Abstract
In today’s world, cybersecurity is important as it secures data against damage and unauthorized access. In recent years, machine learning studies have made remarkable developments in cybersecurity and helped in deriving the pattern of user activities, checking for malicious activities in the network, and identifying the cyberattacks. This research intends to predict the DDoS attack that impedes network access by flooding a large amount of traffic, saturating the bandwidth of the network. The proposed work uses convolutional neural network (CNN) to train the machine learning model to anticipate the DDoS attack before it happens. The CNN model is trained using live data, where the data packets are captured using Wireshark and the KDD CUP 1999 dataset. The packet’s information is converted into a two-dimensional image and is trained using the CNN algorithm. The proposed system’s performance is evaluated and compared with the existing IDS systems, which attains a maximum accuracy of 95.8%.
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References
Liu H, Patras P (2020) NetSentry: a deep learning approach to detecting incipient large-scale network attacks. arXiv:2202.09873
Li Y, Liu Q (2018) A comprehensive review study of cyber-attacks and cyber-security; emerging trends and recent developments. Energy Rep 7:8176–8186. ISSN 2352-4847. https://doi.org/10.1016/j.egyr.2021.08.126
Alazzam H, Sharieh A, Sabri KE (2020) A feature selection algorithm for intrusion detection system based on Pigeon Inspired Optimizer. Expert Syst Appl 148:113249. ISSN 0957-4174. https://doi.org/10.1016/j.eswa.2020.113249
Sambangi S, Gondi L (2020) A machine learning approach for DDoS (distributed denial of service) attack detection using multiple linear regression. Proceedings 63:51. https://doi.org/10.3390/proceedings2020063051
Ferrag MA, Maglaras L, Janicke H, Smith R (2019) Deep learning techniques for cyber security intrusion detection: a detailed analysis. https://doi.org/10.14236/ewic/icscsr19.16
Smitha TV, Madhura S, Sindhu R, Brundha R (2021) A study on various mesh generation techniques used for engineering applications
Farda NA, Lai J-Y, Wang J-C, Lee P-Y, Liu J-W, Hsieh I-H (2021) Sanders classification of calcaneal fractures in CT images with deep learning and differential data augmentation techniques. Injury 52(3):616–624. ISSN 0020-1383. https://doi.org/10.1016/j.injury.2020.09.010
Liu G, Zhang J (2020) CNID: research of network intrusion detection based on convolutional neural network. Discrete Dyn Nat Soc 2020:11, Article ID 4705982. https://doi.org/10.1155/2020/4705982
Zhou J, Liu L, Wei W, Fan J (2023) Network representation learning: from preprocessing, feature extraction to node embedding. ACM Comput Surv 55(2):35, Article 38. https://doi.org/10.1145/3491206
Bi Q, Goodman KE, Kaminsky J, Lessler J (2019) What is machine learning? A primer for the epidemiologist. Am J Epidemiol 188(12):2222–2239. https://doi.org/10.1093/aje/kwz189
Gregorczyk M, Żórawski P, Nowakowski PT, Cabaj K, Mazurczyk W (2020) Sniffing detection based on network traffic probing and machine learning. IEEE Access 8:149255–149269
Shaaban A, Abd-Elwanis E, Hussein M (2019) DDoS attack detection and classification via convolutional neural network (CNN) 233–238. https://doi.org/10.1109/ICICIS46948.2019.9014826
Cheng J, Cai C, Tang X, Sheng V, Guo W, Li M (2020) A DDoS attack information fusion method based on CNN for multi-element data. Comput Mater Continua. 62:131–150. https://doi.org/10.32604/cmc.2020.06175
Jia Y, Zhong F, Alrawais A, Gong B, Cheng X (2020) FlowGuard: an intelligent edge defense mechanism against IoT DDoS attacks. IEEE Internet Things J 7(10):9552–9562. https://doi.org/10.1109/JIOT.2020.2993782
Kumar T (2020) Video based traffic forecasting using convolution neural network model and transfer learning techniques. J Innovative Image Process 2:128–134. https://doi.org/10.36548/jiip.2020.3.002
Shin H-C, Roth HR, Gao M, Lu L, Xu Z, Nogues I, Yao J, Mollura DJ, Summers RM (2016) Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging 35:1285–1298
Aljuhani A (2021) Machine learning approaches for combating distributed denial of service attacks in modern networking environments. IEEE Access 9:42236–42264. https://doi.org/10.1109/ACCESS.2021.3062909
Kim J, Kim J, Kim H, Shim M, Choi E (2020) CNN-based network intrusion detection against denial-of-service attacks. Electronics 9(6):916. https://doi.org/10.3390/electronics9060916
Dodiya B, Singh U (2022) Malicious traffic analysis using Wireshark by collection of indicators of compromise. Int J Comput Appl 183:975–8887. https://doi.org/10.5120/ijca2022921876
Wu J, Wang X, Gao X, Chen J, Fu H, Qiu T, He X (2022) On the effectiveness of sampled softmax loss for item recommendation. arXiv:2201.02327
Muhammad MI, Hussain H, Khan AA, Ullah U, Muhammad Z, Ahmed A, Raza M, Rahman I, Haleem M (2022) A machine learning-based classification and prediction technique for DDoS -attacks. IEEE Access. https://doi.org/10.1109/ACCESS.2022.3152577
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Rinish Reddy, R., Rachamalla, S., Yoosuf, M.S., Anil, G.R. (2023). Convolutional Neural Network Based Intrusion Detection System and Predicting the DDoS Attack. In: Jacob, I.J., Kolandapalayam Shanmugam, S., Izonin, I. (eds) Data Intelligence and Cognitive Informatics. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-19-6004-8_7
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DOI: https://doi.org/10.1007/978-981-19-6004-8_7
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