Botnet Attack Detection in IoT Network Using CNN-LSTM
Keywords:
Convolutional Neural Network, Internet of Things, Network Security, Cyber Security, Botnet attack, NBaIot datasetAbstract
The fast rise of the IoT (Internet of Things) has led to a growth in cyber assaults, especially on IoT devices. Malicious assaults in the IoT ecosystem must be detected in order to decrease safety issues. Botnet attacks, like Bashlite and Mirai, pose a significant threat to IoT devices. Static IoT devices, with the shortage of adequate memory and resources for computation, are particularly more vulnerable. To overcome this problem, we employed the CNN-LSTM model to identify various botnet assaults on IoT devices. In this work, we utilized an actual N-BaIoT dataset with mostly benign and malignant behaviours. According to the outcomes, the CNN-LSTM model works remarkably well. It notices botnet assaults on doorbell IoT devices (such as Danminin and Ennio) with 90.88% and 88.77% accuracy, respectively. The system also achieves 88.53% accuracy while identifying botnet attacks on thermostat device. By observing the results, we can mention that the Convolution Neural Network-Long Short Term Memory (CNN-LSTM) algorithm successfully detects botnet attacks on several IoT devices with high accuracy, providing an efficacious method for enhancing IoT security
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References
Radouan Ait Mouha, Internet of Things," Journal of Data Analysis and Information Processing, Vol.9 No.2, May 2021.
Ashton, Kevin, “That ‘internet of things’ thing,” RFID journal vol. 22, no. 7, pp. 97-114, 2009.
Steward, Jack, “The ultimate list of internet of things statistics for 2022,” URL https://findstack.com/internet-of-things-statistics (2022).’.
Jovanovic, Bojan, “Internet of things statistics for 2022-taking things apart,” Data Prot (2022).’.
Pepper, Robert, “Cisco visual networking index (VNI) global mobile data traffic forecast update.” In Mobile World Congress, 2013.
Alkahtani, Hasan, and Theyazn HH Aldhyani, “Botnet attack detection by using CNN-LSTM model for Internet of Things applications,” Security and Communication Networks, pp. 1-23, 2021,
Shazly, Khadija, Dina A. Salem, Nacereddine Hammami, and Ahmed IB ElSeddawy, “A Review on Distributed Denial of Service Detection in Software Defined Network.”’.
Kalidindi, Archana, and Mahesh Babu Arrama. “A tab transformer based model for detecting botnet-attacks on internet of things using deep learning,” Journal of Theoretical and Applied Information Technology, vol. 101, no. 13, 2023.
Kaur, Navdeep, and Maninder Singh, “Botnet and botnet detection techniques in cyber realm,” In 2016 international conference on inventive computation technologies (ICICT), vol. 3, pp. 1-7. IEEE, 2016.
B. Saha and A, Gairola, “Botnet: An overview,” CERT-In White PaperCIWP-2005-05, 2005.’.
M. Rajab, J. Zarfoss, F. Monrose, and A. Terzis, “A multifaceted approach to understanding the botnet phenomenon,” in Proc. 6th ACM SIGCOMM Conference on Internet Measurement (IMC’06), 2006, pp.41-52’,
Meidan, Yair, 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, vol.17, no. 3, pp. 12-22, 2018.
Li, Xin, Peng Yi, Wei Wei, Yiming Jiang, and Le Tian, “LNNLS-KH: a feature selection method for network intrusion detection,” Security and Communication Networks, pp. 1-22, 2021.
HaddadPajouh, Hamed, Ali Dehghantanha, Raouf Khayami, and Kim-Kwang Raymond Choo, “A deep recurrent neural network based approach for internet of things malware threat hunting,” Future Generation Computer Systems,vol. 85, pp. 88-96, 2018.
McDermott, Christopher D., Farzan Majdani, and Andrei V. Petrovski, “Botnet detection in the internet of things using deep learning approaches,” In 2018 international joint conference on neural networks (IJCNN), pp. 1-8. IEEE, 2018.
Alshamkhany, Mustafa, Wisam Alshamkhany, Mohamed Mansour, Mueez Khan, Salam Dhou, and Fadi Aloul, “Botnet attack detection using machine learning,” In 2020 14th International Conference on Innovations in Information Technology (IIT), pp. 203-208. IEEE, 2020.
X. Xie, D. Wu, S. Liu, and R. Li, “IoT data analytics using deep learning,” in IEEE Access, vol. 6, pp. 16793-16802, 2018, doi: 10.1109/ACCESS.2018.2815839.’.
Y. Meidan, M. Bohadana, A. Shabtai, M. Ochoa, N. O. Tippenhauer, J. D. Guarnizo, and Y. Elovici, “Detection of unauthorized IoT devices using machine learning techniques,” arXiv, 2017, arXiv:1709.04647 [cs.CR].’.
Jo, Wooyeon, Sungjin Kim, Changhoon Lee, and Taeshik Shon, “Packet preprocessing in CNN-based network intrusion detection system,” Electronics, vol. 9, no. 7,pp. 1151, 2020.
Guo, Yanming, Yu Liu, Ard Oerlemans, Songyang Lao, Song Wu, and Michael S. Lew, “Deep learning for visual understanding: A review.” Neurocomputing, vol. 187, pp. 27-48, 2016.
Wu, Jianxin, “Introduction to convolutional neural networks,” National Key Lab for Novel Software Technology. Nanjing University. China 5, no. 23,pp. 495, 2017.
Szegedy, Christian, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich, “Going deeper with convolutions,” In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1-9. 2015.,
Han, Kun, Dong Yu, and Ivan Tashev, “Speech emotion recognition using deep neural network and extreme learning machine,” In Interspeech 2014. 2014.’.
Ahmad, Zeeshan, Adnan Shahid Khan, Cheah Wai Shiang, Johari Abdullah, and Farhan Ahmad, “Network intrusion detection system: A systematic study of machine learning and deep learning approaches,” Transactions on Emerging Telecommunications Technologies, vol. 32, no. 1 (2021): e4150.’.
Kim, Jiyeon, Minsun Shim, Seungah Hong, Yulim Shin, and Eunjung Choi, “Intelligent detection of iot botnets using machine learning and deep learning,” Applied Sciences, vol. 10, no. 19 pp.7009, 2020.
Versaci M, Angiulli G, Crucitti P, De Carlo D, Laganà F, Pellicanò D, Palumbo A, “A Fuzzy Similarity Based Approach to Classify Numerically Simulated and Experimentally Detected Carbon Fiber-Reinforced Polymer Plate Defects,” Sensors, 2022, vol. 22, pp. 4232. https://doi.org/10.3390/s22114232.
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