Journal of Industrial and Systems Engineering

Journal of Industrial and Systems Engineering

Enhancing the Security of Mobile Ad-hoc Networks: A Black Hole Attack Mitigation Approach Using Response Time and Machine Learning Models

Document Type : Research Paper

Authors
Fatimah Jasim Mohammed 1
Azam Andalib 2
Hossein Azgomi 2
Seyed Ali Sharifi 3
1 Computer Engineering Department, Urmia University, Urmia, Iran
2 Department of Computer Engineering, Ra.C., Islamic Azad University, Rasht, Iran
3 Department of Computer Engineering, Bon.C., Islamic Azad University, Bonab, Iran
Abstract
As a type of wireless network, mobile ad-hoc networks (MANETs) consist of mobile nodes that are able to move freely and independently in any direction and operate in a self-configurable and self-organizing manner, especially in situations such as natural disasters, military operations, or large social events that require rapid setup without the need for fixed infrastructure. are absolutely vital. However, these networks face numerous challenges and are naturally vulnerable due to the limited resources of nodes, such as energy, processing, and memory, as well as the lack of pre-designed infrastructure. This vulnerability can lead to a variety of attacks and security threats, one of which is One of the most common is black hole attacks. In these types of attacks, attackers attract network traffic by luring other nodes and then ignore or eliminate it entirely, which can seriously affect network performance. In this paper, a new approach to detect black hole attacks through anomaly detection techniques using response generation time and machine learning models is proposed. It continuously monitors the activities of nodes and examines and analyzes real-time network traffic, and uses machine learning algorithms to identify behavioral characteristics, irregularities, and abnormal activities and differentiate them from other nodes. Analysis of the results of the nightshades shows increased accuracy in detecting black hole attacks and ensuring the security of mobile Ad-hoc networks. This approach not only helps to detect attacks faster, but also increases the overall efficiency of the system.
Keywords
Mobile Ad-hoc Networks
Routing
Black Hole Attack
Destructive Node
Deep Learning and Response Time
Subjects
Abdulhammed, R., Faezipour, M., Abuzneid, A. S., & Abumallouh, A. (2019). Deep and machine learning approaches for anomaly-based intrusion detection of imbalanced network traffic. IEEE Sensors Letters, 3(1), 1–4. https://doi.org/10.1109/LSENS.2018.2880195
Adeel, A., Ali, M., Khan, A. N., Khalid, T., Rehman, F., Jararweh, Y., & Shuja, J. (2022). A multi-attack resilient lightweight IoT authentication scheme. Transactions on Emerging Telecommunications Technologies, 33(3), e3676. https://doi.org/10.1002/ett.3676
Amalia, A., Pramitarini, Y., Perdana, R. H. Y., Shim, K., & An, B. (2023). A deep learning-based secure routing protocol to avoid Blackhole attacks in VANETs. Sensors, 23(20), 8224. https://doi.org/10.3390/s23208224
Arappali, N., & Rajendran, G. B. (2021). MANET security routing protocols based on a machine learning technique. Journal of Ambient Intelligence and Humanized Computing, 12(16), 6317–6331. https://doi.org/10.1007/s12652-020-02211-8
Arulkumaran, G., & Gnanamurthy, R. K. (2019). Fuzzy trust approach for detecting Blackhole attack in mobile ad hoc network. Mobile Networks and Applications, 24(2), 386–393. https://doi.org/10.1007/s11036-017-0923-9
Atia, M. R. A., Mokhtar, M., & Khalil, J. (2022). An ANN parametric approach for the estimation of total production operation time. Ain Shams Engineering Journal, 13(2), 101579. https://doi.org/10.1016/j.asej.2021.09.006
Bhatia, A., Kumar, A., Jain, A., Kumar, A., Verma, C., & Illes, Z. (2022). Networked control system with MANET communication and AODV routing. Heliyon, 8(8), e11678. https://doi.org/10.1016/j.heliyon.2022.e11678
Chou, D., & Jiang, M. (2021). A survey on data-driven network intrusion detection. ACM Computing Surveys (CSUR), 54(1), 1–36. https://doi.org/10.1145/3423163
Cirillo, S., Desiato, D., & Breve, B. (2019). CHRAVAT—Chronology awareness visual analytic tool. In Proceedings of the 2019 23rd International Conference Information Visualisation (IV) (pp. 255–260). IEEE. https://doi.org/10.1109/IV.2019.00052
Cirillo, S., Desiato, D., Scalera, M., & Solimando, G. (2023). A visual privacy tool to help users in preserving social network data. In Proceedings of the IS-EUD Workshops. Cagliari, Italy.
Fallah, M., & Nozari, H. (2021). Quantitative analysis of cyber risks in IoT-based supply chain (FMCG industries). Journal of Decisions and Operations Research, 5(4), 510-521.
Fallah, M., Sadeghi, M. E., & Nozari, H. (2021). Quantitative analysis of the applied parts of Internet of Things technology in Iran: an opportunity for economic leapfrogging through technological development. Science and technology policy Letters, 11(4), 45-61.
Goswami, M., Sharma, P., & Bhargava, A. (2020). Black hole attack detection in MANETs using trust-based technique. International Journal of Innovative Technology and Exploring Engineering, 9(4), 1446–1451.
Gurung, S., & Chauhan, S. (2018). A dynamic threshold-based approach for mitigating Blackhole attack in MANET. Wireless Networks, 24(8), 2957–2971. https://doi.org/10.1007/s11276-017-1512-3
Hamamoto, A. H., Carvalho, L. F., Sampaio, L. D. H., Abrão, T., & Proença, M. L. (2018). Network anomaly detection system using genetic algorithm and fuzzy logic. Expert Systems with Applications, 92, 390–402. https://doi.org/10.1016/j.eswa.2017.09.030
Hammamouche, A., Omar, M., Djebari, N., & Tari, A. (2018). Lightweight reputation-based approach against simple and cooperative Blackhole attacks for MANET. Journal of Information Security and Applications, 43, 12–20. https://doi.org/10.1016/j.jisa.2018.10.002
Kalogeras, S., Mejri, S., & Efthimiou, F. (2022). The neuroscience of student engagement: Case studies in narrative pedagogies in mathematics, science, and technology. International Journal of Online Pedagogy and Course Design (IJOPCD), 12(1), 1-19.
Khamayseh, Y. M., Aljawarneh, S. A., & Asaad, A. E. (2018). Ensuring survivability against Blackhole attacks in MANETs for preserving energy efficiency. Sustainable Computing: Informatics and Systems, 18, 90–100. https://doi.org/10.1016/j.suscom.2018.01.008
Khan, A. U., Abbas, G., Abbas, Z. H., Waqas, M., & Hassan, A. K. (2020). Spectrum utilization efficiency in the cognitive radio-enabled 5G-based IoT. Journal of Network and Computer Applications, 164, 102686. https://doi.org/10.1016/j.jnca.2020.102686
Moudni, H., Er-rouidi, M., Mouncif, H., & Hadadi, B. E. (2019). Black hole attack detection using fuzzy-based intrusion detection systems in MANET. Procedia Computer Science, 151, 1176–1181. https://doi.org/10.1016/j.procs.2019.04.168
Movahed, A. B., Aliahmadi, A., Parsanejad, M., & Nozari, H. (2023). A systematic review of collaboration in supply chain 4.0 with meta-synthesis method. Supply Chain Analytics, 4, 100052.
Nozari, H., & Chobar, A. P. (2024). The Dimensions and Components of Marketing 5.0: Introduction to Marketing 6.0. In Advanced Businesses in Industry 6.0 (pp. 75-86). IGI Global.
Nozari, H., & Szmelter-Jarosz, A. (2022). IoT-based supply chain for smart business. ISNet.
Panos, C., Ntantogian, C., Malliaros, S., & Xenakis, C. (2017). Analyzing, quantifying, and detecting the Blackhole attack in infrastructure-less networks. Computer Networks, 113, 94–110. https://doi.org/10.1016/j.comnet.2016.12.012
Pedroso, C., Batista, A. de S., Brisio, S., & Santos, A. (2024). A direct collaborative network intrusion detection system for IoT networks integration. In Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos (SBRC). SBC.
Rani, P., Kavita, Verma, S., Kaur, N., Wozniak, M., Shafi, J., & Ijaz, M. F. (2022). Robust and secure data transmission using artificial intelligence techniques in ad-hoc networks. Sensors, 22(1), 251. https://doi.org/10.3390/s22010251
Riaz, M. K., Yangyu, F., & Akhtar, I. (2019). A multidimensional trust inference model for the mobile Ad-Hoc networks. In 28th Wireless and Optical Communications Conference (WOCC) (pp. 1–5). IEEE. https://doi.org/10.1109/WOCC.2019.8770587
Saba Farheen, N.S., & Jain, A. (2022). Adaptive fuzzy logic inspired path longevity factor-based forecasting model reliable routing in MANETs. Sensors International, 3, 100201. https://doi.org/10.1016/j.sintl.2022.100201
Sajjad, S. M., Mufti, M. R., Yousaf, M., Aslam, W., Alshahrani, R., Nemri, N., Afzal, H., Khan, M. A., & Chen, C. M. (2022). Detection and blockchain-based collaborative mitigation of Internet of Things botnets. Wireless Communications and Mobile Computing, 2022, 1194899. https://doi.org/10.1155/2022/1194899
Sattaru, N. C., Baker, M. R., Umrao, D., Pandey, U. K., Tiwari, M., & Chakravarthi, M. K. (2022). Heart Attack Anxiety Disorder using Machine Learning and Artificial Neural Networks (ANN) Approaches. In 2022 2nd International Conference on Advanced Computing and Innovative Technologies in Engineering (ICACITE) (pp. 680–683). IEEE. https://doi.org/10.1109/ICACITE53722.2022.9823697
Sauer, C., Lyczkowski, E., Schmidt, M., Nüchter, A., & Hoßfeld, T. (2022). Testing AGV mobility control method for MANET coverage optimization using procedural simulation. Computer Communications, 194, 189–201. https://doi.org/10.1016/j.comcom.2022.07.033
Sharifi, S. A., & Babamir, S. M. (2016). A new approach to detecting and preventing the wormhole attacks for secure routing in mobile ad-hoc networks based on the SPR protocol. In 2016 IEEE 10th International Conference on Application of Information and Communication Technologies (AICT) (pp. 1–5). IEEE. https://doi.org/10.1109/ICAICT.2016.7991672
Tiruvakadu, D. S. K., & Pallapa, V. (2018). Honeypot-based Blackhole attack confirmation in a MANET. International Journal of Wireless Information Networks, 25(4), 434–448. https://doi.org/10.1007/s10776-018-0415-2
Venkatasubramanian, S., Suhasini, A., & Hariprasath, S. (2022). Detection of Black and Grey Hole attacks using hybrid cat with PSO-based deep learning algorithm in MANET. International Journal of Computer Networks and Applications (IJCNA), 9(3), 724–735.
Younas, S., Rehman, F., Maqsood, T., Mustafa, S., Akhunzada, A., & Gani, A. (2022). Collaborative detection of Black Hole and Gray Hole attacks for secure data communication in VANETs. Applied Sciences, 12(24), 12448. https://doi.org/10.3390/app122412448
Journal of Industrial and Systems Engineering
Volume 17, Issue 1 - Serial Number 1
Winter 2025
Pages 204-216

  • Receive Date 22 July 2024
  • Revise Date 18 September 2024
  • Accept Date 28 November 2024