Machine Learning Based Classification Model for Network Traffic Anomaly Detection
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Abstract
In current days, cloud environments are facing a huge challenge from the attackers in terms of various attacks thrown to the cloud service providers. In both industry and academics, the problem of detection and mitigation of DDoS attacks is now a challenging issue. Detecting Distributed Denial of Service (DDos) threats is mainly a classification problem that can be addressed using data mining, machine learning and deep learning techniques. DDoS attacks can occur in any of the seven-layer OSI model's network. Hence, detecting the DDoS attacks is an important task for cloud service providers to overcome dangerous attacks and loss incurred to stake holders and also the provider.
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References
Frederico A. F. Silveira,Agostinho De Medeiros Brito Junior,Genoveva Vargas-Solar, And Luiz F. Silveira, “Smart Detection: An Online Approach For Dos/Ddos Attack Detection Using Machine Learning”,Volume 2019 |Article Id 1574749
Ivandro Ortet Lopes, Deqing Zou,Francis A Ruambo,Saeed Akbar, And Bin Yuan, “Towards Effective Detection Of Recent Ddos Attacks: A Deep Learning Approach”, Volume 2021 |Article Id 5710028
P. Renuka ,Dr. B. Booba, Professor ,”Analysis On Detecting Ddos Attack In Iot Environment “,2018, Issn : 0731-6755
Yuanyuan Wei; Julian Jang-Jaccard; Fariza Sabrina; Amardeep Singh; Wen Xu; Seyit Camtepe,”Ae-Mlp: A Hybrid Deep Learning Approach For Ddos Detection And Classification”,Ieee Access ( Volume: 9)
Zecheng He; Tianwei Zhang; Ruby B. Lee,”Machine Learning Based Ddos Attack Detection From Source Side In Cloud”,2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud)
B. Narsimha, Ch V Raghavendran, Pannangi Rajyalakshmi, G Kasi Reddy, M. Bhargavi and P. Naresh (2022), Cyber Defense in the Age of Artificial Intelligence and Machine Learning for Financial Fraud Detection Application. IJEER 10(2), 87-92. DOI: 10.37391/IJEER.100206.
Naresh, P., & Suguna, R. (2021). IPOC: An efficient approach for dynamic association rule generation using incremental data with updating supports. Indonesian Journal of Electrical Engineering and Computer Science, 24(2), 1084. https://doi.org/10.11591/ijeecs.v24.i2.pp1084-1090.
Mark White, Thomas Wood, Carlos Rodríguez, Pekka Koskinen, Jónsson Ólafur. Exploring Natural Language Processing in Educational Applications. Kuwait Journal of Machine Learning, 2(1). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/168
Kolawole Abubakar Sadiq, Aderonke Thompson, “Mitigating DDoS Attacks in Cloud Network using Fog and SDN: A Conceptual Security Framework”, DOI:10.5120/ijais2020451877, August 2020
Swathi Sambangi,Lakshmeeswari Gondi, ”A Machine Learning Approach for DDoS (Distributed Denial of Service) Attack Detection Using Multiple Linear Regression”, 25 December 2020.
Er. Sakshi kakkar , Er. Dinesh kumar, “A Survey on Distributed Denial of Services (DDOS) “,Vol.5(3), 2014,3863-3866.
Nazrul Hoque; Dhruba K Bhattacharyya; Jugal K Kalita,”A novel measure for low-rate and high-rate DDoS attack detection using multivariate data analysis”, 2016 8th International Conference on Communication Systems and Networks.
Ankit Agarwal, Manju Khari & Rajiv Singh, “Detection of DDOS Attack using Deep Learning Model in Cloud Storage Application”, Wireless Pers Commun (2021).
T. Aruna, P. Naresh, A. Rajeshwari, M. I. T. Hussan and K. G. Guptha, "Visualization and Prediction of Rainfall Using Deep Learning and Machine Learning Techniques," 2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS), Tashkent, Uzbekistan, 2022, pp. 910-914, doi: 10.1109/ICTACS56270.2022.9988553.
V. Krishna, Y. D. Solomon Raju, C. V. Raghavendran, P. Naresh and A. Rajesh, "Identification of Nutritional Deficiencies in Crops Using Machine Learning and Image Processing Techniques," 2022 3rd International Conference on Intelligent Engineering and Management (ICIEM), London, United Kingdom, 2022, pp. 925-929, doi: 10.1109/ICIEM54221.2022.9853072.
Naresh, P., & Suguna, R. (2019). Association Rule Mining Algorithms on Large and Small Datasets: A Comparative Study. 2019 International Conference on Intelligent Computing and Control Systems (ICCS). DOI:10.1109/iccs45141.2019.9065836.
Naresh, K. Pavan kumar, and D. K. Shareef, ‘Implementation of Secure Ranked Keyword Search by Using RSSE,’ International Journal of Engineering Research & Technology (IJERT) ISSN: 2278-0181 Vol. 2 Issue 3, March – 2013.
S, D. A. (2021). CCT Analysis and Effectiveness in e-Business Environment. International Journal of New Practices in Management and Engineering, 10(01), 16–18. https://doi.org/10.17762/ijnpme.v10i01.97
M. I. Thariq Hussan, D. Saidulu, P. T. Anitha, A. Manikandan and P. Naresh (2022), Object Detection and Recognition in Real Time Using Deep Learning for Visually Impaired People. IJEER 10(2), 80-86. DOI: 10.37391/IJEER.100205.. https://doi.org/10.18280/ria.360107.
S. Khaleelullah, P. Marry, P. Naresh, P. Srilatha, G. Sirisha and C. Nagesh, "A Framework for Design and Development of Message sharing using Open-Source Software," 2023 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS), Erode, India, 2023, pp. 639-646, doi: 10.1109/ICSCDS56580.2023.10104679.
Dasari, K.B., Devarakonda, N. (2021). Detection of different DDoS attacks using machine learning classification algorithms. Ingénierie des Systèmes d’Information, Vol. 26, No. 5, pp. 461-468. https://https://doi.org/10.18280/isi.260505.