Öz
As the number of Internet of Things (IoT) devices increases day by day, attacks against these devices are also increasing. In this study, methods of ensuring security in IoT devices and attacks on IoT devices are discussed, and the importance of zero-trust architecture in ensuring IoT security is explained. In addition, the defense rates of padding methods against machine learning used by the attacker are shown and the defense methods used with machine learning techniques are explained. For this purpose, machine learning methods that are effective on attacks, attacks and violations that are achieved by machine learning techniques are specified. In addition, the effectiveness of machine learning techniques in classifying IoT devices in encrypted traffic is examined. The effectiveness of Random Forest and Decision Tree classification algorithms in classifying IoT devices are evaluated. Finally, experiments are carried out for commonly used attack and defense methods. For this purpose, the accuracy rates of the padded and unpadded experiments are compared by analyzing the IoT device traffic. When classifying unpadded data, 84% accuracy rate of IoT devices is achieved, while this accuracy rate has been reduced to 19% with the random padding method that aims to reduce the attacker's rate of accessing correct information.
Anahtar Kelimeler
Internet of Things Machine Learning Padding Shaping Zero-Trust Architecture Classification
Kaynakça
- Chettri, L., & Bera, R. (2019). A comprehensive survey on Internet of Things (IoT) toward 5G wireless systems. IEEE Internet of Things Journal, 7(1), 16-32.
- Samaniego, M., & Deters, R. (2018, July). Zero-trust hierarchical management in IoT. In 2018 IEEE international congress on Internet of Things (ICIOT) (pp. 88-95). IEEE.
- Lakhani, A. (2019, June 17). Examining Top IoT Security Threats and Attack Vectors. Fortinet. https://www.fortinet.com/blog/industry-trends/examining-top-iot-security-threats-and-attack-vectors
- Pinheiro, A. J., de Araujo-Filho, P. F., Bezerra, J. D. M., & Campelo, D. R. (2020). Adaptive Packet Padding Approach for Smart Home Networks: A Tradeoff Between Privacy and Performance. IEEE Internet of Things Journal, 8(5), 3930-3938.
- Sivanathan, A., Gharakheili, H. H., Loi, F., Radford, A., Wijenayake, C., Vishwanath, A., & Sivaraman, V. (2018). Classifying IoT devices in smart environments using network traffic characteristics. IEEE Transactions on Mobile Computing, 18(8), 1745-1759.
- Apthorpe, N., Huang, D. Y., Reisman, D., Narayanan, A., & Feamster, N. (2019). Keeping the smart home private with smart (er) iot traffic shaping. Proceedings on Privacy Enhancing Technologies, 2019(3), 128-148.
- Xiao, L., Wan, X., Lu, X., Zhang, Y., & Wu, D. (2018). IoT security techniques based on machine learning: How do IoT devices use AI to enhance security?. IEEE Signal Processing Magazine, 35(5), 41-49.
- Mohamed Shakeel, P., Baskar, S., Sarma Dhulipala, V. R., Mishra, S., & Jaber, M. M. (2018). Maintaining security and privacy in health care system using learning based deep-Q-networks. Journal of medical systems, 42(10), 1-10.
- Bout, E., Loscri, V., & Gallais, A. (2021). How Machine Learning changes the nature of cyberattacks on IoT networks: A survey. IEEE Communications Surveys & Tutorials.
- Xiao, L., Li, Y., Han, G., Liu, G., & Zhuang, W. (2016). PHY-layer system using learning spoofing detection with reinforcement learning in wireless networks. IEEE Transactions on Vehicular Technology, 65(12), 10037-10047.
- Xiao, L., Xie, C., Chen, T., Dai, H., & Poor, H. V. (2016). A mobile offloading game against smart attacks. IEEE Access, 4, 2281-2291.
- Narudin, F. A., Feizollah, A., Anuar, N. B., & Gani, A. (2016). Evaluation of machine learning classifiers for mobile malware detection. Soft Computing, 20(1), 343-357.
- Buczak, A. L., & Guven, E. (2015). A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Communications surveys & tutorials, 18(2), 1153-1176.
- Zhang, Z. K., Cho, M. C. Y., Wang, C. W., Hsu, C. W., Chen, C. K., & Shieh, S. (2014, November). IoT security: ongoing challenges and research opportunities. In 2014 IEEE 7th international conference on service-oriented computing and applications (pp. 230-234). IEEE.
- Sivanathan, A., Sherratt, D., Gharakheili, H. H., Radford, A., Wijenayake, C., Vishwanath, A., & Sivaraman, V. (2017, May). Characterizing and classifying IoT traffic in smart cities and campuses. In 2017 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) (pp. 559-564). IEEE.
- Apthorpe, N., Reisman, D., Sundaresan, S., Narayanan, A., & Feamster, N. (2017). Spying on the smart home: Privacy attacks and defenses on encrypted iot traffic. arXiv preprint arXiv:1708.05044.
- Engelberg, A., & Wool, A. (2021). Classification of Encrypted IoT Traffic Despite Padding and Shaping. arXiv preprint arXiv:2110.11188.
- Trimananda, R., Varmarken, J., Markopoulou, A., & Demsky, B. (2020, February). Packet-level signatures for smart home devices. In Network and Distributed Systems Security (NDSS) Symposium (Vol. 2020).
- Ghasemi, M., Saadaat, M., & Ghollasi, O. (2019). Threats of social engineering attacks against security of Internet of Things (IoT). In Fundamental research in electrical engineering (pp. 957-968). Springer, Singapore.
- Yekkehkhany, A., Feng, H., & Lavaei, J. (2021, December). Adversarial Attacks on Computation of the Modified Policy Iteration Method. In 2021 60th IEEE Conference on Decision and Control (CDC) (pp. 49-56). IEEE.
- Sagduyu, Y. E., Shi, Y., & Erpek, T. (2019, June). IoT network security from the perspective of adversarial deep learning. In 2019 16th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON) (pp. 1-9). IEEE.
- Luo, Z., Zhao, S., Lu, Z., Sagduyu, Y. E., & Xu, J. (2020, July). Adversarial machine learning based partial-model attack in IoT. In Proceedings of the 2nd ACM Workshop on Wireless Security and Machine Learning (pp. 13-18).
- Winter, P., Pulls, T., & Fuss, J. (2013, November). ScrambleSuit: A polymorphic network protocol to circumvent censorship. In Proceedings of the 12th ACM workshop on Workshop on privacy in the electronic society (pp. 213-224).
Öz
Nesnelerin İnterneti (IoT) cihazlarının sayısının günden güne artmasıyla birlikte bu cihazlara yönelik yapılan saldırılar da artmaktadır. Bu çalışmada, IoT cihazlarında güvenliği sağlama yöntemleri ve IoT cihazlarına yönelik saldırılar ele alınmış, sıfır-güven mimarisinin IoT güvenliğini sağlamadaki önemi açıklanmıştır. Ayrıca, dolgu yöntemlerinin saldırganın kullandığı makine öğrenmesine karşı savunma oranları gösterilmiş ve makine öğrenmesi teknikleriyle kullanılan savunma yöntemleri anlatılmıştır. Bu amaçla, makine öğrenmesi yöntemlerinin etkili olduğu saldırılar, makine öğrenmesi teknikleriyle yapılan saldırılar ve oluşturulan ihlaller belirtilmiştir. Ek olarak, makine öğrenmesi tekniklerinin şifreli trafikte IoT cihazlarını sınıflandırmadaki etkinliği incelenmiştir. Rastgele Orman ve Karar Ağacı sınıflandırma algoritmalarının IoT cihazlarını sınıflandırmadaki etkinliği değerlendirilmiştir. Son olarak, yaygın kullanılan saldırı ve savunma yöntemleri için deneyler gerçekleştirilmiştir. Bu amaçla, IoT cihaz trafiği analiz edilerek dolgulu ve dolgusuz olarak yapılan deneylerin doğruluk oranları karşılaştırılmıştır. Dolgusuz verilere sınıflandırma yapıldığında IoT cihazlarının %84 doğruluk oranı elde edilirken, saldırganın doğru bilgiye erişme oranını düşürmeyi hedefleyen rastgele dolgu yöntemi ile bu doğruluk oranı %19’a düşürülmüştür.Nesnelerin İnterneti (IoT) cihazlarının sayısının günden güne artmasıyla birlikte bu cihazlara yönelik yapılan saldırılar da artmaktadır. Bu çalışmada, IoT cihazlarında güvenliği sağlama yöntemleri ve IoT cihazlarına yönelik saldırılar ele alınmış, sıfır-güven mimarisinin IoT güvenliğini sağlamadaki önemi açıklanmıştır. Ayrıca, dolgu yöntemlerinin saldırganın kullandığı makine öğrenmesine karşı savunma oranları gösterilmiş ve makine öğrenmesi teknikleriyle kullanılan savunma yöntemleri anlatılmıştır. Bu amaçla, makine öğrenmesi yöntemlerinin etkili olduğu saldırılar, makine öğrenmesi teknikleriyle yapılan saldırılar ve oluşturulan ihlaller belirtilmiştir. Ek olarak, makine öğrenmesi tekniklerinin şifreli trafikte IoT cihazlarını sınıflandırmadaki etkinliği incelenmiştir. Rastgele Orman ve Karar Ağacı sınıflandırma algoritmalarının IoT cihazlarını sınıflandırmadaki etkinliği değerlendirilmiştir. Son olarak, yaygın kullanılan saldırı ve savunma yöntemleri için deneyler gerçekleştirilmiştir. Bu amaçla, IoT cihaz trafiği analiz edilerek dolgulu ve dolgusuz olarak yapılan deneylerin doğruluk oranları karşılaştırılmıştır. Dolgusuz verilere sınıflandırma yapıldığında IoT cihazlarının %84 doğruluk oranı elde edilirken, saldırganın doğru bilgiye erişme oranını düşürmeyi hedefleyen rastgele dolgu yöntemi ile bu doğruluk oranı %19’a düşürülmüştür.
Anahtar Kelimeler
Nesnelerin İnterneti Makine Öğrenmesi Dolgu Şekillendirme Sıfır-Güven Mimarisi Sınıflandırma
Kaynakça
- Chettri, L., & Bera, R. (2019). A comprehensive survey on Internet of Things (IoT) toward 5G wireless systems. IEEE Internet of Things Journal, 7(1), 16-32.
- Samaniego, M., & Deters, R. (2018, July). Zero-trust hierarchical management in IoT. In 2018 IEEE international congress on Internet of Things (ICIOT) (pp. 88-95). IEEE.
- Lakhani, A. (2019, June 17). Examining Top IoT Security Threats and Attack Vectors. Fortinet. https://www.fortinet.com/blog/industry-trends/examining-top-iot-security-threats-and-attack-vectors
- Pinheiro, A. J., de Araujo-Filho, P. F., Bezerra, J. D. M., & Campelo, D. R. (2020). Adaptive Packet Padding Approach for Smart Home Networks: A Tradeoff Between Privacy and Performance. IEEE Internet of Things Journal, 8(5), 3930-3938.
- Sivanathan, A., Gharakheili, H. H., Loi, F., Radford, A., Wijenayake, C., Vishwanath, A., & Sivaraman, V. (2018). Classifying IoT devices in smart environments using network traffic characteristics. IEEE Transactions on Mobile Computing, 18(8), 1745-1759.
- Apthorpe, N., Huang, D. Y., Reisman, D., Narayanan, A., & Feamster, N. (2019). Keeping the smart home private with smart (er) iot traffic shaping. Proceedings on Privacy Enhancing Technologies, 2019(3), 128-148.
- Xiao, L., Wan, X., Lu, X., Zhang, Y., & Wu, D. (2018). IoT security techniques based on machine learning: How do IoT devices use AI to enhance security?. IEEE Signal Processing Magazine, 35(5), 41-49.
- Mohamed Shakeel, P., Baskar, S., Sarma Dhulipala, V. R., Mishra, S., & Jaber, M. M. (2018). Maintaining security and privacy in health care system using learning based deep-Q-networks. Journal of medical systems, 42(10), 1-10.
- Bout, E., Loscri, V., & Gallais, A. (2021). How Machine Learning changes the nature of cyberattacks on IoT networks: A survey. IEEE Communications Surveys & Tutorials.
- Xiao, L., Li, Y., Han, G., Liu, G., & Zhuang, W. (2016). PHY-layer system using learning spoofing detection with reinforcement learning in wireless networks. IEEE Transactions on Vehicular Technology, 65(12), 10037-10047.
- Xiao, L., Xie, C., Chen, T., Dai, H., & Poor, H. V. (2016). A mobile offloading game against smart attacks. IEEE Access, 4, 2281-2291.
- Narudin, F. A., Feizollah, A., Anuar, N. B., & Gani, A. (2016). Evaluation of machine learning classifiers for mobile malware detection. Soft Computing, 20(1), 343-357.
- Buczak, A. L., & Guven, E. (2015). A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Communications surveys & tutorials, 18(2), 1153-1176.
- Zhang, Z. K., Cho, M. C. Y., Wang, C. W., Hsu, C. W., Chen, C. K., & Shieh, S. (2014, November). IoT security: ongoing challenges and research opportunities. In 2014 IEEE 7th international conference on service-oriented computing and applications (pp. 230-234). IEEE.
- Sivanathan, A., Sherratt, D., Gharakheili, H. H., Radford, A., Wijenayake, C., Vishwanath, A., & Sivaraman, V. (2017, May). Characterizing and classifying IoT traffic in smart cities and campuses. In 2017 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) (pp. 559-564). IEEE.
- Apthorpe, N., Reisman, D., Sundaresan, S., Narayanan, A., & Feamster, N. (2017). Spying on the smart home: Privacy attacks and defenses on encrypted iot traffic. arXiv preprint arXiv:1708.05044.
- Engelberg, A., & Wool, A. (2021). Classification of Encrypted IoT Traffic Despite Padding and Shaping. arXiv preprint arXiv:2110.11188.
- Trimananda, R., Varmarken, J., Markopoulou, A., & Demsky, B. (2020, February). Packet-level signatures for smart home devices. In Network and Distributed Systems Security (NDSS) Symposium (Vol. 2020).
- Ghasemi, M., Saadaat, M., & Ghollasi, O. (2019). Threats of social engineering attacks against security of Internet of Things (IoT). In Fundamental research in electrical engineering (pp. 957-968). Springer, Singapore.
- Yekkehkhany, A., Feng, H., & Lavaei, J. (2021, December). Adversarial Attacks on Computation of the Modified Policy Iteration Method. In 2021 60th IEEE Conference on Decision and Control (CDC) (pp. 49-56). IEEE.
- Sagduyu, Y. E., Shi, Y., & Erpek, T. (2019, June). IoT network security from the perspective of adversarial deep learning. In 2019 16th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON) (pp. 1-9). IEEE.
- Luo, Z., Zhao, S., Lu, Z., Sagduyu, Y. E., & Xu, J. (2020, July). Adversarial machine learning based partial-model attack in IoT. In Proceedings of the 2nd ACM Workshop on Wireless Security and Machine Learning (pp. 13-18).
- Winter, P., Pulls, T., & Fuss, J. (2013, November). ScrambleSuit: A polymorphic network protocol to circumvent censorship. In Proceedings of the 12th ACM workshop on Workshop on privacy in the electronic society (pp. 213-224).
Ayrıntılar
| Birincil Dil | Türkçe |
|---|---|
| Konular | Mühendislik |
| Bölüm | Makaleler |
| Yazarlar | |
| Yayımlanma Tarihi | 20 Temmuz 2022 |
| Gönderilme Tarihi | 12 Haziran 2022 |
| Yayımlandığı Sayı | Yıl 2022 Cilt: 6 Sayı: 1 |
