Network performance analysis of blackhole attack using AODV routing protocol in VANET Ahmad Yusri Dak\[1\]\*, Arinah Sumayyah Zulkifli2 , Rafiza Ruslan3 , Nor Azirah Mohd Radzi4 *1,3First affiliation, City and Country (Please do not type or edit anything here, until final camera-ready paper submission)* *2Second affiliation, City and Country (Please do not type or edit anything here, until final camera-ready paper submission)*
ARTICLE INFO ABSTRACT

Article history:

Received

Revised

Accepted

Online first

Published 1 March 2024

 Vehicular ad-hoc network (VANET) is widely used in applications like highway automation, traffic management, and intelligent transportation systems due to its advantages over traditional communication systems. Connected Vehicle Technology (CVT) is a new feature in VANET that requires wireless data transmission between Vehicle to Infrastructure (V2I) and Vehicle to Vehicle(V2V) in future transportation system. Yet, due to the polarity of intelligent vehicles, infrastructure and automobile communication are subject to major security threats and numerous attacks. One of the most serious threats to network layer security is blackhole in which a malicious node falsely claims to have the shortest path to a destination and attracts incoming network traffic, enable to drop and manipulate the data, leading to potential disruption of car communication system. Therefore, the research is carried out to investigate a Blackhole attack in Ad-hoc On-Demand Distance Vector (AODV) routing protocol and evaluate its impact to intelligent vehicle network. The metrics used are End-to-End Delay (EED), Packet Delivery Ratio (PDR) and throughput simulated using Network Simulation 2. The scenarios created to compare performance with and without a Blackhole attack in dynamic movement of vehicle against congestion level. Thus, results showed that the blackhole attack in VANET significantly affects the network traffic, causing a tremendous delay of 175.05 ms or 80% with increasing number of nodes. By studying these aspects, improved security measures and protocols can be developed to safeguard VANET and ensure the reliability and safety of intelligent transportation systems.

Keywords:

VANET

Blackhole

AODV

End to End Delay

Network layer

DOI:

10.24191/jcrinn.v9i1
# InTRODUCTION The increasing popularity of wireless connectivity in personal devices has opened opportunities for developing numerous apps and services that rely on the Internet and interoperability (K. Lee et al., 2022). This means that more and more devices, such as smartphones and laptops, can now easily connect to the Internet wirelessly, enabling the creation of various applications and services that can communicate and work together. Moreover, advancements in wireless communication technologies have significantly impacted our quality of life. These advancements have improved the reliability, speed, and accessibility of wireless networks, enhancing our ability to stay connected, access information, and communicate effectively. VANET is employed in various applications, including highway automation, traffic management, and intelligent transportation systems, because they offer many advantages over traditional communication systems (M. Lee & Atkison, 2021). These include increased scalability, robustness, and flexibility. VANET also provides more accurate and up-to-date information (Khan et al., 2021), allowing vehicles to be aware of potential hazards on the road, traffic conditions, and alternative routes. VANET communications exchange Global Positioning System (GPS) coordinates, traffic route information, and emergency messages between automobiles and nodes (Mistareehi et al., 2022). Furthermore, VANET minimizes emissions by letting vehicles communicate with one another and coordinate their motions, resulting in more efficient driving and, as a result, lower emissions (M. Lee & Atkison, 2021). VANET comprises of intelligent nodes representing vehicles that may communicate with surrounding nodes and Roadside Units (RSU), which are called roadside infrastructure (Aziz, A., Samad, F., & Siddiqui, 2002). As the VANET depends on wireless communication channels and does not depend on a fixed infrastructure, it will be vulnerable to threats such as the Blackhole, Wormhole, and Sybil attacks. This study has focused on the Blackhole attack, as it is a type of malicious attack where a rogue node can intercept data packets and cause disruption or manipulation of data. This can have devastating consequences, such as the potential to hijack vehicles, disrupt communication between vehicles, or cause a denial of service to the entire network (Alshammari, A., Zohdy, M. A., Debnath, D., & Corser, 2020). Blackhole attack launched against VANET could cause data interruptions and manipulation (Lee & Atkison, 2020). If a malicious node spoofs its identity to intercept and drop data packets, the data could become unavailable or be routed in the wrong direction. This would result in the data moving in a different direction. Blackhole-based attacks are risky since they can facilitate several criminal activities. This includes the potential for vehicles to be hijacked, communication between vehicles to be disrupted, a denial of service to be delivered to the entire network, and data manipulation. Therefore, to protect against these attacks, it is critical to install suitable security measures and employ the most suitable routing protocol based on the traffic scenario. Performance metrics are also required for evaluating, comparing, optimizing, and troubleshooting systems or networks. By deploying routing protocol as well as acquiring insight through performance metric analysis such as EED, PDR and throughput, the network may strengthen its resilience to attacks and enable safe communication. The results demonstrated in this project show a significant decrease in throughput when Blackhole attacks were present, indicating the severe impact on the overall data transmission efficiency in VANET. Additionally, the experiments revealed a notable increase in EED and a decrease in PDR, highlighting the compromised network performance due to malicious nodes. ## RELATED WORK Referring to the statistics released by Malaysian Computer Emergency Response Team, Cybersecurity Malaysia in the year 2022 showed a significant increase in cyber-attacks involving wireless communication by 150% from 2013 until 2022. Two types of attacks that are often used by attackers to destroy the connection between a group of cars instead of communicating are wormhole and blackhole attack that is located at the network layer of the OSI model. A blackhole attack is rerouting the network traffic through a specific node controlled by the attacker. Attackers may be able to control the car’s communication and rerouting network traffic if there is no solution where intelligent cars are rapidly developing. According to (Fenzl et al., 2021), Tesla's smart car can be controlled using a combination of the black hole attack techniques and the experiment conducted in 2018 and 2021 proved lack of the current security approach. The result showed that current intelligent cars like Tesla Model S (P75 and P76) can be successfully manipulated using WIFI and cellular network. Attackers have compromised in-vehicle systems such as the IC, CID, and Gateway, and then spread malicious CAN messages into Tesla's database system using blackhole malicious attacks. Most attackers are detected using a technique like a watchdog (Krzysztoń & Marks, 2020; N. Premalatha, Manju Kumaresan, Shalini Devi Raja, 2020; Sharma et al., 2022), routing technique (Basomingera & Choi, 2020), profile databases (Arjoune et al., 2020) and cluster head (Chandravathi & Mahadevan, 2021) to improve detection rate. Intrusion attack performance using techniques like watchdog, database and routing reached a less satisfactory level with a detection rate of less than 32% while cluster head was on the scale of 46%. Packet Delivery Ratio (PDR) and Signal Strength (SS) are two common metrics used to measure the performance of intrusion attack detection at a lower layer of the OSI model (Nabou et al., 2018). Therefore, inappropriate use of the techniques, metrics and layers model will result in inaccurate outcomes in determining the performance of each intrusion. Examining these works provides valuable insights into the progress made in securing VANET and optimizing their performance. (Kumar et al., 2021) focused on detecting Blackhole attacks in VANET using a secure AODV routing algorithm. The problem addressed was the presence of malicious nodes acting as routers and injecting spoofed routing tables, which disrupt the network's performance. The researchers measured metrics such as EED, PDR, and throughput to evaluate their proposed method. The results demonstrated a significant improvement, with only 238 packets dropped using the proposed method compared to 1532 packets with the previous approach. (Bamhdi, 2020) proposed an efficient dynamic-power AODV routing protocol based on node density. The aim was to tackle the challenges of limited fixed infrastructure and low stability in VANET. The researcher evaluated the protocol's performance by analyzing Control Overhead, EED, Jitter, Packet Delivery Fraction, PDR, and throughput metrics. The findings showed an increase in Packet Delivery Ratio from 12% to 31% and a decrease in End-to-End Delay to 51%, indicating improved performance compared to previous methods. (Fatemidokht & Kuchaki Rafsanjani, 2020) proposed the QMM-VANET clustering algorithm, which considers Qos and monitoring malicious vehicles in VANET. The study tackled the challenges associated with the absence of fixed infrastructure and the need for an efficient routing protocol. The algorithm's performance was evaluated using EED, PDR, and throughput metrics. The findings revealed an increased PDR of 12% and a decreased EED of 45%, indicating improved performance compared to previous methods. These studies collectively contribute to the advancement of VANET security and performance optimization. By addressing specific challenges and evaluating the effectiveness of proposed solutions, it can provide valuable insights for enhancing the reliability, efficiency, and security of VANET communication. Table 1.0 shows a comparison between the previous research and related works. Table 1.0 Comparison between the previous research and related works
Author, Year Title Problem Statement Methodology Results & Findings
Kumar et al., 2021 Blackhole attack detection in Vehicular ad-hoc network using secure AODV routing algorithm

  • Node act as router

  • Malicious nodes inject spoofed routing table.

  • EED

  • PDR

  • Throughput

  • Total packets drop for proposed method is only 238, compared to 1532 for the previous approach.
Bamhdi, 2020 Efficient dynamic-power AODV routing protocol based on node density

  • No fixed infrastructure

  • Low stability

  • Control Overhead

  • EED

  • Jitter

  • Packet Delivery Fraction

  • Throughput

  • PDR ratio increased from 12% to 31%

  • EED decreased to 51%
(Fatemidokht & Kuchaki Rafsanjani, 2020) QMM-VANET: An efficient clustering algorithm based on QoS and monitoring of malicious vehicles in vehicular ad hoc networks

  • No fixed infrastructure

  • Developing efficient routing protocol is challenging

  • EED

  • PDR

  • Throughput

  • Increased PDR by 12%.

  • Decreased EED by 45%.
## METHODOLOGY This project, NS-2 is used as a simulation tool to recreate a VANET network and study its behaviour under different scenarios. The simulations involve varying the number of nodes (20, 30, 40, 50, and 60) to represent traffic congestion in a network area of 1000 x 1000 square meters. The project’s details and parameters are outlined in Table 2, providing a comprehensive overview of the setup, configurations, and simulation scenarios. Using NS-2, the project aims to gain insights into the performance and characteristics of VANET networks, especially in analyzing Blackhole attacks. Table 2 also summarizes the simulation details, including the simulator, mobility model, number of nodes, simulation area, simulation time, routing protocols and performance metrics evaluated in the study. Table 2 Network parameter
Parameter Value
Simulator NS 2.35
Mobility Model BonnMotion

Number of Nodes

 20,30,40,50,60

Simulation Area

 1000 𝑚2
Simulation time 140 s

Routing Protocol

 AODV
Performance Metric EED, PDR, throughput
Fig. 1 illustrates the process, which includes setting up the simulation environment, configuring parameters in a ".tcl" file, running the simulation, generating a trace file ".tr" with network activity data, using an AWK script ".awk" to extract relevant information from the trace file, analysing the data, and creating graphs using tools like Excel. The comprehensive process allows researchers to simulate and study network behaviour, extract data, and visualize results for analysis and interpretation. \\\\ Figure 1.0 The simulation process The EED measures the time it takes for packets to travel from the source node to the destination node in the network. By analysing the EED as part of parameters, the project aims to understand the presence of a Blackhole attack affects the time it takes for packets to reach their destination. ## RESULT AND ANALYSIS As more vehicles join Vehicular Ad Hoc Networks (VANETs), assessing the impact of blackhole attacks depends on minimizing EED. Increased vehicle density exacerbates the danger of blackhole attacks by increasing delay in V2V and V2I communications. Communication disruptions are more likely due to lengthier delays in data transmission, along with the dropping or manipulation of information by malicious nodes. In larger networks, dealing with blackhole attacks while maintaining reasonable EED becomes more intricate. Effective EED monitoring is crucial to detect and address these attacks, ensuring stability, reliability, and safety in VANETs amid growing vehicular participation. In a VANET network with a blackhole attack, delays or discards occur. As shown in Fig 2 comparison in VANET network without Blackhole attacks, where packets are delivered directly, the presence of Blackhole attacks leads to higher EED values. This impact damages network performance, highlighting the negative consequences of Blackhole attacks on EED. The simulated VANET network analysis evidence that such attacks significantly affect EED, resulting in longer packet delivery times for the majority of scenarios compared to those without these attacks.to scenarios without such attacks. ![](6594290fda604_media/media/image1.png) Fig 2 EED vs No of Nodes Figure 3.0 Fig 3 compares the PDR performance metric between without and with a Blackhole attack. The PDR indicates the percentage of successfully delivered packets out of the total sent. In the scenario without a Blackhole attack, packets are delivered as intended, establishing a baseline PDR to evaluate typical delivery reliability. In the scenario with a Blackhole attack, a malicious node intentionally drops or manipulates packets, leading to a decreased PDR. Comparing the PDRs provides insights into the impact of Blackhole attacks on packet delivery, helping assess the effectiveness of countermeasures and detection mechanisms. The analysis enables the development of strategies to enhance packet delivery and network reliability in VANET under Blackhole attack scenarios. > ![A graph with numbers and lines Description automatically generated](6594290fda604_media/media/image2.png) Fig 3 PDR vs No. of nodes For 20 nodes, the PDR without a Blackhole attack is 36.43%, indicating that approximately 36.43% of the packets were successfully delivered. However, in the presence of a Blackhole attack, the PDR drops significantly to 9.14%, indicating a substantial decrease in the successful delivery of packets. With 30 nodes, the PDR without a Blackhole attack is 57.69%, while the PDR with a Blackhole attack shows a slightly lower value of 36.43%. The difference between the two scenarios is relatively minimal, suggesting that the Blackhole attack has a limited impact on packet delivery in this configuration. In the case of 40 and 50 nodes, the PDR without a Blackhole attack remains higher at 63.5% and 63.43%, respectively. However, with the introduction of a Blackhole attack, the PDR decreases to 36.43% for both node configurations. This signifies a significant reduction in the successful delivery of packets due to the presence of the Blackhole attack. With 60 nodes, the PDR without a Blackhole attack is considerably high at 99.78%, indicating a near-perfect packet delivery rate. However, when a Blackhole attack is introduced, the PDR drops significantly to 10.01%, demonstrating a substantial decrease in the successful delivery of packets. These results illustrate the varying impact of Blackhole attack on the PDR in different node configurations. The presence of a Blackhole attack significantly degrades the successful delivery of packets, leading to decreased PDR values. Understanding these variations is crucial in assessing the effectiveness of countermeasures and developing strategies to mitigate the impact of Blackhole attacks on packet delivery in VANET environments. Fig 4 compares the throughput performance metric between without a Blackhole attack and another with a Blackhole attack. Throughput measures the amount of data transmitted over the network in a given time period. The first scenario represents normal network conditions, where data transmission occurs without interference. In the second scenario with a Blackhole attack, malicious node intentionally disrupts data transmission. By comparing the throughput in these scenarios, the project aims to evaluate the impact of a Blackhole attack on the network's data transmission efficiency. This analysis helps assess the network's resilience and identifies measures to improve performance and security in the presence of Blackhole attacks. > ![A graph with numbers and lines](6594290fda604_media/media/image3.png) Fig 4 Throughput vs No. of nodes For 20 nodes, the throughput is 29.37Kbps without a Blackhole attack and decreases to 7.37Kbps with a Blackhole attack. With 30 nodes, the throughput is 46.51Kbps without a Blackhole attack, dropping to 29.37Kbps with a Blackhole attack. For 40 nodes, the throughput remains the same without a Blackhole attack at 51.19Kbps but decreases to 29.37Kbps with a Blackhole attack. Similarly, with 50 nodes, the throughput is 51.13Kbps without a Blackhole attack but decreases to 29.37Kbps with a Blackhole attack. For 60 nodes, the throughput is 46.94Kbps without a Blackhole attack, and significantly decreases to 6.84Kbps with a Blackhole attack. The results indicate that the presence of a Blackhole attack significantly impacts the network's throughput. In all cases, the throughput is notably lower when a Blackhole attack is present compared to scenarios without the attack. This highlights the disruptive effect of Blackhole attacks on data transmission, emphasizing the importance of implementing effective countermeasures to mitigate their impact and maintain higher network efficiency and throughput. **CONCLUSION** The research successfully analysed Blackhole attacks in VANET using the AODV routing protocol. Blackhole Attacks enable to disrupt the network's efficiency and reliability in VANET. Thus, to improve VANET security, future work should explore different types of attacks, evaluate other routing protocols, and create trust-based methods and intrusion detection systems. 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