QUIDS: A Novel Edge-Based Botnet Detection with Quantization for IoT Device Pairing
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Keywords:
Intrusion detection system, edge computing, botnet attack, quantization, IoT device pairing
Abstract
Advanced machine learning has managed to detect IoT botnets. However, conflicts arise due to complex models and limited device resources. Our research aim is on a quantized intrusion detection system (QUIDS), an edge-based botnet detection for IoT device pairing. Using knearest neighbor (KNN) within QUIDS, we incorporate quantization, random sampling (RS), and feature selection (FS). Initially, we simulated a botnet attack, devised countermeasures via a sequence diagram, and then utilized a Kaggle botnet attack dataset. Our novel approach includes RS, FS, and 16-bit quantization, optimizing each step empirically. The test results show that employing a mean decrease in impurity (MDI) by FS reduces features from 115 to 30. Despite a slight accuracy drop in KNN due to RS, FS, and quantization sustain performance. Testing our model revealed 1200 RS samples as optimal, maintaining performance while reducing features. Quantization to 16-bit doesn’t alter feature value distribution. Implementing QUIDS increased the compression ratio (CR) to 175×, surpassing RS+FS threefold and RS by 13 times. This novel method emerges as the most efficient in CR.
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References
[1] Manoj S Koli and Manik K Chavan. An advanced method for detection of botnet traffic using intrusion detection system. In 2017 International Conference on Inventive Communication and Computational Technologies (ICICCT), pages 481–485. IEEE, 2017.
[2] Bandari Pranay Kumar, Gautham Rampalli, Pille Kamakshi, and T Senthil Murugan. Ddos botnet attack detection in iot devices. In Smart Trends in Computing and Communications: Proceedings of SmartCom 2022, pages 21–27. Springer, 2022.
[3] Alaa Dhahi Khaleefah and Haider M Al-Mashhadi. Detection of iot botnet cyber attacks using machine learning. Informatica, 47(6), 2023.
[4] Aji Gautama Putrada, Maman Abdurohman, Doan Perdana, and Hilal Hudan Nuha. Shuffle split-edited nearest neighbor: A novel intelligent control model compression for smart lighting in edge computing environment. In Information Systems for Intelligent Systems: Proceedings of ISBM 2022, pages 219–227. Springer, 2023.
[5] Aji Gautama Putrada, Maman Abdurohman, Doan Perdana, and Hilal Hudan Nuha. Edgesl: Edge-computing architecture on smart lighting control with distilled knn for optimum processing time. IEEE Access, 2023.
[6] Yun Cai, Hong Gu, and Toby Kenney. Rank selection for non-negative matrix factorization, 2022.
[7] Luxi Jiang and Xiuhong Chen. Spectral feature selection via low rank decomposition and local preservation. In 2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE), pages 518–522. IEEE, 2023.
[8] Zezhou Zhu, Yuan Dong, and Zhong Zhao. Learning low-rank representations for model compression. In 2023 International Joint Conference on Neural Networks (IJCNN), pages 1–9. IEEE, 2023.
[9] Marta Catillo, Antonio Pecchia, and Umberto Villano. A deep learning method for lightweight and cross-device iot botnet detection. Applied Sciences, 13(2):837, 2023.
[10] Yan Naung Soe, Yaokai Feng, Paulus Insap Santosa, Rudy Hartanto, and Kouichi Sakurai. Machine learning-based iot-botnet attack detection with sequential architecture. Sensors, 20(16):4372, 2020.
[11] Arne Bruesch, Ngu Nguyen, Dominik Schürmann, Stephan Sigg, and Lars Wolf. Security properties of gait for mobile device pairing. IEEE Transactions on Mobile Computing, 19(3):697–710, 2019.
[12] Habiba Farrukh, Muslum Ozgur Ozmen, Faik Kerem Ors, and Z Berkay Celik. One key to rule them all: Secure group pairing for heterogeneous iot devices. In 2023 IEEE Symposium on Security and Privacy (SP), pages 3026–3042. IEEE, 2023.
[13] Pradeeka Seneviratne and Pradeeka Seneviratne. Connecting with iot servers using a restful api. Beginning LoRa Radio Networks with Arduino: Build Long Range, Low Power Wireless IoT Networks, pages 171–194, 2019.
[14] Aji Gautama Putrada and Nur Ghaniaviyanto Ramadhan. A proposed hidden markov model method for dynamic device pairing on internet of things end devices. Journal of ICT Research & Applications, 14(3), 2021.
[15] Heka Bagaskara, Aji Gautama Putrada, and Endro Ariyanto. Proximity and dynamic device pairing based authentication for iot end devices with decision tree method. In 2020 6th International Conference on Interactive Digital Media (ICIDM), pages 1–5. IEEE, 2020.
[16] Amritanshu Pandey, Sumaiya Thaseen, Ch Aswani Kumar, and Gang Li. Identification of botnet attacks using hybrid machine learning models. In Hybrid Intelligent Systems: 19th International Conference on Hybrid Intelligent Systems (HIS 2019) held in Bhopal, India, December 10-12, 2019 19, pages 249–257. Springer, 2021.
[17] Aji Gautama Putrada, Nur Alamsyah, Syafrial Fachri Pane, Mohamad Nurkamal Fauzan, and Doan Perdana. Knowledge distillation for a lightweight deep learning-based indoor positioning system on edge environments. In 2023 International Seminar on Intelligent Technology and Its Applications (ISITIA), pages 370–375. IEEE, 2023.
[18] Prashant Kumar, Gaurav Purohit, Pramod Tanwar, Chitra Gautam, and Kota Solomon Raju. Real time, an iot-based affordable air pollution monitoring for smart home. In First International Conference on Sustainable Technologies for Computational Intelligence: Proceedings of ICTSCI 2019, pages 837–844. Springer, 2019.
[19] Aji Gautama Putrada, Nur Alamsyah, Syafrial Fachri Pane, and Mohamad Nurkamal Fauzan. Xgboost for ids on wsn cyber attacks with imbalanced data. In 2022 International Symposium on Electronics and Smart Devices (ISESD), pages 1–7. IEEE, 2022.
[20] BL Kiran, J Chandan, BS Jeevan, C Mohananka, and Vallabh Mahale. A survey on door lock security system using iot. Perspectives in Communication, Embedded-systems and Signal-processing-PiCES, 5(2):40–43, 2021.
[21] Irfan, IM Wildani, and IN Yulita. Classifying botnet attack on internet of things device using random forest. In IOP Conference Series: Earth and Environmental Science, volume 248, page 012002. IOP Publishing, 2019.
[22] Rufaida Bibi Auliar and Girish Bekaroo. Security in iot-based smart homes: A taxonomy study of detection methods of mirai malware and countermeasures. In 2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), pages 1–6. IEEE, 2021.
[23] Pooja Kumari and Ankit Kumar Jain. A comprehensive study of ddos attacks over iot network and their countermeasures. Computers & Security, page 103096, 2023.
[24] Benjamin Vignau, Raphaël Khoury, Sylvain Hallé, and Abdelwahab Hamou-Lhadj. The evolution of iot malwares, from 2008 to 2019: Survey, taxonomy, process simulator and perspectives. Journal of Systems Architecture, 116:102143, 2021.
[25] Raphaël Khoury, Benjamin Vignau, Sylvain Hallé, Abdelwahab Hamou-Lhadj, and Asma Razgallah. An analysis of the use of cves by iot malware. In Foundations and Practice of Security: 13th International Symposium, FPS 2020, Montreal, QC, Canada, December 1–3, 2020, Revised Selected Papers 13, pages 47–62. Springer, 2021.
[26] Yair Meidan, Michael Bohadana, Yael Mathov, Yisroel Mirsky, Asaf Shabtai, Dominik Breitenbacher, and Yuval Elovici. N-baiot—network-based detection of iot botnet attacks using deep autoencoders. IEEE Pervasive Computing, 17(3):12–22, 2018.
[27] Rofif Irsyad Fakhruddin, Maman Abdurohman, and Aji Gautama Putrada. Improving pir sensor network-based activity recognition with pca and knn. In 2021 International Conference on Intelligent Cybernetics Technology & Applications (ICICyTA), pages 138–143. IEEE, 2021.
[28] Parlin Nando, Aji Gautama Putrada, and Maman Abdurohman. Increasing the precision of noise source detection system using knn method. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, pages 157–168, 2019.
[29] Ikke Dian Oktaviani and Aji Gautama Putrada. Knn imputation to missing values of regression-based rain duration prediction on bmkg data. Jurnal Infotel, 14(4):249–254, 2022.
[30] Faza Ghassani, Maman Abdurohman, and Aji Gautama Putrada. Prediction of smarthphone charging using k-nearest neighbor machine learning. In 2018 Third International Conference on Informatics and Computing (ICIC), pages 1–4. IEEE, 2018.
[31] Sufen Chen, Xueqiang Zeng, et al. Progressive sampling-based joint automatic model selection of machine learning and feature selection. Journal of Artificial Intelligence Practice, 4(1):30–38, 2021.
[32] Edy Syuryawan Saputra, Aji Gautama Putrada, and Maman Abdurohman. Selection of vape sensing features in iot-based gas monitoring with feature importance techniques. In 2019 Fourth International Conference on Informatics and Computing (ICIC), pages 1–5. IEEE, 2019.
[33] Hanlin Lu, Changchang Liu, Shiqiang Wang, Ting He, Vijaykrishnan Narayanan, Kevin S Chan, and Stephen Pasteris. Joint coreset construction and quantization for distributed machine learning. In 2020 IFIP Networking Conference (Networking), pages 172–180. IEEE, 2020.
[34] Aji Gautama Putrada, Irfan Dwi Wijaya, and Dita Oktaria. Overcoming data imbalance problems in sexual harassment classification with smote. International Journal on Information and Communication Technology (IJoICT), 8(1):20–29, 2022.
[35] Günce Keziban Orman and Serhat Çolak. Similarity based compression ratio for dynamic network modelling. In IEEE EUROCON 2021-19th International Conference on Smart Technologies, pages 227–232. IEEE, 2021.
[36] Tzu-Tsung Wong and Po-Yang Yeh. Reliable accuracy estimates from k-fold cross validation. IEEE Transactions on Knowledge and Data Engineering, 32(8):1586–1594, 2019.
[37] Ruslan Seifullaev, Steffi Knorn, and Anders Ahlén. A comparative investigation of information loss due to variable quantization on parameter estimation of compound distribution. IFAC-PapersOnLine, 53(2):2379–2384, 2020.
[2] Bandari Pranay Kumar, Gautham Rampalli, Pille Kamakshi, and T Senthil Murugan. Ddos botnet attack detection in iot devices. In Smart Trends in Computing and Communications: Proceedings of SmartCom 2022, pages 21–27. Springer, 2022.
[3] Alaa Dhahi Khaleefah and Haider M Al-Mashhadi. Detection of iot botnet cyber attacks using machine learning. Informatica, 47(6), 2023.
[4] Aji Gautama Putrada, Maman Abdurohman, Doan Perdana, and Hilal Hudan Nuha. Shuffle split-edited nearest neighbor: A novel intelligent control model compression for smart lighting in edge computing environment. In Information Systems for Intelligent Systems: Proceedings of ISBM 2022, pages 219–227. Springer, 2023.
[5] Aji Gautama Putrada, Maman Abdurohman, Doan Perdana, and Hilal Hudan Nuha. Edgesl: Edge-computing architecture on smart lighting control with distilled knn for optimum processing time. IEEE Access, 2023.
[6] Yun Cai, Hong Gu, and Toby Kenney. Rank selection for non-negative matrix factorization, 2022.
[7] Luxi Jiang and Xiuhong Chen. Spectral feature selection via low rank decomposition and local preservation. In 2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE), pages 518–522. IEEE, 2023.
[8] Zezhou Zhu, Yuan Dong, and Zhong Zhao. Learning low-rank representations for model compression. In 2023 International Joint Conference on Neural Networks (IJCNN), pages 1–9. IEEE, 2023.
[9] Marta Catillo, Antonio Pecchia, and Umberto Villano. A deep learning method for lightweight and cross-device iot botnet detection. Applied Sciences, 13(2):837, 2023.
[10] Yan Naung Soe, Yaokai Feng, Paulus Insap Santosa, Rudy Hartanto, and Kouichi Sakurai. Machine learning-based iot-botnet attack detection with sequential architecture. Sensors, 20(16):4372, 2020.
[11] Arne Bruesch, Ngu Nguyen, Dominik Schürmann, Stephan Sigg, and Lars Wolf. Security properties of gait for mobile device pairing. IEEE Transactions on Mobile Computing, 19(3):697–710, 2019.
[12] Habiba Farrukh, Muslum Ozgur Ozmen, Faik Kerem Ors, and Z Berkay Celik. One key to rule them all: Secure group pairing for heterogeneous iot devices. In 2023 IEEE Symposium on Security and Privacy (SP), pages 3026–3042. IEEE, 2023.
[13] Pradeeka Seneviratne and Pradeeka Seneviratne. Connecting with iot servers using a restful api. Beginning LoRa Radio Networks with Arduino: Build Long Range, Low Power Wireless IoT Networks, pages 171–194, 2019.
[14] Aji Gautama Putrada and Nur Ghaniaviyanto Ramadhan. A proposed hidden markov model method for dynamic device pairing on internet of things end devices. Journal of ICT Research & Applications, 14(3), 2021.
[15] Heka Bagaskara, Aji Gautama Putrada, and Endro Ariyanto. Proximity and dynamic device pairing based authentication for iot end devices with decision tree method. In 2020 6th International Conference on Interactive Digital Media (ICIDM), pages 1–5. IEEE, 2020.
[16] Amritanshu Pandey, Sumaiya Thaseen, Ch Aswani Kumar, and Gang Li. Identification of botnet attacks using hybrid machine learning models. In Hybrid Intelligent Systems: 19th International Conference on Hybrid Intelligent Systems (HIS 2019) held in Bhopal, India, December 10-12, 2019 19, pages 249–257. Springer, 2021.
[17] Aji Gautama Putrada, Nur Alamsyah, Syafrial Fachri Pane, Mohamad Nurkamal Fauzan, and Doan Perdana. Knowledge distillation for a lightweight deep learning-based indoor positioning system on edge environments. In 2023 International Seminar on Intelligent Technology and Its Applications (ISITIA), pages 370–375. IEEE, 2023.
[18] Prashant Kumar, Gaurav Purohit, Pramod Tanwar, Chitra Gautam, and Kota Solomon Raju. Real time, an iot-based affordable air pollution monitoring for smart home. In First International Conference on Sustainable Technologies for Computational Intelligence: Proceedings of ICTSCI 2019, pages 837–844. Springer, 2019.
[19] Aji Gautama Putrada, Nur Alamsyah, Syafrial Fachri Pane, and Mohamad Nurkamal Fauzan. Xgboost for ids on wsn cyber attacks with imbalanced data. In 2022 International Symposium on Electronics and Smart Devices (ISESD), pages 1–7. IEEE, 2022.
[20] BL Kiran, J Chandan, BS Jeevan, C Mohananka, and Vallabh Mahale. A survey on door lock security system using iot. Perspectives in Communication, Embedded-systems and Signal-processing-PiCES, 5(2):40–43, 2021.
[21] Irfan, IM Wildani, and IN Yulita. Classifying botnet attack on internet of things device using random forest. In IOP Conference Series: Earth and Environmental Science, volume 248, page 012002. IOP Publishing, 2019.
[22] Rufaida Bibi Auliar and Girish Bekaroo. Security in iot-based smart homes: A taxonomy study of detection methods of mirai malware and countermeasures. In 2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), pages 1–6. IEEE, 2021.
[23] Pooja Kumari and Ankit Kumar Jain. A comprehensive study of ddos attacks over iot network and their countermeasures. Computers & Security, page 103096, 2023.
[24] Benjamin Vignau, Raphaël Khoury, Sylvain Hallé, and Abdelwahab Hamou-Lhadj. The evolution of iot malwares, from 2008 to 2019: Survey, taxonomy, process simulator and perspectives. Journal of Systems Architecture, 116:102143, 2021.
[25] Raphaël Khoury, Benjamin Vignau, Sylvain Hallé, Abdelwahab Hamou-Lhadj, and Asma Razgallah. An analysis of the use of cves by iot malware. In Foundations and Practice of Security: 13th International Symposium, FPS 2020, Montreal, QC, Canada, December 1–3, 2020, Revised Selected Papers 13, pages 47–62. Springer, 2021.
[26] Yair Meidan, Michael Bohadana, Yael Mathov, Yisroel Mirsky, Asaf Shabtai, Dominik Breitenbacher, and Yuval Elovici. N-baiot—network-based detection of iot botnet attacks using deep autoencoders. IEEE Pervasive Computing, 17(3):12–22, 2018.
[27] Rofif Irsyad Fakhruddin, Maman Abdurohman, and Aji Gautama Putrada. Improving pir sensor network-based activity recognition with pca and knn. In 2021 International Conference on Intelligent Cybernetics Technology & Applications (ICICyTA), pages 138–143. IEEE, 2021.
[28] Parlin Nando, Aji Gautama Putrada, and Maman Abdurohman. Increasing the precision of noise source detection system using knn method. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, pages 157–168, 2019.
[29] Ikke Dian Oktaviani and Aji Gautama Putrada. Knn imputation to missing values of regression-based rain duration prediction on bmkg data. Jurnal Infotel, 14(4):249–254, 2022.
[30] Faza Ghassani, Maman Abdurohman, and Aji Gautama Putrada. Prediction of smarthphone charging using k-nearest neighbor machine learning. In 2018 Third International Conference on Informatics and Computing (ICIC), pages 1–4. IEEE, 2018.
[31] Sufen Chen, Xueqiang Zeng, et al. Progressive sampling-based joint automatic model selection of machine learning and feature selection. Journal of Artificial Intelligence Practice, 4(1):30–38, 2021.
[32] Edy Syuryawan Saputra, Aji Gautama Putrada, and Maman Abdurohman. Selection of vape sensing features in iot-based gas monitoring with feature importance techniques. In 2019 Fourth International Conference on Informatics and Computing (ICIC), pages 1–5. IEEE, 2019.
[33] Hanlin Lu, Changchang Liu, Shiqiang Wang, Ting He, Vijaykrishnan Narayanan, Kevin S Chan, and Stephen Pasteris. Joint coreset construction and quantization for distributed machine learning. In 2020 IFIP Networking Conference (Networking), pages 172–180. IEEE, 2020.
[34] Aji Gautama Putrada, Irfan Dwi Wijaya, and Dita Oktaria. Overcoming data imbalance problems in sexual harassment classification with smote. International Journal on Information and Communication Technology (IJoICT), 8(1):20–29, 2022.
[35] Günce Keziban Orman and Serhat Çolak. Similarity based compression ratio for dynamic network modelling. In IEEE EUROCON 2021-19th International Conference on Smart Technologies, pages 227–232. IEEE, 2021.
[36] Tzu-Tsung Wong and Po-Yang Yeh. Reliable accuracy estimates from k-fold cross validation. IEEE Transactions on Knowledge and Data Engineering, 32(8):1586–1594, 2019.
[37] Ruslan Seifullaev, Steffi Knorn, and Anders Ahlén. A comparative investigation of information loss due to variable quantization on parameter estimation of compound distribution. IFAC-PapersOnLine, 53(2):2379–2384, 2020.
Published
2024-01-30
How to Cite
Aji Gautama Putrada, Nur Alamsyah, Mohamad Nurkamal Fauzan, Sidik Prabowo, & Ikke Dian Oktaviani. (2024). QUIDS: A Novel Edge-Based Botnet Detection with Quantization for IoT Device Pairing. Indonesia Journal on Computing (Indo-JC), 8(3), 29-41. https://doi.org/10.34818/INDOJC.2023.8.3.878
Section
Articles
Copyright (c) 2023 Aji Gautama Putrada, Nur Alamsyah, Mohamad Nurkamal Fauzan, Sidik Prabowo, Ikke Dian Oktaviani
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