Research Article
BibTex RIS Cite

Detection of Sybil Attacks in IoT with Machine Learning Methods

Year 2020, Ejosat Special Issue 2020 (ICCEES), 530 - 536, 05.10.2020
https://doi.org/10.31590/ejosat.838994

Abstract

Internet of Things (IoT) devices are increasing their usage rates with advances in the wireless sensor networks. All IoT devices are connected to themselves with a heterogeneous network. Thus, they are also rather vulnerable to external attacks. Many routing protocol attacks have been described until now and continue to expand and diversify. Therefore, the recommended detection and prevention methods should be updated and improved according to today’s condition. Sybil attack is a kind of the Routing Protocol for Low-Power and Lossy Network (RPL) attacks in IoT. The attack detection based on the signal strength of the nodes in Sybil attacks are one of the most commonly used and recommended approaches. In particular, classical methods that used to detect and prevent attack may not be appropriate for attack detection. The most critical problems in resource constrained IoT systems are energy consumption and heavy computational cost. In this study, packet distribution rates and machine learning approaches such as Naïve Bayes, Random Forest and Logistic Regression have been proposed for the prediction of Sybil attacks on RPL protocol in IoT networks. The Sybil attacks have been detected with 99.51% accuracy rate and this result is higher than classical methods for Sybil attack detection.

References

  • Tahsien, S. M., Karimipour, H., & Spachos, P. (2020). Machine learning based solutions for security of Internet of Things (IoT): A survey, J. Netw. Comput. Appl., vol. 161.
  • Abane, A., Muhlethaler, P., Bouzefrane, S., & Battou, A. (2019). Modeling and Improving Named Data Networking over IEEE 802.15.4. 2019 8th International Conference on Performance Evaluation and Modeling in Wired and Wireless Networks (PEMWN), Paris, France, pp. 1-6, doi: 10.23919/PEMWN47208.2019.8986906.
  • Alaba, F. A., Othman, M., Hashem, I. A. T., & Alotaibi, F. (2017). Internet of Things security: A survey. Journal of Network and Computer Applications.
  • Meghdadi, M., Özdemir, S., & Güler, İ. (2010). Kablosuz Algılayıcı Ağlarında Güvenlik: Sorunlar ve Çözümler. Bilişim Teknol. Derg., vol. 1, no. 1, pp. 35–41.
  • Arış, A., Oktuğ, S., & Yalçın, S. B. Ö. (2015). Nesnelerin Interneti Güvenliği: Servis Engelleme Saldırıları. 23th Signal Processing and Communications Applications Conference (SIU), pp. 1–4.
  • Le, A., Loo, J., Lasebae, A., Vinel, A., Chen, Y., & Chai, M. (2013). The impact of rank attack on network topology of routing protocol for low-power and lossy networks. IEEE Sens. J., vol. 13, no. 10, pp. 3685–3692.
  • Shelby, Z., & Bormann, C. (2011). 6LoWPAN: TheWireless Embedded Internet. vol. 43. New York, NY, USA: Wiley.
  • Hui, J., & Thubert, P. (2011). Compression Format for IPv6 Datagrams over IEEE 802.15.4-Based Networks. RFC 6282 (Proposed Standard), Internet Engineering Task Force.
  • Le, A., Loo, J., Lasebae, A., Vinel, A., Chen, Y., & Chai, M. (2013). The impact of rank attack on network topology of routing protocol for low-power and lossy networks. IEEE Sensors Journal, 13(10), 3685–3692. https://doi.org/10.1109/JSEN.2013.2266399.
  • Kaplantzis, S., Shilton, A., Mani, N., & Sekercioglu, Y. A. (2007). Detecting selective forwarding attacks in wireless sensor networks using support vector machines. In Intelligent Sensors, Sensor Networks and Information, 3rd International Conference on, pages 335–340.
  • Khan, F., Shon, T., Lee, T., & Kim, K. (2013). Wormhole attack prevention mechanism for RPL based LLN network. Fifth International Conference on Ubiquitous and Future Networks (ICUFN), pp. 149–154.
  • Wallgren, S. R. L., & Voigt, T. (2013). Routing Attacks and Countermeasures in the RPL-Based Internet of Things. International Journal of Distributed Sensor Networks, vol. 2013, p. 11.
  • Weekly, K. & Pister, K. (2012). Evaluating sinkhole defense techniques in RPL networks. 20th IEEE International Conference on Network Protocols (ICNP), pp. 1–6.
  • Contiki, (2015). Contiki: The Open Source Operating System for the Internet of Things. http://www.contiki-os.org/, E.T. 19.01.2020.
  • Colina, A. L., Vives, A., Bagula, A., Zennaro, M., & Pietrosemoli, E. (2015). IoT in 5 days.
  • IEEE Standard for Local and Metropolitan Area Networks – Part 15.4: Low Rate Wireless Personal Area Networks, (2011). IEEE Std. 802.15.4-2011.
  • Demir, B., Ayrancıoğlu, G., Gezer, C., & Gözüaçık, N. (2016). 6LoWPAN Kullanan Bir Algılayıcı Ağ Sistemi A Wireless Sensor Network System Using 6LoWPAN. Elektrik-Elektronik ve Biyomedikal Mühendisliği Konferansı (ELECO 2016).
  • Dhamodharan, U. S. R. K., & Vayanaperumal, R. (2015). Detecting and preventing sybil attacks in wireless sensor networks using message authentication and passing method. The Scientific World Journal, 2015:7.
  • Sherasiya, T., & Upadhyay, H. (2016). Intrusion Detection System for Internet of Things. no. 3, pp. 2395–4396.
  • Dhanalakshmi, T. G., Bharathi, N., & Monisha, M. (2014). Safety concerns of Sybil attack in WSN. International Conference on Science Engineering and Management Research, ICSEMR 2014.

Nesnelerin İnternetinde Sahte Kimlik Saldırılarının Makine Öğrenme Yöntemleri ile Tespiti

Year 2020, Ejosat Special Issue 2020 (ICCEES), 530 - 536, 05.10.2020
https://doi.org/10.31590/ejosat.838994

Abstract

Nesnelerin interneti (Internet of Things, IoT) cihazları, kablosuz algılayıcı ağlarında yaşanan gelişmelerle her geçen gün daha fazla kullanım oranına sahip olmaktadır. IoT cihazlarının tümünün birbirine bağlanması ile oluşan heterojen ağ, dışarıdan gelen saldırılara oldukça açıktır. Günümüze kadar birçok yönlendirme protokolü saldırıları ortaya atılmış olup gün geçtikçe saldırılar artmaya ve çeşitlenmeye devam etmektedir. Bununla birlikte, önerilen tespit ve önleme yöntemlerinin de günümüz şartlarına göre iyileştirilmesi ve güncel olması gerekmektedir. Sahte kimlik saldırıları, IoT’ de ağ katmanında kayıplı ağlarda yönlendirme protokolünde (Routing Protocol for Low-Power and Lossy Network, RPL) yer almaktadır. Sahte kimlik saldırıları türünde düğümlerin sinyal gücüne bağlı saldırı tespitleri, en yaygın kullanılan ve önerilen yöntemlerdendir. Kaynak kısıtlı olan IoT cihazlarında, enerji korunumu ve düşük işlem yükü önemli hususların başında gelmektedir. Özellikle saldırı tespitinde kullanılan klasik yöntemler, saldırıların tespiti ve önlenmesinde yetersiz kalabilmektedir. Bu çalışmada, düğümlerin paket dağıtım oranları ve makine öğrenmesi yaklaşımlarından Naive-Bayes, Random Forest ve Lojistik Regresyon ile sahte kimlik saldırılarının tespiti önerilmiştir. Sahte kimlik saldırıları, klasik yöntemlere kıyasla daha yüksek başarım oranı (99.51% doğruluk) ile tespit edilmiştir.

References

  • Tahsien, S. M., Karimipour, H., & Spachos, P. (2020). Machine learning based solutions for security of Internet of Things (IoT): A survey, J. Netw. Comput. Appl., vol. 161.
  • Abane, A., Muhlethaler, P., Bouzefrane, S., & Battou, A. (2019). Modeling and Improving Named Data Networking over IEEE 802.15.4. 2019 8th International Conference on Performance Evaluation and Modeling in Wired and Wireless Networks (PEMWN), Paris, France, pp. 1-6, doi: 10.23919/PEMWN47208.2019.8986906.
  • Alaba, F. A., Othman, M., Hashem, I. A. T., & Alotaibi, F. (2017). Internet of Things security: A survey. Journal of Network and Computer Applications.
  • Meghdadi, M., Özdemir, S., & Güler, İ. (2010). Kablosuz Algılayıcı Ağlarında Güvenlik: Sorunlar ve Çözümler. Bilişim Teknol. Derg., vol. 1, no. 1, pp. 35–41.
  • Arış, A., Oktuğ, S., & Yalçın, S. B. Ö. (2015). Nesnelerin Interneti Güvenliği: Servis Engelleme Saldırıları. 23th Signal Processing and Communications Applications Conference (SIU), pp. 1–4.
  • Le, A., Loo, J., Lasebae, A., Vinel, A., Chen, Y., & Chai, M. (2013). The impact of rank attack on network topology of routing protocol for low-power and lossy networks. IEEE Sens. J., vol. 13, no. 10, pp. 3685–3692.
  • Shelby, Z., & Bormann, C. (2011). 6LoWPAN: TheWireless Embedded Internet. vol. 43. New York, NY, USA: Wiley.
  • Hui, J., & Thubert, P. (2011). Compression Format for IPv6 Datagrams over IEEE 802.15.4-Based Networks. RFC 6282 (Proposed Standard), Internet Engineering Task Force.
  • Le, A., Loo, J., Lasebae, A., Vinel, A., Chen, Y., & Chai, M. (2013). The impact of rank attack on network topology of routing protocol for low-power and lossy networks. IEEE Sensors Journal, 13(10), 3685–3692. https://doi.org/10.1109/JSEN.2013.2266399.
  • Kaplantzis, S., Shilton, A., Mani, N., & Sekercioglu, Y. A. (2007). Detecting selective forwarding attacks in wireless sensor networks using support vector machines. In Intelligent Sensors, Sensor Networks and Information, 3rd International Conference on, pages 335–340.
  • Khan, F., Shon, T., Lee, T., & Kim, K. (2013). Wormhole attack prevention mechanism for RPL based LLN network. Fifth International Conference on Ubiquitous and Future Networks (ICUFN), pp. 149–154.
  • Wallgren, S. R. L., & Voigt, T. (2013). Routing Attacks and Countermeasures in the RPL-Based Internet of Things. International Journal of Distributed Sensor Networks, vol. 2013, p. 11.
  • Weekly, K. & Pister, K. (2012). Evaluating sinkhole defense techniques in RPL networks. 20th IEEE International Conference on Network Protocols (ICNP), pp. 1–6.
  • Contiki, (2015). Contiki: The Open Source Operating System for the Internet of Things. http://www.contiki-os.org/, E.T. 19.01.2020.
  • Colina, A. L., Vives, A., Bagula, A., Zennaro, M., & Pietrosemoli, E. (2015). IoT in 5 days.
  • IEEE Standard for Local and Metropolitan Area Networks – Part 15.4: Low Rate Wireless Personal Area Networks, (2011). IEEE Std. 802.15.4-2011.
  • Demir, B., Ayrancıoğlu, G., Gezer, C., & Gözüaçık, N. (2016). 6LoWPAN Kullanan Bir Algılayıcı Ağ Sistemi A Wireless Sensor Network System Using 6LoWPAN. Elektrik-Elektronik ve Biyomedikal Mühendisliği Konferansı (ELECO 2016).
  • Dhamodharan, U. S. R. K., & Vayanaperumal, R. (2015). Detecting and preventing sybil attacks in wireless sensor networks using message authentication and passing method. The Scientific World Journal, 2015:7.
  • Sherasiya, T., & Upadhyay, H. (2016). Intrusion Detection System for Internet of Things. no. 3, pp. 2395–4396.
  • Dhanalakshmi, T. G., Bharathi, N., & Monisha, M. (2014). Safety concerns of Sybil attack in WSN. International Conference on Science Engineering and Management Research, ICSEMR 2014.
There are 20 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Articles
Authors

Semih Çakır 0000-0003-3072-9532

Nesibe Yalçın 0000-0003-0324-9111

Sinan Toklu 0000-0002-8147-9089

Publication Date October 5, 2020
Published in Issue Year 2020 Ejosat Special Issue 2020 (ICCEES)

Cite

APA Çakır, S., Yalçın, N., & Toklu, S. (2020). Nesnelerin İnternetinde Sahte Kimlik Saldırılarının Makine Öğrenme Yöntemleri ile Tespiti. Avrupa Bilim Ve Teknoloji Dergisi530-536. https://doi.org/10.31590/ejosat.838994

Cited By

Giyilebilir Teknolojiler Üzerine Bir Araştırma
Harran Üniversitesi Mühendislik Dergisi
Ahmet ÇİFTÇİ
https://doi.org/10.46578/humder.903092