Sathish Kumar M, Praveena T


Internet is the global network that interconnects entities all over the world. This unparalleled network has occupied the mandatory part in the life of every individual. In recent days, due to the increase in the number of flow, the internet traffic is increased. The increasing traffic is flooding with the DDoS flows from multiple DDoS attackers. If DDoS flow traffic enters the internet, then there will be a drastic increase in the utilization of resources. Due to this, the legitimate traffic will not get proper service. In order to address the above issues, this paper has proposed an approach that classifies the internet traffic as Normal traffic flow or DDoS traffic flow. A huge volume of traffic flows is analyzed in this paper and the results are presented. The MapReduce is implemented for the classification as it accurately maps the flow features and reduces them into the appropriate traffic type. The incoming traffic is classified into one of the three categories as Web Traffic, DDoS Traffic (Heavy User) or DDoS Traffic (Spoofed IP). The main objective of this paper is to classify structured as well as unstructured data of IP, TCP, HTTP and NetFlow analysis. The experimental observations were carried out in the Hadoop 2.7.2 environment. The dataset is obtained from Wireshark, which consists of traffic flow based on latest traffic pattern. Hadoop Distributed File System (HDFS) and MapReduce components of Hadoop are used under the metrics as Work Completion Time, Throughput and Accuracy.


DDoS Traffic, Hadoop, Internet Traffic, MapReduce, Spoofed IP

Full Text:



Arthur Callado, Carlos Kamienski, Geza Szabo, Balazs Peter Gero, Judith Kelner, Stenio Fernandes, Djamel Sadok, “A Survey on Internet Traffic Identification”, IEEE Communications Surveys & Tutorials, IEEE, Vol. 11, no. 3, pp. no. 37-52, 2009.

Akshay Kumar Suman, Dr. Manasi Gyanchandani, Priyank Jain, “A Survey on Miscellaneous Attacks in Hadoop Framework”, 2018 2nd International Conference on Inventive Systems and Control, IEEE, 2018.

Ronaldo Celso Messias Correia, Gabriel Spadon, Pedro Henrique De Andrade Gomes, Danilo Medeiros Eler, Rogério Eduardo Garcia and Celso Olivete Junior, “Hadoop Cluster Deployment:A Methodological Approach”, information, MDPI, Vol. 9, no. 6, 2018.

Kaushik Sekaran, G.Raja Vikram, B.V. Chowdar, UNP Gangadhar Raju, “Combating Distributed Denial of Service Attacks Using Load Balanced Hadoop Clustering in Cloud Computing Environment”, ICDTE 2018: Proceedings of the 2nd International Conference on Digital Technology in Education, pp. no. 77-81, 2018.

Andrea Morichetta, Marco Mellia, “Clustering and evolutionary approach for longitudinal web traffic analysis”, Performance Evaluation, ELSEVIER, vol. 135, 2019.

Neha Sehta, Karuna Mishra, “Network Traffic Classification Using Hadoop Server”, International Journal of Engineering Science and Computing, IJESC, Vol. 8, no. 10, 2018.

Margaret Gratian, Darshan Bhansali, Michel Cukier, Josiah Dykstra, “Identifying Infected Users via Network Traffic”, Computers & Security, ELSEVIER, Vol. 80, pp. no. 306-316, 2019.

Muhammad Aamir, Syed Mustafa Ali Zaidi, “Clustering based semi-supervised machine learning for DDoS attack classification”, Journal of King Saud University - Computer and Information Sciences, ELSEVIER, 2019.

Tae-YoungKim and Sung-Bae Cho, “Web Traffic Anomaly Detection using C-LSTM Neural Networks”, Expert Systems With Applications, ELSEVIER, Vol. 106, pp. no. 66-76, 2018.

Mohammed Ali Al-Garadi, Amr Mohamed, AbdullaAl-Ali, Xiaojiang Du, Mohsen Guizani, “A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security”, Cryptography and Security, arXiv, 2018.

Alan Saied, Richard E. Overill, Tomasz Radzik, “Detection of known and unknown DDoS attacks using Artificial Neural Networks”, Neurocomputing, ELSEVIER, Vol. 172, pp. no. 385-393, 2016.

Asad Arfeen, Krzysztof Pawlikowski, Don McNickle, Andreas Willig, “The role of the Weibull distribution in modelling traffic in Internet access and backbone core networks”, Journal of Network and Computer Applications, ELSEVIER, Vol. 141, pp. no. 1-22, 2019.

Muhammad Taufiq Zulfikar, Suharjito, “Detection Traffic Congestion Based on Twitter Data using Machine Learning”, Procedia Computer Science, ELSEVIER, Vol. 157, pp. no. 118-124, 2019.

ShakeelAhmad,AmanullahYasin, Qaisar Shafi, “DDoS Attacks Analysis in Bigdata (Hadoop) Environment”, 2018 15th International Bhurban Conference on Applied Sciences and Technology (IBCAST), IEEE, 2018.

Vishal Maheshwari, Ashutosh Bhatia and Kuldeep Kumar, “Faster Detection and Prediction of DDoS attacks using MapReduce and Time Series Analysis”, 2018 International Conference on Information Networking (ICOIN), IEEE, 2018.

Sufian Hameed and Usman Ali, “HADEC: Hadoop-based live DDoSdetection framework”, EURASIP Journal on Information Security, 2018.

Nilesh Vishwasrao Patil, C.Rama Krishna, Krishan Kumar, SunnyBehal, “E-Had: A distributed and collaborative detection framework for early detection of DDoS attacks”, Journal of King Saud University - Computer and Information Sciences, ELSEVIER, 2019.

Awais Ahmed, Sufian Hameed, Muhammad Rafi, Qublai Khan Ali Mirza, “An Intelligent and Time- Efficient DDoS Identification Framework for Real-Time Enterprise Networks”, Cryptography and Security, arXiv, 2020.

Nakul Chorey, Rujuta Kate, Prajakta Khatavkar, Ms. Renuka.R.Kajale, “Detecting, Capturing &Resolving of DDoS Attacks with Hadoop”, IJSRD -International Journal for Scientific Research & Development|, IJSRD, Vol. 6, no. 2, 2018.

M. Sughasiny, “Zero Event Anomaly Detection in Big Data using Spark for Fast and Streaming Applications”, International Journal of Pure and Applied Mathematics, Vol. 119, no. 15, 2018.

Mounir Hafsa and Farah Jemili, “Comparative Study between Big Data AnalysisTechniques in Intrusion Detection”, big data and cognitive computing, MDPI, Vol. 3, no. 1, 2018.



  • There are currently no refbacks.

Copyright (c) 2020 International Journal of Advanced Research in Computer Science