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KABLOSUZ SENSÖR AĞLARDA SYBIL SALDIRISININ AYRINTILI ANALİZİ

Year 2023, Volume: 7 Issue: 1, 41 - 54, 29.06.2023

Abstract

Kablosuz algılayıcı (sensör) ağlar genellikle korumasız, kullanılamayan veya olumsuz koşullarda çalışmaktadır. Bu nedenle kablosuz algılayıcı ağların güvenliği büyük önem taşımaktadır. Kablosuz sensör ağları üzerinde en popüler saldırılardan bazıları DDOS saldırısı, Sybil saldırısı, Seçici Yönlendirme saldırısı, Wormhole saldırısı ve Blackhole saldırısıdır. Kablosuz sensör ağlarında en etkili saldırı potansiyeli olabilen Sybil saldırısının literatürde birçok tanımı bulunmakla birlikte çoğu çalışma Sybil saldırısını detaylı olarak açıklamamaktadır. Sybil saldırısının simülasyon ortamında uygulanması hakkında detaylı bilgi verilmemektedir. Bir Sybil saldırısında, kötü amaçlı düğüm kendisini komşu düğümlere rastgele oluşturulmuş veya çalınmış birçok kimlikle birlikte göstermektedir. Hiçbir şeyden habersiz olan kurban düğüm, kötü niyetli düğümden gelen paketi farklı bir kimliğe sahip başka bir düğümden gelmiş gibi algılar. Bu şekilde ağa sahte paketler göndererek ağ trafiğini olumsuz etkileyebilir ve düğümlerin paket alışverişi yapamamasına neden olabilir. Diğer etkilerde, sahte kimlikler tarafından üretilen sahte paketler temel düğümde toplanır ve ağdaki doğru bilgiler yerine sahte bilgilerle ağın sürekliliği ve kararlılığı tehlikeye atılabilir. Bu çalışmada, kablosuz sensör ağlarında tehlikeli bir saldırı olan Sybil saldırısı detaylı bir şekilde anlatılmış ve NS2 simülasyon ortamında adım adım bir Sybil saldırısı gerçekleştirilmiştir. Ayrıca NS2 simülasyon ortamında oluşturulan 9 farklı senaryonun uygulaması ve Sybil saldırısının sisteme etkileri analiz edilmiştir. Her senaryo, farklı konum ve sayıda Sybil ve replikasyon düğümleri ile hazırlanmıştır. Bu sayede birçok durumda Sybil saldırısının sistem üzerindeki etkileri gözlemlenmiştir. NS2 tarafından elde edilen tüm veriler analiz için kullanılmıştır. Veriler sonucunda paket teslim hızı, verim, normalleştirilmiş iletim yükü ve uçtan uca gecikme değerleri karşılaştırılmıştır.

References

  • Almesaeed, R., & Al-Salem, E. (2022). Sybil attack detection scheme based on channel profile and power regulations in wireless sensor networks. Wireless Networks, 28(4), 1361-1374. https://doi.org/10.1007/s11276-021-02871-0
  • Angappan, A., Saravanabava, T. P., Sakthivel, P., & Vishvaksenan, K. S. (2020). Novel sybil attack detection using rssi and neighbour information to ensure secure communication in wsn. Journal of Ambient Intelligence and Humanized Computing, 12(6), 6567-6578. https://doi.org/10.1007/s12652-020-02276-5
  • Ardakani, M. M., Tabarzad, M. A., & Shayegan, M. A. (2022). Detecting sybil attacks in vehicular ad hoc networks using fuzzy logic and arithmetic optimization algorithm. Journal of Supercomputing, 78(14), 16303-16335. https://doi.org/10.1007/s11227-022-04526-z
  • Avila, K., Sanmartin, P., Jabba, D., & Gomez, J. (2021). An analytical survey of attack scenario parameters on the techniques of attack mitigation in wsn. Wireless Personal Communications, 122(4), 3687-3718. https://doi.org/10.1007/s11277-021-09107-6
  • Biswas, R. N., Mitra, S. K., & Naskar, M. K. (2022). Localization under node capture attacks using fuzzy based anchor mobility control. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-021-03619-6
  • Ceyhan, E. B. & Sağıroğlu, Ş. (2013). Kablosuz algılayıcı ağlarda güvenlik sorunları ve alınabilecek önlemler. Politeknik Dergisi , 16, (4), 155-163. https://dergipark.org.tr/tr/pub/politeknik/issue/33068/367995
  • Chen, S., Pang, Z., Wen, H., Yu, K., Zhang, T., & Lu, Y. (2021). Automated labeling and learning for physical layer authentication against clone node and sybil attacks in industrial wireless edge networks. IEEE Transactions on Industrial Informatics, 17(3), 2041-2051. https://doi.org/10.1109/tii.2020.2963962
  • Ezhilarasi, M., Gnanaprasanambikai, L., Kousalya, A., & Shanmugapriya, M. (2022). A novel implementation of routing attack detection scheme by using fuzzy and feed-forward neural networks. Soft Computing. https://doi.org/10.1007/s00500-022-06915-1
  • Ibrahim, I. S., King, P. J. B., & Loidl, H. W. (2015). Nsgtfa: A gui tool to easily measure network performance through the ns2 trace file. Journal of Intelligent Systems, 24(4), 467-477. https://doi.org/10.1515/jisys-2014-0153
  • Jamshidi, M., Esnaashari, M., Darwesh, A. M., & Meybodi, M. R. (2019). Detecting sybil nodes in stationary wireless sensor networks using learning automaton and client puzzles. IET Communications, 13(13), 1988-1997. https://doi.org/10.1049/iet-com.2018.6036
  • Karakaya, A., & Akleylek, S. (2018). A survey on security threats and authentication approaches in wireless sensor networks. In 2018 6th International Symposium on Digital Forensic and Security (ISDFS) (pp. 1-6). IEEE. https://doi.org/10.1109/ISDFS.2018.8355381
  • Mehbodniya, A., Webber, J. L., Shabaz, M., Mohafez, H., & Yadav, K. (2021). Machine learning technique to detect sybil attack on iot based sensor network. IETE Journal of Research, 1-9. https://doi.org/10.1080/03772063.2021.2000509
  • Pushpa, X. S., & Raja, S. K. S. (2022). Enhanced ecc based authentication protocol in wireless sensor network with dos mitigation. Cybernetics and Systems, 53(8), 734-755. https://doi.org/10.1080/01969722.2022.2055403
  • Sadeghizadeh, M. (2022). A lightweight intrusion detection system based on rssi for sybil attack detection in wireless sensor networks. International Journal of Nonlinear Analysis and Applications, 13(1), 305-320. https://doi.org/10.22075/ijnaa.2022.5491
  • Shehni, R. A., Faez, K., Eshghi, F., & Kelarestaghi, M. (2018). A new lightweight watchdog-based algorithm for detecting sybil nodes in mobile wsns. Future Internet, 10(1), 1. https://doi.org/10.3390/fi10010001
  • Shehnaz, T. P., & Nital, H. M. (2017). A review: Sybil attack detection techniques in wsn. In 2017 4th International Conference on Electronics and Communication Systems (ICECS) (pp. 1116-1121). IEEE. https://doi.org/10.1109/ecs.2017.8067865
  • Singh, S., & Saini, H. S. (2018). Security approaches for data aggregation in wireless sensor networks against sybil attack. In The 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT) (pp. 656-661). IEEE. https://doi.org/10.1109/icicct.2018.8473091
  • Vamsi, P. R., & Kant, K. (2014). A lightweight sybil attack detection framework for wireless sensor networks. In 2014 Seventh International Conference on Contemporary Computing (IC3) (pp. 293-298). IEEE. https://doi.org/10.1109/IC3.2014.6897205
  • Wang, H., Ma, L., & Bai, H. (2020). A three-tier scheme for sybil attack detection in wireless sensor networks. In 2020 5th International Conference on Computer and Communication Systems (ICCCS 2020) (pp. 54-58). IEEE. https://doi.org/10.1109/ICCCS49078.2020.9118478
  • Wadii, J., Rim, H., & Ridha, B. (2019). Detecting and preventing sybil attacks in wireless sensor networks. In 2019 IEEE 19th Mediterranean Microwave Symposium (MMS) (pp. 1-4). IEEE. https://doi.org/10.1109/MMS48040.2019.9157321
  • Zhang, J., Sun, J., & Zhang, C. (2022). Stochastic game in linear quadratic gaussian control for wireless networked control systems under dos attacks. IEEE Transactions on Systems Man Cybernetics-Systems, 52(2), 902-910. https://doi.org/10.1109/tsmc.2020.3010515
  • Zhukabayeva, T. K., Mardenov, E. M., & Abdildaeva, A. A. (2020). Sybil attack detection in wireless sensor networks. In 2020 IEEE 14th International Conference on Application of Information and Communication Technologies (AICT2020) (pp. 1-6). IEEE. https://doi.org/10.1109/AICT50176.2020.9368790

DETAILED ANALYSIS OF SYBIL ATTACK IN WIRELESS SENSOR NETWORKS

Year 2023, Volume: 7 Issue: 1, 41 - 54, 29.06.2023

Abstract

Wireless sensor networks often operate in unprotected, unavailable, or adverse conditions. Therefore, the security of wireless sensor networks is of great importance. Some of the most popular attacks among wireless sensor networks are DDOS attack, Sybil attack, Selective Routing attack, Wormhole attack and Blackhole attack. In the literature, there are many definitions of Sybil attack, which can be the most effective attack potential in wireless sensor networks, but most studies do not describe the Sybil attack in detail. They do not give detailed information about the implementation of the Sybil attack in the simulation environment. In a Sybil attack, the malicious node presents itself to neighboring nodes, along with many randomly generated or stolen identities. Unaware of anything, the victim node perceives the packet from the malicious node as if it came from another node with a different identity. By sending fake packets to the network in this way, it can negatively affect network traffic and cause nodes to be unable to exchange packets. In other effects, bogus packets generated by fake identities are collected at the base node, and the continuity and stability of the network can be compromised with phony information instead of accurate information on the network. In this study, the Sybil attack, a dangerous attack in wireless sensor networks, is explained in detail, and a step-by-step Sybil attack is carried out in the NS2 simulation environment. In addition, the application of 9 different scenarios created in the NS2 simulation environment and the effects of the Sybil attack on the system were analyzed. Each scenario was prepared with a different location and number of Sybil and replication nodes. In this way, the effects of the Sybil attack on the system have been observed in many cases. All data obtained by NS2 was used for analysis. As a result of the data, packet delivery speed, throughput, normalized forwarding load and end-to-end latency values were compared.

References

  • Almesaeed, R., & Al-Salem, E. (2022). Sybil attack detection scheme based on channel profile and power regulations in wireless sensor networks. Wireless Networks, 28(4), 1361-1374. https://doi.org/10.1007/s11276-021-02871-0
  • Angappan, A., Saravanabava, T. P., Sakthivel, P., & Vishvaksenan, K. S. (2020). Novel sybil attack detection using rssi and neighbour information to ensure secure communication in wsn. Journal of Ambient Intelligence and Humanized Computing, 12(6), 6567-6578. https://doi.org/10.1007/s12652-020-02276-5
  • Ardakani, M. M., Tabarzad, M. A., & Shayegan, M. A. (2022). Detecting sybil attacks in vehicular ad hoc networks using fuzzy logic and arithmetic optimization algorithm. Journal of Supercomputing, 78(14), 16303-16335. https://doi.org/10.1007/s11227-022-04526-z
  • Avila, K., Sanmartin, P., Jabba, D., & Gomez, J. (2021). An analytical survey of attack scenario parameters on the techniques of attack mitigation in wsn. Wireless Personal Communications, 122(4), 3687-3718. https://doi.org/10.1007/s11277-021-09107-6
  • Biswas, R. N., Mitra, S. K., & Naskar, M. K. (2022). Localization under node capture attacks using fuzzy based anchor mobility control. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-021-03619-6
  • Ceyhan, E. B. & Sağıroğlu, Ş. (2013). Kablosuz algılayıcı ağlarda güvenlik sorunları ve alınabilecek önlemler. Politeknik Dergisi , 16, (4), 155-163. https://dergipark.org.tr/tr/pub/politeknik/issue/33068/367995
  • Chen, S., Pang, Z., Wen, H., Yu, K., Zhang, T., & Lu, Y. (2021). Automated labeling and learning for physical layer authentication against clone node and sybil attacks in industrial wireless edge networks. IEEE Transactions on Industrial Informatics, 17(3), 2041-2051. https://doi.org/10.1109/tii.2020.2963962
  • Ezhilarasi, M., Gnanaprasanambikai, L., Kousalya, A., & Shanmugapriya, M. (2022). A novel implementation of routing attack detection scheme by using fuzzy and feed-forward neural networks. Soft Computing. https://doi.org/10.1007/s00500-022-06915-1
  • Ibrahim, I. S., King, P. J. B., & Loidl, H. W. (2015). Nsgtfa: A gui tool to easily measure network performance through the ns2 trace file. Journal of Intelligent Systems, 24(4), 467-477. https://doi.org/10.1515/jisys-2014-0153
  • Jamshidi, M., Esnaashari, M., Darwesh, A. M., & Meybodi, M. R. (2019). Detecting sybil nodes in stationary wireless sensor networks using learning automaton and client puzzles. IET Communications, 13(13), 1988-1997. https://doi.org/10.1049/iet-com.2018.6036
  • Karakaya, A., & Akleylek, S. (2018). A survey on security threats and authentication approaches in wireless sensor networks. In 2018 6th International Symposium on Digital Forensic and Security (ISDFS) (pp. 1-6). IEEE. https://doi.org/10.1109/ISDFS.2018.8355381
  • Mehbodniya, A., Webber, J. L., Shabaz, M., Mohafez, H., & Yadav, K. (2021). Machine learning technique to detect sybil attack on iot based sensor network. IETE Journal of Research, 1-9. https://doi.org/10.1080/03772063.2021.2000509
  • Pushpa, X. S., & Raja, S. K. S. (2022). Enhanced ecc based authentication protocol in wireless sensor network with dos mitigation. Cybernetics and Systems, 53(8), 734-755. https://doi.org/10.1080/01969722.2022.2055403
  • Sadeghizadeh, M. (2022). A lightweight intrusion detection system based on rssi for sybil attack detection in wireless sensor networks. International Journal of Nonlinear Analysis and Applications, 13(1), 305-320. https://doi.org/10.22075/ijnaa.2022.5491
  • Shehni, R. A., Faez, K., Eshghi, F., & Kelarestaghi, M. (2018). A new lightweight watchdog-based algorithm for detecting sybil nodes in mobile wsns. Future Internet, 10(1), 1. https://doi.org/10.3390/fi10010001
  • Shehnaz, T. P., & Nital, H. M. (2017). A review: Sybil attack detection techniques in wsn. In 2017 4th International Conference on Electronics and Communication Systems (ICECS) (pp. 1116-1121). IEEE. https://doi.org/10.1109/ecs.2017.8067865
  • Singh, S., & Saini, H. S. (2018). Security approaches for data aggregation in wireless sensor networks against sybil attack. In The 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT) (pp. 656-661). IEEE. https://doi.org/10.1109/icicct.2018.8473091
  • Vamsi, P. R., & Kant, K. (2014). A lightweight sybil attack detection framework for wireless sensor networks. In 2014 Seventh International Conference on Contemporary Computing (IC3) (pp. 293-298). IEEE. https://doi.org/10.1109/IC3.2014.6897205
  • Wang, H., Ma, L., & Bai, H. (2020). A three-tier scheme for sybil attack detection in wireless sensor networks. In 2020 5th International Conference on Computer and Communication Systems (ICCCS 2020) (pp. 54-58). IEEE. https://doi.org/10.1109/ICCCS49078.2020.9118478
  • Wadii, J., Rim, H., & Ridha, B. (2019). Detecting and preventing sybil attacks in wireless sensor networks. In 2019 IEEE 19th Mediterranean Microwave Symposium (MMS) (pp. 1-4). IEEE. https://doi.org/10.1109/MMS48040.2019.9157321
  • Zhang, J., Sun, J., & Zhang, C. (2022). Stochastic game in linear quadratic gaussian control for wireless networked control systems under dos attacks. IEEE Transactions on Systems Man Cybernetics-Systems, 52(2), 902-910. https://doi.org/10.1109/tsmc.2020.3010515
  • Zhukabayeva, T. K., Mardenov, E. M., & Abdildaeva, A. A. (2020). Sybil attack detection in wireless sensor networks. In 2020 IEEE 14th International Conference on Application of Information and Communication Technologies (AICT2020) (pp. 1-6). IEEE. https://doi.org/10.1109/AICT50176.2020.9368790
There are 22 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Articles
Authors

Abdullah Orman 0000-0002-3495-1897

Yunus Üstün 0000-0002-1277-6274

Murat Dener 0000-0001-5746-6141

Publication Date June 29, 2023
Published in Issue Year 2023 Volume: 7 Issue: 1

Cite

APA Orman, A., Üstün, Y., & Dener, M. (2023). DETAILED ANALYSIS OF SYBIL ATTACK IN WIRELESS SENSOR NETWORKS. Uluslararası Sürdürülebilir Mühendislik Ve Teknoloji Dergisi, 7(1), 41-54.
AMA Orman A, Üstün Y, Dener M. DETAILED ANALYSIS OF SYBIL ATTACK IN WIRELESS SENSOR NETWORKS. Uluslararası Sürdürülebilir Mühendislik ve Teknoloji Dergisi. June 2023;7(1):41-54.
Chicago Orman, Abdullah, Yunus Üstün, and Murat Dener. “DETAILED ANALYSIS OF SYBIL ATTACK IN WIRELESS SENSOR NETWORKS”. Uluslararası Sürdürülebilir Mühendislik Ve Teknoloji Dergisi 7, no. 1 (June 2023): 41-54.
EndNote Orman A, Üstün Y, Dener M (June 1, 2023) DETAILED ANALYSIS OF SYBIL ATTACK IN WIRELESS SENSOR NETWORKS. Uluslararası Sürdürülebilir Mühendislik ve Teknoloji Dergisi 7 1 41–54.
IEEE A. Orman, Y. Üstün, and M. Dener, “DETAILED ANALYSIS OF SYBIL ATTACK IN WIRELESS SENSOR NETWORKS”, Uluslararası Sürdürülebilir Mühendislik ve Teknoloji Dergisi, vol. 7, no. 1, pp. 41–54, 2023.
ISNAD Orman, Abdullah et al. “DETAILED ANALYSIS OF SYBIL ATTACK IN WIRELESS SENSOR NETWORKS”. Uluslararası Sürdürülebilir Mühendislik ve Teknoloji Dergisi 7/1 (June 2023), 41-54.
JAMA Orman A, Üstün Y, Dener M. DETAILED ANALYSIS OF SYBIL ATTACK IN WIRELESS SENSOR NETWORKS. Uluslararası Sürdürülebilir Mühendislik ve Teknoloji Dergisi. 2023;7:41–54.
MLA Orman, Abdullah et al. “DETAILED ANALYSIS OF SYBIL ATTACK IN WIRELESS SENSOR NETWORKS”. Uluslararası Sürdürülebilir Mühendislik Ve Teknoloji Dergisi, vol. 7, no. 1, 2023, pp. 41-54.
Vancouver Orman A, Üstün Y, Dener M. DETAILED ANALYSIS OF SYBIL ATTACK IN WIRELESS SENSOR NETWORKS. Uluslararası Sürdürülebilir Mühendislik ve Teknoloji Dergisi. 2023;7(1):41-54.