Detection and Mitigation of Attacks at the Edge of IoT Networks Using Deep Learning and P4
Resumo
Context: The concern for security in IoT networks is becoming increasingly common, considering the accelerated use of these devices in recent years. Problem: Traditional methods for detecting attacks in IoT networks are ineffective due to device heterogeneity. While learning approaches show promise in threat detection, the computational limitations of IoT devices make it challenging to implement these solutions. Solution: This work proposes a deep learning solution for IoT attack detection, focusing on efficient threat mitigation using the Programming Protocol-independent Packet Processors (P4) language, adaptable feature extraction for algorithm inference, and execution on SBC devices. SI theory: This work is based on General Systems Theory and Machine Learning Theory, emphasizing integrating components in IoT networks and learning for attack detection. Methods: This study uses a case study to evaluate the effectiveness of deep learning algorithms in detecting attacks in IoT networks, adopting a descriptive and quantitative approach with metrics like accuracy and F1 score. Results: In our work, the Long Short Term Memory (LSTM), Convolutional Neural Network (CNN), and Gated Recurrent Unit (GRU) algorithms achieved accuracy rates of 96.9%, 95.6%, and 93.9%, respectively. However, the CNN model was selected for the final implementation due to its superior inference time and resource consumption performance. Contributions and impact in the IS area: This work contributes to Information Systems by integrating deep learning and P4 programming for attack detection and mitigation in IoT networks, enhancing security, and promoting further research in the field.
Referências
Mohammed Ali Al-Garadi, Amr Mohamed, Abdulla Khalid Al-Ali, Xiaojiang Du, Ihsan Ali, and Mohsen Guizani. 2020. A survey of machine and deep learning methods for internet of things (IoT) security. IEEE Communications Surveys & Tutorials 22, 3 (2020), 1646–1685.
Faris Alhamed, Davide Scano, Piero Castoldi, Juan Jose Vegas Olmos, Ilya Vershkov, Francesco Paolucci, and Filippo Cugini. 2023. P4 Telemetry collector. Computer Networks 227 (2023), 109727.
Pat Bosshart, Dan Daly, Glen Gibb, Martin Izzard, Nick McKeown, Jennifer Rexford, Cole Schlesinger, Dan Talayco, Amin Vahdat, George Varghese, et al. 2014. P4: Programming protocol-independent packet processors. ACM SIGCOMM Computer Communication Review 44, 3 (2014), 87–95.
Antonia Raiane Santos Araujo Cruz, Rafael Lopes Gomes, and Marcial Porto Fernandez. 2021. DIMI: Detecção Inteligente de Botnets Mirai em Redes IoT. Anais do Computer on the Beach 12 (2021), 362–369.
Mahmoud Said Elsayed, Nhien-An Le-Khac, Soumyabrata Dev, and Anca Delia Jurcut. 2020. Ddosnet: A deep-learning model for detecting network attacks. In 2020 IEEE 21st International Symposium on “A World of Wireless, Mobile and Multimedia Networks"(WoWMoM). IEEE, 391–396.
Mohamed Amine Ferrag, Othmane Friha, Djallel Hamouda, Leandros Maglaras, and Helge Janicke. 2022. Edge-IIoTset: A new comprehensive realistic cyber security dataset of IoT and IIoT applications for centralized and federated learning. IEEE Access 10 (2022), 40281–40306.
Pradyumna Gokhale, Omkar Bhat, and Sagar Bhat. 2018. Introduction to IOT. International Advanced Research Journal in Science, Engineering and Technology 5, 1 (2018), 41–44.
P4 Education Working Group. 2021. P4Pi: P4 on Raspberry Pi. [link].
Mahmudul Hasan, Md. Milon Islam, Md Ishrak Islam Zarif, and M.M.A. Hashem. 2019. Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches. Internet of Things 7 (2019), 100059. DOI: 10.1016/j.iot.2019.100059
Frederik Hauser, Marco Häberle, Daniel Merling, Steffen Lindner, Vladimir Gurevich, Florian Zeiger, Reinhard Frank, and Michael Menth. 2023. A survey on data plane programming with p4: Fundamentals, advances, and applied research. Journal of Network and Computer Applications 212 (2023), 103561.
Sya Raihan Heggi, Parman Sukarno, and Satria Akbar Mugitama. 2022. LSTM-NB: DoS Attack Detection On SDN With P4 Programmable Dataplane. In 2022 International Conference on Advanced Creative Networks and Intelligent Systems (ICACNIS). IEEE, 1–6.
Ammar D Jasim et al. 2022. A survey of intrusion detection using deep learning in internet of things. Iraqi Journal For Computer Science and Mathematics 3, 1 (2022), 83–93.
Ansam Khraisat and Ammar Alazab. 2021. A critical review of intrusion detection systems in the internet of things: techniques, deployment strategy, validation strategy, attacks, public datasets and challenges. Cybersecurity 4 (2021), 1–27.
Igor Kotenko, Konstantin Izrailov, and Mikhail Buinevich. 2022. Static analysis of information systems for IoT cyber security: a survey of machine learning approaches. Sensors 22, 4 (2022), 1335.
Sándor Laki, Radostin Stoyanov, Dávid Kis, Robert Soulé, Péter Vörös, and Noa Zilberman. 2021. P4Pi: P4 on Raspberry Pi for networking education. ACM SIGCOMM Computer Communication Review 51, 3 (2021), 17–21.
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. 2015. Deep learning. nature 521, 7553 (2015), 436–444.
Milton Lima, Ricardo Lima, Fernando Lins, and Michel Bonfim. 2022. Beholder–A CEP-based intrusion detection and prevention systems for IoT environments. Computers & Security 120 (2022), 102824.
Francisco Sales de Lima Filho, Frederico AF Silveira, Agostinho de Medeiros Brito Junior, Genoveva Vargas-Solar, and Luiz F Silveira. 2019. Smart detection: an online approach for DoS/DDoS attack detection using machine learning. Security and Communication Networks 2019 (2019), 1–15.
Francesco Musumeci, Ali Can Fidanci, Francesco Paolucci, Filippo Cugini, and Massimo Tornatore. 2022. Machine-learning-enabled DDoS attacks detection in P4 programmable networks. Journal of Network and Systems Management 30 (2022), 1–27.
Dhiego Ramos Pinto, Julio Cesar Duarte, and Ricardo Sant’Ana. 2019. A deep learning approach to the Malware classification problem using autoencoders. In Proceedings of the XV Brazilian Symposium on Information Systems. 1–8.
Qiaofeng Qin, Konstantinos Poularakis, and Leandros Tassiulas. 2020. A learning approach with programmable data plane towards IoT security. In 2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS). IEEE, 410–420.
Amiya Kumar Sahu, Suraj Sharma, Mohammad Tanveer, and Rohit Raja. 2021. Internet of Things attack detection using hybrid Deep Learning Model. Computer Communications 176 (2021), 146–154.
Eryk Schiller, Andy Aidoo, Jara Fuhrer, Jonathan Stahl, Michael Ziörjen, and Burkhard Stiller. 2022. Landscape of IoT security. Computer Science Review 44 (2022), 100467.
Sabrina Rocha Souza, Bruno Pedraça Souza, Anderson Gonçalves Uchôa, and Daniella Oliveira Costa. 2022. TEl-IoT: A Template for Eliciting IoT Software System Requirements. In Proceedings of the XVIII Brazilian Symposium on Information Systems. 1–8.
Claes Wohlin, Per Runeson, Martin Höst, Magnus C Ohlsson, Björn Regnell, and Anders Wesslén. 2012. Experimentation in software engineering. Springer Science & Business Media.
