Master_Research_Proposal_v2.ppt

PREPARED FOR
FACULTY OF COMPUTER & MATHEMATICAL SCIENCES
UNIVERSITI TEKNOLOGI MARA
Research Proposal
CS780
PREPARED BY
ZAID BIN AHMAD (2009673048)
SUPERVISOR
DR. KAMARULARIFIN ABD JALIL (UiTM)
DR. JAMALUL-LAIL AB MANAN (MIMOS BERHAD)

Agenda
• Research Topic
• Background of the study
• Problem identification
• Scope of the study
• Research Methodology
• Significant of the study

Enhancing MOSAODV
Routing Protocol Against
Black Hole Attacks

Mobile Ad Hoc Network (MANET)
• Collection of wireless devices that can be
deployed as multi-hop network without the aid of
any established infrastructure or centralized
administration.
• Routing Protocol based on trust and cooperation
• Two major ad hoc routing classification
– Reactive – e.g. AODV, DSR
– Proactive – e.g. DSDV, OLSR

Ad-hoc On-demand Distance Vector (AODV)
• Reactive Ad-hoc routing protocol [1,2]
• Combines on-demand and distance vector
• Broadcast Route Query, Unicast Route Reply
• Quick adaptation to dynamic link condition
and scalability to large scale network
• Route Discovery
– RREQ: route request
– RREP: route reply
• Route Maintenance
– RERR: route error

7
AODV Route Discovery
A D
B C
RREQ (Src, Dst)
RREP (Dst, Src)

8
AODV Route Maintenance
A D
B C
RERR (B)
RERR (C)

AODV Security
Security Consideration
“AODV does not specify any special security measures.
Route protocols, however, are prime targets for
impersonation attacks. If there is danger of such attacks,
AODV control messages must be protected by use of
authentication techniques, such as those involving
generation of unforgeable and cryptographically strong
message digests or digital signatures. ”
- RFC 3561

AODV Security
• Some types of potential attacks：
– Black hole
– Denial of service
– Routing table overflow
– Impersonation
– Energy consummation
– Information disclosure

Black Hole Attack
• Malicious node falsely claimed that it has a
shortest path / fresh route to destination [3,4]
• Manipulated routing control packet i.e. AODV
dest. seq. no. of RREP
• Immediate response to route request with
intention to intercept and source node will
assume this is the shortest path to destination.
• BH attack absorbs the packet by dropping data
packets or forwarding to unknown address after
gaining the routing path.

Blackhole Attack
D
S
S1
S2
M
S3
RREQ(D, 3)
RREQ(D, 3)
RREQ(D, 3)
RREQ(D, 3)
RREQ(D, 3)
Route Request
Route Reply
D
S
S1
S2
M
S3
RREP(D, 30)
RREP(D, 3)
RREP(D, 3)
RREP(D, 3)

Blackhole Attack
D
S
S1
S2
M
S3
DATA(S,D)
Data Transfer

Related Works - AODV-based
techniques against Black hole
• Payal N. Raj et al., 2009 [7] - second checking on RREP using
threshold value (dynamically update) and ALARM packet.
• L. Tamilselvan et al.,2008 [13] – Fidelity table (value > 0) and table
is updated through acknowledge from destination node. Store RREP
temporary in collection table.
• H. Weerasinghe et, al. 2008 [14] – enhancement of [8] by improving
the accuracy. Previous algorithm doesn’t check current intermediate
node if next hop node is not available.
• C. W. Yu et al.,2007 [10] – estimate table contains information of
neighbor nodes. Various stages to detect malicious node.
• S. Kurosawa et al.,2007 [11] – anomaly detection (normal state)
using dynamic training data, updated at regular time interval.

Related Works - AODV-based
techniques against Black hole
• M. Al-Shurman et al.,2004 [9] – 2 techniques
– Redundant route
– Tracking last-packet-sequence-number sent & received
• S. Ramaswamy et al., 2003 [8] - Data Routing Information (DRI)
table and Cross checking. Additional Further Request packet in
routing protocol.
• H. Deng et al.,2002 [12] – Cross checking using Further Request
packet

Related Works Summary
Proposed Detection
Techniques
Detection Proposal Assumption
of BH attack
Overhead
in time
N. Mistry et al., 2010 [6] Modified AODV using MOS_WAIT_TIME timer
to detect BH attack
Single YES Timer to gather RREP
packets
N. Mistry et al., 2009 [5] Modified AODV using MOS_WAIT_TIME timer
to detect BH attack
Single YES Timer to gather RREP
packets
P.N. Raj et al., 2009 [7] Dynamic Learning system using DPRAODV Multiple YES Interval time to
update threshold
L. Tamilselvan et al., 2008
[13]
Prevention of BH attack using Fidelity Table Multiple YES Timer to collect RREP
& analyse FT. Ack.
from dest. node.
H. Weerasinghe et al., 2008
[14]
Detection of BH using DRI and cross checking.
Enhance version of [8]
Multiple YES Further Request
process
C. W. Yu et al.,2007 [10] Distributed and cooperative mechanism Multiple YES Multiple stages
process
S. Kurosawa et al., 2007 [11] Detecting BH attack on AODV-based ad-hoc
network using dynamic anomaly detection
Multiple YES Dynamic updating
training data time
M. Al-Shurman et al., 2004
[9]
BH node detection using two different
solution
Multiple YES Time to find shared
nodes / hops
S. Ramaswamy et al., 2003
[8]
Cooperative BH Node detection using DRI and
cross checking
Multiple YES Further Request
process
H. Deng et al., 2002 [12] Single BH node detection Single YES Extra RREQ further
request to checks IN

MOSAODV
• Modified Secure AODV was introduced by N.H.
Mistry et al., 2009 / 2010 [5,6] to protect AODV from
Black Hole attack.
• In MOSAODV extra pre-processing function called
Pre_Receive_Reply (packet p) is added to AODV
protocol to detect Black hole attack by looking at
potential malicous node thru replied RREP info.
– Table Cmg_RREP_Tab
– Timer MOS_WAIT_TIME
– Variable Mali_node
• RREP with exceptional high destination sequence
number will be suspected as coming from malicious
node.

RREP Packets
Receive_Reply (p)
If p_dest_seq_no > rt.dest_seq_no
update rt
Data Transfer
RREP Packets are gathered into Cmg_RREP_tab until
MOS_WAIT_TIME expired
Receive_Reply (q)
If q.dest_seq_no > rt.dest_seq_no
update rt
Data Transfer
Analyze (p) until Cmg_RREP_tab empty
If p.dest_seq_no exceptional high
discard p and mali_node = p
Pre_Receive_Reply (p)
RREP Packets
Select highest des_seq_no q from Cmg_RREP _tab
MOSAODV
Normal AODV
1st RREP Packet p
Discard others

Black hole Attack Assumption
D
S
S1
S2
M
S3
RREP(D, 30)
RREP(D, 3)
RREP(D, 3)
RREP(D, 3)
MOSAODV assumes
1. Single Black hole in the network
2. Direct attack to source node

Black hole Attack Assumption
D
S
S1
M2
M1
S3
RREP(D, 30)
RREP(D, 3)
RREP(D, 3)
RREP(D, 3)
RREP(D, 40)
RREP(D, 40)
What happen if
1. More than one Black hole in the network
2. Fake RREP is sent indirectly through intermediate node

Problem Statement
• MOS_WAIT_TIME timer used to collect and analyze
RREP packet for detecting a Black hole attack gives a
drawback to MOSAODV protocol because it introduces
delay during executing the Pre_Received_Reply(). Thus
in route discovery process, there is an overhead in time.
• MOSAODV assumes there is a single Black hole attack
in the network directly to the source node. In the event
where more than one attack exist and the attacks are
thru intermediate node, this protocol has limitation in
controlling such attacks.
• In this research, we propose to enhance the MOSAODV
protocol by improving Pre_ReceivedReply() algorithm
that are able to reduce route discover time and to
handle multiple BH attacks.

Research Objectives
1. An enhancement for MOSAODV
protocol to reduce route discover time
2. An enhancement for MOSAODV
protocol to handle multiple Black hole
attacks

Scope of the study
• Only one protocol/technique will be studied
and to be enhanced – Modified Secure AODV
(MOSAODV) routing protocol
• Focus specifically in Route Discovery
• Request Reply Message
• Malicious node analysis/identification time
• Malicious node handling capability
• Attack model – non-cooperative Black hole
attack in MANET
• The study is through simulations

Approach
1. Quantitative – investigate problem through
simulations, collect & analyze data and
experiments
2. Simulations – measure and gather data
• normal AODV
• previous work MOSAODV
• AODV under BH attack
• MOSAODV under BH attack
3. Experiments
• reduce delay technique model
• Multiple Black hole attack model
4. Analysis and conclusion

Simulations
1. Steps in simulation study will be adopted
from J. Banks
2. Simulation tool is software NS2
3. Evaluation metrics
– Route discovery time: time taken from
RREQ is sent out until route is created
– Packet delivery ratio: originated packet over
received packet
– Average end-to-end-delay: average time
delay between sending and receiving.

Simulation Study
J. Banks, DISCRETE EVENT SYSTEM SIMULATION,
Prentice-Hall, Upper Saddle River, New Jersey, 2000.

Proposed Model 1
Source
Node
Routing Table
Update
Pre_ReceiveReply()
Intermediate
Nodes
RREP Replies
RREQ Broadcast
Time taken from initiating of RREQ until routing table is updated
1. Existing algorithm vs. New algorithm
Initiate RREQ(T1)
Update completed (T2)

Proposed Model 2
D
S
S1
M2
M1
S3
RREP(D, 30)
RREP(D, 3)
RREP(D, 3)
RREP(D, 3)
RREP(D, 40)
RREP(D, 40)
RREP(D, 45)
M3
List of Malicious
Nodes

Expected contribution
• Enhanced MOSAODV protocol with
efficient algorithm
– By reducing delay in order to detect malicious
Black hole attack in MANET
– By increasing capability to detect more than
one malicious Black hole attack

References
[1] C.E. Perkins, S.R. Das, and E. Royer, “Ad-Hoc on Demand Distance Vector
(AODV)”, March 2000, http://www.ietf.org/internet-drafts/draft-ietf-manet-aodv-05.txt
[2] Charles E. Perkins, Elizabeth M. Belding-Royer, Samir R. Das, Mobile Ad Hoc
Networking Working Group, Internet Draft, 17 February 2003.
[3] Hongmei Deng, Wei Li, and Dharma P. Agarwal, “Routing Security in Wireless Ad
Hoc Networks”, University of Cincinnati, IEEE Communications magazine, October
2002..
[4] Yi-Chun Hu, Adrian Perrig, “A Survey of Secure Wireless Ad Hoc Routing”, IEEE
Security and Privacy May/June 2004.
[5] N.H. Mistry, D.C. Jinwala and M.A. Zaveri, “MOSAODV: Solution to Secure AODV
against Black hole Attack”, International Journal of Computer and Network Security
(IJCNS), Vol. 1, December 2009
[6] Nital Mistry, Devesh C Jinwala and Mukesh Zaveri, “Improving AODV Protocol
against Black hole Attack”, Proceeding of International MultiConference of Engineers
and Computer Scientists (IMECS) 2010 Vol. II, March 17 – 19, 2010, Hong Kong.
[7] Payal N. Raj and Prashant B. Swadas,”DPRAODV: A dynamic learning system
against black hole attack in AODV based Manet”, International Journal of Computer
Science Issues (IJCSI), Vol. 2, Issue 3, pp: 54-59, 2009
[8] Sanjay Ramaswamy, Huirong Fu, Manohar Sreekantaradhya, John Dixon and
Kendall Nygard, “Prevention of Cooperative Black Hole Attack in Wireless Ad Hoc
Networks”. In Proceedings International Conference on Wireless Networks (ICWN),
2003.

[9] Mohammad Al-Shurman and Seong-Moo Yoon and Seungjin Park, “Black Hole Attack in
Mobile Ad Hoc Networks”
[10] Chang Wu Yu, Tung-Kuang, Wu, Rei Heng, Cheng, and Shun Chao Chang, “A
Distributed and Cooperative Black Hole Node Detection and Elimination Mechanism for Ad
Hoc Networks”, PAKDD 2007 Workshops, pp. 538–549, 2007
[11] Satoshi Kurosawa, Hidehisa Nakayama, Nei Kato, Abbas Jamalipour, and Yoshiaki
Nemoto, “Detecting Blackhole Attack on AODV-based Mobile Ad Hoc Networks by Dynamic
Learning Technique”, International Journal of Network Security, Vol.5, Issue 3, pp: 338–346,
2007
[12] Hongmei Deng, Wei Li, and Dharma P.Agrawal,“Routing Security in Wireless Ad Hoc
Network”, IEEE Communications Magzine, vol. 40, Issue: 10, 2002
[13] Latha Tamilselvan, Dr. V Sankaranarayanan, “Prevention of Blackhole Attack in
MANET”, The 2nd International Conference on Wireless Broadband and Ultra Wideband
Communications (AusWireless 2007), 2007
[14] Hesiri Weerasinghe, “Preventing Cooperative Black Hole Attacks in Mobile Ad Hoc
Networks: Simulation Implementation and Evaluation”, Proceedings of the Future
Generation Communication and Networking, vol. 02, pp: 362-367, 2007
[15] The VINT Project, The network simulator – ns-2 http://www.isi.edu/nsnam/ns/
[16] J. Banks, DISCRETE EVENT SYSTEM SIMULATION, Prentice-Hall, Upper Saddle
River, New Jersey, 2000.
References

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