STARS: A Statistical Traffic Pattern Discovery System
for MANETs
ABSTRACT:
Many anonymity
enhancing techniques have been proposed based on packet encryption to protect
the communication anonymity of mobile ad hoc networks (MANETs). However, in
this paper, we show that MANETs are still vulnerable under passive statistical
traffic analysis attacks. To demonstrate how to discover the communication
patterns without decrypting the captured packets, we present a novel
statistical traffic pattern discovery system (STARS). STARS works passively to
perform traffic analysis based on statistical characteristics of captured raw
traffic. STARS is capable of discovering the sources, the destinations, and the
end-to-end communication relations. Empirical studies demonstrate that STARS
achieves good accuracy in disclosing the hidden traffic patterns.
EXISTING SYSTEM:
Evidence-based
statistical traffic analysis model, every captured packet is treated as evidence
supporting a point-to-point (one-hop) transmission between the sender and the
receiver. A sequence of point-to-point traffic matrices is created, and then
they are used to derive end- to-end (multihop) relations. This approach
provides a practical attacking framework against MANETs but still leaves
substantial information about the communication patterns undiscovered. MANET
systems can achieve very restricted communication anonymity under the attack of
STARS.
Statistical traffic analysis attacks have attracted broad
interests due to their passive nature, i.e., attackers only need toc ollect
information and perform analysis quietly without changing the network behavior
(such as injecting or modifying packets). The predecessor attacks and
disclosure attacks are two representatives.
However, all these previous approaches do not work well
to analyze MANET traffic because of the following three natures of MANETs:
1) The broadcasting nature: In wired networks, a
point-to-point message transmission usually has only one possible receiver.
While in wireless networks, a message is broadcasted, which can have multiple
possible receivers and so in curs additional uncertainty.
2) The ad hoc nature: MANETs lack network
infrastructure, and each mobile node can serve as both a host and a router.
Thus, it is difficult to determine the role of a mobile node to be a source, a destination,
or just a relay.
3) The mobile nature: Most of existing traffic
analysis models does not take into consideration the mobility of communication
peers, which make the communication relations among mobile nodes more complex.
DISADVANTAGES
OF EXISTING SYSTEM:
Ø Approaches do not work well to analyze MANET traffic.
Ø The scheme fails to address several important
constrains when deriving the end-to-end traffic from the one hop evidences.
Ø It does not provide a method to identify the actual
source and destination nodes (or to calculate the source/destination
probability distribution).
Ø Most of the previous approaches are partial attacks
in the sense that they either only try to identify the source (or destination)
nodes or to find out the corresponding destination (source) nodes for given
particular source (destination) nodes.
PROPOSED SYSTEM:
Ø We propose a novel STARS for MANETs. STARS is basically
an attacking system, which only needs to capture the raw traffic from the
PHY/MAC layer without looking into the contents of the intercepted packets.
Ø From the captured packets, STARS constructs a sequence
of point-to-point traffic matrices to derive the end-to-end traffic matrix, and
then uses a heuristic data processing model to reveal the hidden traffic
patterns from the end-to-end matrix.
Ø In this paper, we propose a novel statistical
traffic pattern discovery system (STARS). STARS aims to derive the
source/destination probability distribution, i.e., the probability for each
node to be a message source/destination, and the end-to-end link probability
distribution, i.e., the probability for each pair ofnodes to be an end-to-end
communication pair.
Ø To achieve its goals, STARS includes two major
steps:
1) Construct point-to-point
traffic matrices using the time-slicing technique, and then derive the
end-to-end traffic matrix with a set of traffic filtering rules; and
2) Apply a
heuristic approach to identify the actual source and destination nodes, and
then correlate the source nodes with their corresponding destinations.
ADVANTAGES
OF PROPOSED SYSTEM:
The attacker can
take advantage of STARS to perform traffic analysis as follows:
Ø Divide the entire network into multiple regions
geographically;
Ø Deploy sensors along the boundaries of each region
Ø To monitor the cross-component traffic;
Ø Treat each
region as a super node and use STARS to figure out the sources, destinations,
and end-to-end communication relations; and
Ø Analyze the
traffic even when nodes are close to each other by treating the close nodes as
a super node.
SYSTEM
ARCHITECTURE:
WORK
FLOW:
SYSTEM
REQUIREMENTS:
HARDWARE REQUIREMENTS:
Ø
System : Pentium IV 2.4 GHz.
Ø
Hard Disk :
40 GB.
Ø
Floppy Drive : 1.44
Mb.
Ø
Monitor : 15
VGA Colour.
Ø
Mouse :
Logitech.
Ø Ram : 512 Mb.
SOFTWARE
REQUIREMENTS:
Ø Operating system : Windows
XP/7/LINUX.
Ø Implementation : NS2
Ø NS2 Version : NS2.2.28
Ø Front
End : OTCL (Object Oriented
Tool Command Language)
Ø Tool : Cygwin (To simulate in Windows OS)
REFERENCE:
Yang Qin,
Dijiang Huang, and Bing Li,“STARS: A Statistical Traffic Pattern Discovery
System for MANETs,” MARCH/APRIL 2014.
