Research Article
BibTex RIS Cite

Decision Tree Based Intrusion Detection Method in the Internet of Things

Year 2022, Volume: 6 Issue: 1, 17 - 23, 28.06.2022
https://doi.org/10.46460/ijiea.970383

Abstract

Developments in computer and network technologies have also positively affected internet technology. With the development of the Internet, the concept of IoT (Internet of Things) has been invented. Nowadays, IoT devices provide convenience in many areas, and the positive effects of IoT-based systems increase people's quality of life. People want to remotely monitor and manage smart cities, smart homes, and other platforms. However, IoT systems have many vulnerabilities and thus have become the target of attackers. Detecting such attacks and preventing security vulnerabilities will further increase the rate of use of IoT technology. In this work, an intelligent intrusion detection system (IDS) for IoT devices has been suggested. The presented intelligent IDS for IoT devices have been developed on a big attack dataset and this dataset contains 3,668,443 observations. In prior works which used this dataset, researchers worked on a binary classification problem (attacked and normal). However, this research aims to classify the attack types, hence, nine categories have been used. In order to propose a prompt responded IDS model, a fast classifier which is a decision tree (DT) has been employed. Our proposal attained 97.43% classification accuracy on this dataset using 10-fold cross-validation. This accuracy rate frankly demonstrates the classification ability of our proposed IDS model for IoT devices.

References

  • F. Ertam, I. F. Kilincer, O. Yaman, and A. Sengur, “A New IoT Application for Dynamic WiFi based Wireless Sensor Network,” 2020 Int. Conf. Electr. Eng. ICEE 2020, pp. 6–9, 2020, doi: 10.1109/ICEE49691.2020.9249771.
  • M. Hasan, M. M. Islam, M. I. I. Zarif, and M. M. A. Hashem, “Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches,” Internet of Things, vol. 7, p. 100059, Sep. 2019, doi: 10.1016/j.iot.2019.100059.
  • S. D. Okegbile and O. I. Ogunranti, “Users emulation attack management in the massive internet of things enabled environment,” ICT Express, vol. 6, no. 4, pp. 353–356, Dec. 2020, doi: 10.1016/j.icte.2020.06.005.
  • J. Ashraf et al., “IoTBoT-IDS: A novel statistical learning-enabled botnet detection framework for protecting networks of smart cities,” Sustain. Cities Soc., vol. 72, no. May, p. 103041, 2021, doi: 10.1016/j.scs.2021.103041.
  • “IoT Anaytics,” IoT Analytics - Market insights for the Internet of Things. https://iot-analytics.com/ (accessed Jun. 02, 2021).
  • K. Gupta and S. Shukla, “Internet of Things: Security challenges for next generation networks,” in 2016 1st International Conference on Innovation and Challenges in Cyber Security, ICICCS 2016, Aug. 2016, pp. 315–318, doi: 10.1109/ICICCS.2016.7542301.
  • P. Kumar, A. Braeken, A. Gurtov, J. Iinatti, and P. H. Ha, “Anonymous Secure Framework in Connected Smart Home Environments,” IEEE Trans. Inf. Forensics Secur., vol. 12, no. 4, pp. 968–979, Apr. 2017, doi: 10.1109/TIFS.2016.2647225.
  • E. Deniz, “Nesnelerin İnternetinde Gizlilik Ve Güvenlik Yönetimi, Yüksek Lisans Tezi, Ankara Üniversitesi,” Ankara, 2019.
  • G. D’Angelo, F. Palmieri, M. Ficco, and S. Rampone, “An uncertainty-managing batch relevance-based approach to network anomaly detection,” Appl. Soft Comput. J., vol. 36, pp. 408–418, 2015, doi: 10.1016/j.asoc.2015.07.029.
  • M. Ring, S. Wunderlich, D. Scheuring, D. Landes, and A. Hotho, “A survey of network-based intrusion detection data sets,” Comput. Secur., vol. 86, pp. 147–167, 2019, doi: 10.1016/j.cose.2019.06.005.
  • M. Shafiq, Z. Tian, A. K. Bashir, X. Du, and M. Guizani, “IoT malicious traffic identification using wrapper-based feature selection mechanisms,” Comput. Secur., vol. 94, p. 101863, Jul. 2020, doi: 10.1016/j.cose.2020.101863.
  • M. Mohammadi, A. Al-Fuqaha, S. Sorour, and M. Guizani, “Deep learning for IoT big data and streaming analytics: A survey,” IEEE Communications Surveys and Tutorials, vol. 20, no. 4. Institute of Electrical and Electronics Engineers Inc., pp. 2923–2960, Oct. 2018, doi: 10.1109/COMST.2018.2844341.
  • E. Yönem, “Nesnelerin Internetinde Veri Analizi İçin Tekrarlayıcı Sinir Ağları Yönetiminin Yapay Arı Koloni Algoritması İle Eğitilmesi, Erciyes Üniversitesi, Yüksek Lisans Tezi,” p. 124, 2019.
  • S. Rathore and J. H. Park, “Semi-supervised learning based distributed attack detection framework for IoT,” Appl. Soft Comput. J., vol. 72, pp. 79–89, 2018, doi: 10.1016/j.asoc.2018.05.049.
  • L. Xiao, X. Wan, X. Lu, Y. Zhang, and D. Wu, “IoT Security Techniques Based on Machine Learning,” IEEE Signal Process. Mag., vol. 35, no. 5, pp. 41–49, 2018, doi: 10.1109/MSP.2018.2825478.
  • I. Kotenko, I. Saenko, A. Kushnerevich, and A. Branitskiy, “Attack Detection in IoT Critical Infrastructures: A Machine Learning and Big Data Processing Approach,” Proc. - 27th Euromicro Int. Conf. Parallel, Distrib. Network-Based Process. PDP 2019, pp. 340–347, 2019, doi: 10.1109/EMPDP.2019.8671571.
  • L. Vu, Q. U. Nguyen, D. N. Nguyen, D. T. Hoang, and E. Dutkiewicz, “Deep Transfer Learning for IoT Attack Detection,” IEEE Access, vol. 8, pp. 107335–107344, 2020, doi: 10.1109/ACCESS.2020.3000476.
  • Q. Zhang, H. Zhong, W. Shi, and L. Liu, “A trusted and collaborative framework for deep learning in IoT,” Comput. Networks, vol. 193, p. 108055, Jul. 2021, doi: 10.1016/j.comnet.2021.108055.
  • C. Zhang and R. Green, “Communication security in internet of thing: Preventive measure and avoid DDoS attack over IoT network,” Simul. Ser., vol. 47, no. 3, pp. 8–15, 2015.
  • F. Y. Yavuz, “Deep Learning in Cyber Security for Internet of Things, Yüksek Lisans Tezi, Istanbul City University,” 2018.
  • E. M. Irmak, “Makine Ögrenmesi Regresyon Yöntemlerinin Nesnelerin İnterneti Verilerine Uygulanması, Yüksek Lisans Tezi, Harran Üniversitesi,” p. 75, 2019.
  • T. A. Gürkan, “Security Analysis of Coap and Dtls Protocols for Internet of Things Applications, Master of Science, Işık University,” p. 53, 2019.
  • M. Erhan, “It Security And Privacy Guidance Tool For IoT Designs And Products, Master Of Science, The Middle East Technical University,” vol. 8, no. 5, p. 127, 2019.
  • N. Koroniotis, N. Moustafa, E. Sitnikova, and B. Turnbull, “Towards the development of realistic botnet dataset in the Internet of Things for network forensic analytics: Bot-IoT dataset,” Futur. Gener. Comput. Syst., vol. 100, pp. 779–796, Nov. 2019, doi: 10.1016/j.future.2019.05.041.
  • N. Koroniotis and N. Moustafa, “The Bot-IoT Dataset,” UNSW Canberra at ADFA. https://research.unsw.edu.au/projects/bot-iot-dataset (accessed Jun. 02, 2021).
  • N. Koroniotis, “Designing an effective network forensic framework for the investigation of botnets in the Internet of Things,” no. March, 2020.
  • O. Yaman, H. Yetis, and M. Karakose, “Decision Tree Based Customer Analysis Method for Energy Planning in Smart Cities,” 2020, doi: 10.1109/ICDABI51230.2020.9325644.

Nesnelerin İnternetinde Karar Ağacı Tabanlı Saldırı Tespit Yöntemi

Year 2022, Volume: 6 Issue: 1, 17 - 23, 28.06.2022
https://doi.org/10.46460/ijiea.970383

Abstract

Bilgisayar ve ağ teknolojilerindeki gelişmeler internet teknolojisini de olumlu yönde etkilemiştir. İnternetin gelişmesiyle birlikte IoT (Nesnelerin İnterneti) kavramı ortaya çıkmıştır. Günümüzde IoT cihazları birçok alanda kolaylık sağlamakta ve IoT tabanlı sistemlerin olumlu etkileri insanların yaşam kalitesini artırmaktadır. İnsanlar akıllı şehirleri, akıllı evleri ve diğer platformları uzaktan izlemek ve yönetmek istemektedir. Ancak IoT sistemleri birçok güvenlik açığına sahiptir ve bu nedenle saldırganların hedefi haline gelmiştir. Bu tür saldırıları tespit etmek ve güvenlik açıklarını önlemek, IoT teknolojisinin kullanım oranını daha da artıracaktır. Bu çalışmada, IoT cihazları için akıllı bir saldırı tespit sistemi (IDS) önerilmiştir. IoT cihazları için sunulan akıllı IDS, büyük bir saldırı veriseti üzerinde geliştirildi ve bu veriseti 3.668.443 örnek içermektedir. Bu veri setini kullanan önceki çalışmalarda, araştırmacılar ikili sınıflandırma problemi (Atak ve Normal) üzerinde çalışmışlardır. Ancak bu çalışmada saldırı türlerini sınıflandırmayı amaçladığından dokuz kategori kullanılmıştır. Hızlı yanıt veren bir IDS modeli önermek için karar ağacı (DT) olan hızlı bir sınıflandırıcı kullanılmıştır. Önerimiz, 10 kat çapraz doğrulama kullanarak bu veri setinde %97,43 sınıflandırma doğruluğu elde edilmiştir. Bu doğruluk oranı, IoT cihazları için önerilen IDS modelimizin sınıflandırma yeteneğini açıkça göstermektedir.

References

  • F. Ertam, I. F. Kilincer, O. Yaman, and A. Sengur, “A New IoT Application for Dynamic WiFi based Wireless Sensor Network,” 2020 Int. Conf. Electr. Eng. ICEE 2020, pp. 6–9, 2020, doi: 10.1109/ICEE49691.2020.9249771.
  • M. Hasan, M. M. Islam, M. I. I. Zarif, and M. M. A. Hashem, “Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches,” Internet of Things, vol. 7, p. 100059, Sep. 2019, doi: 10.1016/j.iot.2019.100059.
  • S. D. Okegbile and O. I. Ogunranti, “Users emulation attack management in the massive internet of things enabled environment,” ICT Express, vol. 6, no. 4, pp. 353–356, Dec. 2020, doi: 10.1016/j.icte.2020.06.005.
  • J. Ashraf et al., “IoTBoT-IDS: A novel statistical learning-enabled botnet detection framework for protecting networks of smart cities,” Sustain. Cities Soc., vol. 72, no. May, p. 103041, 2021, doi: 10.1016/j.scs.2021.103041.
  • “IoT Anaytics,” IoT Analytics - Market insights for the Internet of Things. https://iot-analytics.com/ (accessed Jun. 02, 2021).
  • K. Gupta and S. Shukla, “Internet of Things: Security challenges for next generation networks,” in 2016 1st International Conference on Innovation and Challenges in Cyber Security, ICICCS 2016, Aug. 2016, pp. 315–318, doi: 10.1109/ICICCS.2016.7542301.
  • P. Kumar, A. Braeken, A. Gurtov, J. Iinatti, and P. H. Ha, “Anonymous Secure Framework in Connected Smart Home Environments,” IEEE Trans. Inf. Forensics Secur., vol. 12, no. 4, pp. 968–979, Apr. 2017, doi: 10.1109/TIFS.2016.2647225.
  • E. Deniz, “Nesnelerin İnternetinde Gizlilik Ve Güvenlik Yönetimi, Yüksek Lisans Tezi, Ankara Üniversitesi,” Ankara, 2019.
  • G. D’Angelo, F. Palmieri, M. Ficco, and S. Rampone, “An uncertainty-managing batch relevance-based approach to network anomaly detection,” Appl. Soft Comput. J., vol. 36, pp. 408–418, 2015, doi: 10.1016/j.asoc.2015.07.029.
  • M. Ring, S. Wunderlich, D. Scheuring, D. Landes, and A. Hotho, “A survey of network-based intrusion detection data sets,” Comput. Secur., vol. 86, pp. 147–167, 2019, doi: 10.1016/j.cose.2019.06.005.
  • M. Shafiq, Z. Tian, A. K. Bashir, X. Du, and M. Guizani, “IoT malicious traffic identification using wrapper-based feature selection mechanisms,” Comput. Secur., vol. 94, p. 101863, Jul. 2020, doi: 10.1016/j.cose.2020.101863.
  • M. Mohammadi, A. Al-Fuqaha, S. Sorour, and M. Guizani, “Deep learning for IoT big data and streaming analytics: A survey,” IEEE Communications Surveys and Tutorials, vol. 20, no. 4. Institute of Electrical and Electronics Engineers Inc., pp. 2923–2960, Oct. 2018, doi: 10.1109/COMST.2018.2844341.
  • E. Yönem, “Nesnelerin Internetinde Veri Analizi İçin Tekrarlayıcı Sinir Ağları Yönetiminin Yapay Arı Koloni Algoritması İle Eğitilmesi, Erciyes Üniversitesi, Yüksek Lisans Tezi,” p. 124, 2019.
  • S. Rathore and J. H. Park, “Semi-supervised learning based distributed attack detection framework for IoT,” Appl. Soft Comput. J., vol. 72, pp. 79–89, 2018, doi: 10.1016/j.asoc.2018.05.049.
  • L. Xiao, X. Wan, X. Lu, Y. Zhang, and D. Wu, “IoT Security Techniques Based on Machine Learning,” IEEE Signal Process. Mag., vol. 35, no. 5, pp. 41–49, 2018, doi: 10.1109/MSP.2018.2825478.
  • I. Kotenko, I. Saenko, A. Kushnerevich, and A. Branitskiy, “Attack Detection in IoT Critical Infrastructures: A Machine Learning and Big Data Processing Approach,” Proc. - 27th Euromicro Int. Conf. Parallel, Distrib. Network-Based Process. PDP 2019, pp. 340–347, 2019, doi: 10.1109/EMPDP.2019.8671571.
  • L. Vu, Q. U. Nguyen, D. N. Nguyen, D. T. Hoang, and E. Dutkiewicz, “Deep Transfer Learning for IoT Attack Detection,” IEEE Access, vol. 8, pp. 107335–107344, 2020, doi: 10.1109/ACCESS.2020.3000476.
  • Q. Zhang, H. Zhong, W. Shi, and L. Liu, “A trusted and collaborative framework for deep learning in IoT,” Comput. Networks, vol. 193, p. 108055, Jul. 2021, doi: 10.1016/j.comnet.2021.108055.
  • C. Zhang and R. Green, “Communication security in internet of thing: Preventive measure and avoid DDoS attack over IoT network,” Simul. Ser., vol. 47, no. 3, pp. 8–15, 2015.
  • F. Y. Yavuz, “Deep Learning in Cyber Security for Internet of Things, Yüksek Lisans Tezi, Istanbul City University,” 2018.
  • E. M. Irmak, “Makine Ögrenmesi Regresyon Yöntemlerinin Nesnelerin İnterneti Verilerine Uygulanması, Yüksek Lisans Tezi, Harran Üniversitesi,” p. 75, 2019.
  • T. A. Gürkan, “Security Analysis of Coap and Dtls Protocols for Internet of Things Applications, Master of Science, Işık University,” p. 53, 2019.
  • M. Erhan, “It Security And Privacy Guidance Tool For IoT Designs And Products, Master Of Science, The Middle East Technical University,” vol. 8, no. 5, p. 127, 2019.
  • N. Koroniotis, N. Moustafa, E. Sitnikova, and B. Turnbull, “Towards the development of realistic botnet dataset in the Internet of Things for network forensic analytics: Bot-IoT dataset,” Futur. Gener. Comput. Syst., vol. 100, pp. 779–796, Nov. 2019, doi: 10.1016/j.future.2019.05.041.
  • N. Koroniotis and N. Moustafa, “The Bot-IoT Dataset,” UNSW Canberra at ADFA. https://research.unsw.edu.au/projects/bot-iot-dataset (accessed Jun. 02, 2021).
  • N. Koroniotis, “Designing an effective network forensic framework for the investigation of botnets in the Internet of Things,” no. March, 2020.
  • O. Yaman, H. Yetis, and M. Karakose, “Decision Tree Based Customer Analysis Method for Energy Planning in Smart Cities,” 2020, doi: 10.1109/ICDABI51230.2020.9325644.
There are 27 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Rojbin Tekin 0000-0002-1346-5929

Orhan Yaman 0000-0001-9623-2284

Türker Tuncer 0000-0002-5126-6445

Early Pub Date June 25, 2022
Publication Date June 28, 2022
Submission Date July 12, 2021
Published in Issue Year 2022 Volume: 6 Issue: 1

Cite

APA Tekin, R., Yaman, O., & Tuncer, T. (2022). Decision Tree Based Intrusion Detection Method in the Internet of Things. International Journal of Innovative Engineering Applications, 6(1), 17-23. https://doi.org/10.46460/ijiea.970383
AMA Tekin R, Yaman O, Tuncer T. Decision Tree Based Intrusion Detection Method in the Internet of Things. IJIEA. June 2022;6(1):17-23. doi:10.46460/ijiea.970383
Chicago Tekin, Rojbin, Orhan Yaman, and Türker Tuncer. “Decision Tree Based Intrusion Detection Method in the Internet of Things”. International Journal of Innovative Engineering Applications 6, no. 1 (June 2022): 17-23. https://doi.org/10.46460/ijiea.970383.
EndNote Tekin R, Yaman O, Tuncer T (June 1, 2022) Decision Tree Based Intrusion Detection Method in the Internet of Things. International Journal of Innovative Engineering Applications 6 1 17–23.
IEEE R. Tekin, O. Yaman, and T. Tuncer, “Decision Tree Based Intrusion Detection Method in the Internet of Things”, IJIEA, vol. 6, no. 1, pp. 17–23, 2022, doi: 10.46460/ijiea.970383.
ISNAD Tekin, Rojbin et al. “Decision Tree Based Intrusion Detection Method in the Internet of Things”. International Journal of Innovative Engineering Applications 6/1 (June 2022), 17-23. https://doi.org/10.46460/ijiea.970383.
JAMA Tekin R, Yaman O, Tuncer T. Decision Tree Based Intrusion Detection Method in the Internet of Things. IJIEA. 2022;6:17–23.
MLA Tekin, Rojbin et al. “Decision Tree Based Intrusion Detection Method in the Internet of Things”. International Journal of Innovative Engineering Applications, vol. 6, no. 1, 2022, pp. 17-23, doi:10.46460/ijiea.970383.
Vancouver Tekin R, Yaman O, Tuncer T. Decision Tree Based Intrusion Detection Method in the Internet of Things. IJIEA. 2022;6(1):17-23.