce
continuous network connectivity. The behavioral
characterization of malware based on Naive Bayesian model
is an alternative approach to pattern matching for detecting
proximity malware. Computer is an important part of an
everyday life to many people across the world. Computer in
the hand of consumer to lack the knowledge of protection
tools and to have limited administrator skill are vulnerable to
Virus attack .these system are extremely valuable to
intruders as they have lot of secret personal information
about the users. Attacker exploit vulnerabilities in the
software layers to install malicious program on users
Machine to steal secret data for financial gains. Security
protocols have been in place for some time to counter the
threat posed by the attack however, despite the presence of
such measures; the number of attacks on consumer computer
is growing rapidly. A recent trend in attacks has been the
attempt to disable security protocol In a place at the host
machine. This type of attacks leaves the host computer
completely defenseless and vulnerable to many further
exploits through the internet.
Published In : IJCAT Journal Volume 3, Issue 3
Date of Publication : March 2016
Pages : 134 - 136
Figures :01
Tables : --
Publication Link :Behavioral Malware Detection in Delay-Tolerant
Networks
Najim Sheikh : M-Tech 1st RGPV
Priya Patil : BE RTMNU
Sneha Pillewan : BE RTMNU
Suman Suryavanshi : BE RTMNU
Vaishnavi Nilapwar : BE RTMNU
DTN, Signature-Based Malware Detection
Techniques
In this survey a series of malware detection techniques
have been presented. The problems related to traditional
signature based detection method is also highlighted. Rate
of new malware’s causing destruction to systems
worldwide is increasing at alarming rate. Detection of
malware’s changing their signatures frequently has posed
many open research issues. Challenge lies in the
development of good disassemble, similarity analysis
algorithm so that the variants of malware’s can be detected
in shorter time there by reducing the computation over
head. Behavioral characterization of malware is an
effective alternative to pattern matching in detecting
malware, especially when dealing with polymorphic or
obfuscated malware. Naive Bayesian model has been
successfully applied in non-DTN settings. The general
behavioral characterization of DTN-based proximity
malware with look ahead proposed, along with dogmatic
filtering and adaptive look ahead, to address two unique
challenging in extending Bayesian filtering to DTNs:
“insufficient evidence versus evidence collection risk” and
“filtering false evidence sequentially and in a distribute
manner.”
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