ANALYZING THE IMPACT OF BLACKHOLE ATTACKS ON AODV AND DSR ROUTING PROTOCOLS’ PERFORMANCE IN NS-2

International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol. 13, No. 2/3/4, August 2023
DOI 10.5121/ijcsea.2023.13402 19
ANALYZING THE IMPACT OF BLACKHOLE
ATTACKS ON AODV AND DSR ROUTING
PROTOCOLS’ PERFORMANCE IN NS-2
Ferdinand Alifo1
, Mustapha Awinsongya yakubu2
, Martin Doe3
and Michael
Asante4
1
MIS/Computer Dep., Ministry of Local Government, Local Gov’t Service.
2
University of Cincinnati Ohio, USA
3
Computer Science Department, University of Business and Integrated Development
Studies, Ghana
4
Department of Computer Science, Kwame Nkrumah University of Science and
Technology, Ghana
ABSTRACT
Mobile Ad-Hoc Networks (MANETs) are wireless networks characterized by their lack of a fixed
infrastructure, allowing nodes to move freely and serve as both routers and hosts. These nodes establish
virtual links and utilize routing protocols such as AODV, DSR, and DSDV to establish connections.
However, security is a significant concern, with the Blackhole attack posing a notable threat, wherein a
malicious node drops packets instead of forwarding them. To investigate the impact of Blackhole nodes and
assess the performance of AODV and DSR protocols, the researchers employed the NS-2.35 ns-allinone-
2.35 version for simulation purposes. The study focused on several metrics, including average throughput,
packet delivery ratio, and residual energy. The findings revealed that AODV demonstrated better energy
efficiency and packet delivery compared to DSR, but DSR outperformed AODV in terms of throughput.
Additionally, environmental factors and data sizes were taken into account during the analysis.
KEYWORDS
Performance Analyss, AODV, DSR, MANET, Protocols, Security, Blackhole, NS2
1. INTRODUCTION
Routing protocols have a crucial role in Mobile Ad hoc Network (MANET) research,
concentrating on guaranteeing efficient and secure communication. [1]. Several routing protocols
have been specifically developed for MANETs, including AODV, DSR, DSDV, OLSR, among
others. MANETs encounter vulnerabilities attributed to factors such as an open access medium,
dynamic topology changes, absence of central administration or monitoring systems, cooperative
algorithms, and transparent protective mechanisms [2]. Due to their attractive features, MANETs
are vulnerable to malicious activities. Therefore, emphasizing security concerns becomes crucial
to enhance network availability, data reliability, and privacy. Since MANETs function wirelessly,
they are susceptible to different forms of attacks, including Blackhole attacks, saturating attacks,
routing table overflow attacks, Denial of Service (DoS) attacks, Sybil attacks and impersonation
attacks, [3]. These threats pose a serious risk to the availability, integrity, and confidentiality of
network services. Among these threats, the Blackhole Attack stands out as one of the most
dangerous attacks on MANETs. This attack has been known to have a considerable impact on
MANET reactive routing protocols, such as Ad-hoc OnDemand Distance Vector (AODV) and

20
Dynamic Source Routing (DSR) [4]. To ensure network services' availability and safeguard user
privacy, it is imperative to address network security issues. This research utilizes the NS2
Simulator to investigate the performance of AODV and DSR protocols in Mobile Ad Hoc
Networks (MANETs) using various metrics, including Average Throughput, Packet Delivery
Ratio (PDR), and Residual Energy at the destination node. The primary objective of the paper is
to assess how the AODV and DSR routing protocols respond to a Blackhole attack and how this
security risk impacts crucial variables such as packet delivery ratio, average throughput, and
residual energy. Moreover, the study analyzes the existing body of knowledge on evaluating
routing protocol performance under security attacks, gaining insights into the behavior and
effectiveness of AODV and DSR during a Blackhole attack.
MANETs are gaining popularity in diverse sectors due to their wireless network connectivity and
cable-free convenience. However, their vulnerability to security attacks, particularly Blackhole
attacks, raises significant concerns. By scrutinizing the behavior of AODV and DSR routing
protocols under Blackhole attacks, this research yields valuable insights into these vulnerabilities
and can potentially inform strategies to bolster MANET security.
2. RELATED WORKS
[3]discusses Mobile Ad hoc Networks (MANETs), which are dynamic, infrastructure-less
wireless networks comprising mobile, self-organized nodes. Routing protocols in MANETs find
routes between nodes for communication. The study highlights that numerous routing protocols
are designed to handle dynamic topology changes. However, MANET's characteristics, like
openness and restricted resources, make it vulnerable to security attacks, especially Distributed
Denial of Service (DDoS) attacks. DDoS attacks consume system resources, affecting the
efficiency of nodes and making them unavailable to legitimate users. The research aims to use a
simulation model called DDoS-Sim to examine the impact of DDoS attacks on routing protocols
like AODV, DSR, and DSDV. The model utilizes the NS-2 network simulator to apply DDoS
attacks on the selected routing protocols and analyze their performance in terms of packet
delivery fraction and end-to-end delay.
In [5], a Mobile Ad Hoc Network (MANET) consists of freely and mobile nodes that form a
temporary dynamic wireless network without any fixed infrastructure. These nodes act as both
hosts and routers, forwarding data packets to their destinations. The communication and
connectivity are facilitated by routing protocols like Ad hoc On -Demand Distance Vector
(AODV).
However, due to the lack of security measures and the absence of infrastructure, MANETs are
vulnerable to various security threats and attacks. This paper investigates the impact of two types
of attacks, namely Blackhole and Wormhole Attacks, on the AODV routing protocol using
Network Simulator version 2 (NS2) environment. The goal of these attacks is to prevent data
packets from reaching their intended destinations.
Given the prevalence of mobile devices in today's world, the authors in [6] have conducted a
comprehensive review to explore various routing protocol technologies applicable to MANETs.
The study covers different types of routing protocols, their classifications, routing techniques,
geographical coverage, route metric, route repository, and route reconfiguration strategies in
detail. The [6] compares the various routing protocols discussed in the study, highlighting their
respective areas of strength. Additionally, the research examines network simulators that have
these protocols enabled by default to gain insights into their practical implementation.

21
The authors in [7] proposed a cooperative analysis method to detect malicious nodes in MANETs.
The mechanism involves protocols working together to analyze and reliably identify malicious
nodes. They validate their method through simulations using mobile nodes and different routing
protocols such as AODV, DSR, OLSR, and DSDV. The results show that the malicious node
detection rate is excellent, with low overhead, a relatively high packet delivery proportion, and
quick response times, particularly when there are changes in mobility speed.
According to [8], Mobile Ad hoc Networks (MANETs), routing protocols' performance is
negatively affected by various network attacks, including black hole and rushing attacks. The
vulnerability to these attacks extends to all layers of mobile ad hoc networks. MANETs lack
centralized administration control and infrastructure, making them less secure. The paper
evaluates the Ad hoc On-Demand Distance Vector (AODV) routing protocol under rushing
attacks, black hole attacks, and grayhole attacks. The study assesses the network's performance
under different attack scenarios, considering various factors like speed, network size, node
density, and the number of attacking nodes. Performance metrics such as average throughput,
average end-to-end delay, and packet delivery ratio are used for evaluation. The results show that
the attacks lead to poor throughput, increased packet dropping, and higher end-to-end delays in
the network. AODV is relatively less affected by rushing attacks compared to black hole and
grayhole attacks.
In [9], the authors proposed an approach called Stable-Ad hoc On Demand Distance Vector
(AODV) to enhance the routing performance of AODV in mobile ad hoc networks. The approach
aims to improve route stability by considering node's residual energy and link quality, estimated
through received signal power, against dynamic threshold values for hop selection during route
discovery. If both values exceed their thresholds, the node increases the stability factor of the
route before forwarding the route request (RREQ) packet. This process continues until the
destination node is reached. If a later duplicate RREQ packet with a better stability factor arrives,
the node processes it. Additionally, the approach maintains local neighborhood information
through hello messages. When a node receives a hello message from its neighbor, it updates the
neighbor table based on its current residual energy and link connectivity status using distance and
hello message delay parameters. Simulation results demonstrate that the proposed Stable-AODV
algorithm outperforms traditional AODV in terms of packet delivery ratio, throughput, delay,
control message overhead, and normalized routing load.
The authors of [10] proposed an enhanced trust model for securing data transfer in Mobile Ad hoc
Networks (MANETs) against malicious attacks. The proposed scheme combines blind trust and
referential trust to establish a more robust trust model. To achieve this, the trust relationship
function is integrated with the dynamic source routing (DSR) protocol, making the protocol more
secure. The authors conduct a thorough analysis of the DSR protocol and generate performance
matrices for packet-related data, including packets sent, packets received, packets loss, and
throughput. They also evaluate the effectiveness of the improved trust establishment scheme by
implementing three algorithms in the NS2 simulator to detect and prevent various types of
attacks.
The main focus of the paper is to enhance the security of data transfer in MANETs by introducing
a trust-based approach and integrating it with the DSR protocol for better protection against
malicious attacks. The proposed scheme is evaluated and compared against different types of
attacks through simulations in the NS2 simulator.
In [11], the authors introduced the Dynamic Source Routing (DSR) Protocol to enhance quality-
ofservice (QoS) by calculating path reliability and link sustainability criteria. The protocol uses
node energy, centrality, degree, and link cost to select relay nodes. Qualified relaying nodes are

22
utilized to transmit data packets from the source node to the cluster head (CH). The suggested
technique outperforms other existing routing protocols in terms of packet loss ratio, energy
consumption, and delay. It achieves a lower packet loss ratio of approximately 4%, which is 3%
less than the existing methods.
3. METHODOLOGY
In this paper, we employ the ns-2.35 simulator on Ubuntu LINUX to simulate Blackhole attack
on the AODV and DSR routing protocols using Object Tool Command Language (OTCL). This
simulation enables us to examine and compare the performance metrics of both protocols when
the network was exposed to a Blackhole attack. Through these simulations, we were able to
assess the behaviour and effectiveness of AODV and DSR in the presence of such an attack. By
utilizing ns-2.35 as our simulation tool, we were able to evaluate how the Blackhole attack affects
the routing protocols and draw significant findings about their performance under the metrics.
3.1. Performance Metrics
This study employed three key metrics to evaluate the research work. The first metric, Average
Throughput, measures the average data transmission rate within the network, reflecting its overall
performance. The second metric, PDR (Packet Delivery Ratio), is essential as it indicates the
proportion of successfully delivered packets, providing insights into reliability and minimizing
data losses. The third metric, Residual Energy, assesses the energy levels of nodes, offering
valuable information on network efficiency and longevity.
3.2. Simulation Parameters
Simulation parameters govern a model's actions and qualities, influencing outcomes and findings.
They can be modified to examine various situations or circumstances as shown in table 2.
Table 1. Simulation Parameters
Parameter Settings
Channel type Channel/Wireless channel
Propagation Model Propagation/TwoRayGround
Physical Type Phy/WirelessPhysical
Mac Protocol Type Mac/802_11
Interface queue type Queue/DropTail/CMU/PriQueue
Link layer type LL
Antenna model Antenna/OmniAntenna
Maximum packet in queue 50
Number of mobile nodes 17
Routing protocol AODV and DSR
Transmission Range 550 Meters
Interference Range 550 Meters
Start Time/Stop Time 1 Second/15 Seconds
Packet in queue 5, 10, 15, 20, 25, 30
Number of Blackholes nodes 2

23
3.3. Routing Topology
Routing topology is the plan arrangement of conceptual elements (nodes) in a communication
network. For this study, fifteen (15) legitimate nodes and two (2) malicious nodes were set in the
topography of 956m by 600m simulation environment. Two commands nam AODV.nam and
nam DSR.nam were executed to invoke network animator (NAM) to show the path for AODV
and DSR respectively with N
Figure 1: Screenshot of NAM with 17 nodes including two false nodes
3.4. Simulator Execution and setup
Following the successful assembly of NS-2 version ns-allinone-2.35 via Ubuntu on VMware
station 16, Six (6) important files required for simulation were copied to the installation folder's
home directory for execution. The Six (6) files comprises of;
• Blackhole.tcl
• average_throu.awk
• energy.awk
• pdr.awk
• AODV.tr
• DSR.tr
Location path for these files;
2.35/bin:/home/ferdinand/ns-allinone-
2.35/Blackhole/home/ferdinand/ns-allinone-2.35/ns-
#PATH
PATH=$PATH:/home/ferdinand/ns-allinone-2.35/bin:/home/ferdinand/nsallinone-
2.35/tcl11.5.0/unix:/home/ferdinand/ns-allinone-2.35/tk11.5.0/unix
LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/home/ferdinand/ns-allinone-
2.35/otcl-1.14:/home/ferdinand/ns-allinone-2.35/lib NS=/home/ferdinand/ns-allinone-2.35 /ns-
2.35/
NAM=/home/ferdinand/ns-allinone-2.35 /nam-
1.14/ PATH=$PATH:$XGRAPH:$N$S:$$NAM

24
3.5. Simulation Experiment for AODV
After validating the NS-2.35 installation and modifying all of the parameters, changes were made
to the Blackhole.tcl file to simulate the AODV routing protocol;
• Interface queue type was set to Queue/DropTail/PriQueue
• Routing protocol was set to AODV tcl file was created as Blackhole.tcl
• trace file was set to tracefile [open AODVblack.tr B]
• nam file for path was set to namfile [open AODVblack.nam B]
• node 10 and node 11 were set as Blackhole node
• packets in queue [5, 10, 15, 20, 25, and 30]
3.6. Simulation Experiment for DSR
After validating the NS-2.35 installation and modifying all of the parameters, the Blackhole.tcl
file was modified to simulate the DSR routing protocol:
• Interface queue type was set to CMUPriQueue
• Routing protocol was set to DSR
• tcl file was created as Blackhole.tcl
• trace file was set to tracefile [open DSRblack.tr B]
• nam file for topology was set to namfile [open DSRblack.nam B]
• nam file for path was set to namfile [open AODVblack.nam B]
• node 10 and node 11 were set as Blackhole node
4. RESULT AND ANALYSIS
Table 3 showing Average Throughput, Packet Delivery Ratio, and Residual Energy as
performance metrics and data sizes displays the simulation results for AODV and DSR.
Table 2: Results of performance metrics for AODV and DSR with data sizes
Protocol
Speed/Data
Average
Throughput
Packet Delivery
Ratio(PDR) Residual
Energy (J)
Size
AODV
5
0.288707 0.993377 1.57784
DSR 0.327946 0.983577 1.410075
AODV
10
0.328707 0.993449 3.155679
DSR 0.367946 0.983725 2.820152
AODV
15
0.368707 0.993521 4.733519
DSR 0.407946 0.983871 4.230228
AODV
20
0.408707 0.99359 6.311358
DSR 0.447946 0.984014 5.640304
AODV
25
0.448707 0.993658 7.889198
DSR 0.487946 0.984155 7.05038

25
AODV
30
0.488707 0.993724 9.467037
DSR 0.527946 0.984293 8.460455
4.1. Average Throughput Analysis
Average throughput analysis is a technique employed to assess the mean rate at which data is
transmitted within a specific time frame in a given system.
Figure 2. A graph of Average Throughput for AODV and DSR
DSR has higher throughput compared to AODV under Blackhole attack as shown in figure 2, due
to its ability to detect and avoid malicious routes, enabling successful packet transmission.
Additionally, DSR uses efficient routing mechanisms, reducing overhead and maintaining higher
throughput even under attack.
4.2. Packet Delivery Ratio Analysis
Packet Delivery Ratio (PDR) analysis was employed to assess the efficiency and dependability of
transmitting data packets within the communication system.
Figure 3. A graph of Packet Delivery Ratio for AODV and DSR

26
AODV outperforms DSR in PDR under Blackhole attack as shown in figure 3 due to better route
detection and avoiding malicious node routes. AODV's efficient routing mechanisms enable
lower overhead transmission, ensuring higher PDR even under attack.
4.3. Residual Energy Analysis
Residual energy analysis was utilized to evaluate the energy levels that remain in nodes within
energy-constrained systems like wireless sensor networks.
Figure 4. A graph of Residual Energy for AODV and DSR
AODV is more energy-efficient than DSR under Blackhole attack as shown in figure 4, due to its
ability to detect and avoid malicious routes, while DSR is more vulnerable. AODV's efficient
routing mechanisms require less energy to transmit packets, allowing nodes to conserve energy
even during attacks.
5. CONCLUSIONS
This study conducted a performance analysis of the AODV and DSR routing protocols under
Blackhole attacks, using PDR, Residual Energy, and Average Throughput as metrics. The
simulation results indicated that DSR had a higher Throughput but AODV had better PDR and
Residual Energy. These findings can help MANET network designers and managers select the
best protocols for their unique network requirements. .
ACKNOWLEDGEMENTS
We express our gratitude to the reviewers for their thorough examination of the paper, as well as
their valuable comments and suggestions that significantly enhanced the quality of the
manuscript. Also, our gratitude goes to University of Science and Technology KNUST, Kumasi
and University of Cincinnati, Ohio for their support
REFERENCES
[1] B. H. Khudayer et al., "A Comparative Performance Evaluation of Routing Protocols for Mobile
Ad-hoc Networks," International Journal of Advanced Computer Science and Applications, vol. 14,
no. 4, 2023.

27
[2] S. Al-Emadi and A. Al-Mohannadi, "Towards enhancement of network communication architectures
and routing protocols for FANETs: A survey," in 2020 3rd International Conference on Advanced
Communication Technologies and Networking (CommNet), 2020: IEEE, pp. 1-10.
[3] M. T. Sultan, H. El Sayed, and M. A. Khan, "Performance Analysis of the Impact of DDoS Attack
on Routing Protocols in Infrastructure-less Mobile Networks," in 2022 5th International Conference
on Communications, Signal Processing, and their Applications (ICCSPA), 2022: IEEE, pp.1-6.
[4] T. Safdar Malik, M. N. Siddiqui, M. Mateen, K. R. Malik, S. Sun, and J. Wen, "Comparison of
blackhole and wormhole attacks in cloud MANET enabled IoT for agricultural field
monitoring," Security and Communication Networks, vol. 2022, 2022.
[5] M. H. Al Rubaiei, H. S. Jassim, and B. T. Sharef, "Performance analysis of black hole and worm
hole attacks in MANETs," International Journal of Communication Networks and Information
Security, vol. 14, no. 1, pp. 126-131, 2022.
[6] A. C. Onuora, E. E. Essien, and P. Ana, "A Comprehensive Review of Routing Protocols for Mobile
Ad Hoc Networks (Manets)," International Journal of Information System and Computer
Science (IJISCS) Vol, vol. 6, pp. 1-13, 2022.
[7] K. Thamizhmaran, "Secure Analysis of Routing Protocol under Wormhole for MANET," Journal
of Advancement in Electronics Design, vol. 5, no. 2, pp. 47-52, 2022.
[8] P. Sarao, "Performance Analysis of MANET under Security Attacks," J. Commun., vol. 17, no. 3,
pp.194-202, 2022.
[9] P. Pandey and R. Singh, "Efficient ad hoc on demand distance vector routing protocol based on
route stability in MANETs," International Journal of Wireless Information Networks, vol. 29, no. 3,
pp. 393-404, 2022.
[10] S. A. Syed and A. Shahzad, "Enhanced dynamic source routing for verifying trust in mobile ad hoc
network for secure routing," International Journal of Electrical and Computer Engineering, vol. 12,
no. 1, p. 425, 2022.
[11] C. N. Kishore and H. V. Kumar, "Dynamic source routing protocol for robust path reliability and
link sustainability aware routing in wireless communication," Optik, vol. 282, p. 170036, 2023.
AUTHORS
Ferdinand Alifo is the Head of ICT/MIS department with Ministry of Local Government
in Ghana. He holds an MSc Information Technology from KNUST, a BSc in Computer
Science, and His expertise is further validated by his certifications in Cisco Certified
Network Professional (CCNP) and Cisco Certified Network Associate (CCNA).
Throughout his career, Ferdinand has made significant contributions to improving IT
systems and services within the government and private sectors
Mustapha Awinsongya Yakubu is a Graduate Teaching Assistant at the University of
Cincinnati where he is currently studying for PhD Information Technology. His research
focuses in areas of Enterprise Architecture & Security, Human Computer Interaction, and
Crisis Informatics. He earned his Master’s degree in Information Technology from the
Kwame Nkrumah University of Science and Technology. He has over a decade experience
working in both public and private sector as an IT Professional.
Martin Doe is a Ph.D student at the University of Business and integrated development
Studies, Ghana. He had MPhil in Information Technology at the Department of Computer
Science in the Kwame Nkrumah University of Science and Technology, Ghana. His
research area includes Cyber Security, computer networks and internet of things.
Prof. Michael Asante is an Associate Professor in Computer Science at the Department of
Computer Science at the Kwame Nkrumah University of Science and Technology, Ghana.
His research areas include Computer Security, Cyber Security, and Network

ANALYZING THE IMPACT OF BLACKHOLE ATTACKS ON AODV AND DSR ROUTING PROTOCOLS’ PERFORMANCE IN NS-2

More Related Content

Similar to ANALYZING THE IMPACT OF BLACKHOLE ATTACKS ON AODV AND DSR ROUTING PROTOCOLS’ PERFORMANCE IN NS-2 (20)

Recently uploaded (20)

ANALYZING THE IMPACT OF BLACKHOLE ATTACKS ON AODV AND DSR ROUTING PROTOCOLS’ PERFORMANCE IN NS-2