A Novel Framework for Detection of DoS/DDoS Attack Using Deep Learning Techniques, and An Approach to Mitigate the Impact of DoS/DDoS attack in Network Environment
Keywords:
Classification, Dataset, MLP, NSL-KDD, Scapy, WiresharkAbstract
DoS attacks are a major network security issue. The internet and computer networks are essential to our daily lives and businesses. With our reliance on computers and communication networks, harmful actions have increased. Network hazards plague modern communication. To keep networks running smoothly and users' data safe, network traffic flow must be monitored for malicious activity and assaults. Denial-of-service (DoS) attacks aim to disrupt a network server, website, or web service. Computer networks and services are vulnerable to DoS and DDoS attacks. Flooding may be the simplest DDoS assault. DDoS attacks transmit massive amounts of useless data to a network or server. The study seeks to strengthen network infrastructures against various threats, maintain service continuity, and secure the network. Denial-of-service (DoS) attacks prevent legitimate users from accessing and using information systems and resources. Figure B shows DoS/DDoS attacks using ICMP, UDP, and the more prevalent TCP flood assaults. These strikes must be detected and stopped immediately. Businesses and schools went online during COVID-19. Because so much data is created and stored, traditional Machine Learning-based DoS/DDoS attack detection approaches are ineffective. This study uses SVM, MLP, and LSTM algorithms for Deep Learning. The proposed Deep Learning model learns and builds binary and multiclass classification models that can distinguish network attack activity from normal traffic. We look for outliers and attack signals in traffic patterns and data. Our deep learning model is studied with accuracy and precision. In detection, the system checks for attack or regular network data. MLP Algorithm helps this model discover items 97% of the time. LSVM ML classification compares the suggested system's performance. This paper examines traffic behavior. This study also used traffic filtering to eliminate suspicious or attack-signature traffic. Next, we limited traffic from specified sources and locations using rate-limiting. Python SCAPY and wireshark Sniffer in Linux OS capture network packet data for analysis and repair. Compared wireshark with scapy packet capturing analysis and mitigation. This study examines network DoS/DDoS assaults and their prevention. These approaches detect and mitigate flood-based DoS assaults to keep systems functioning and networks safe. To keep up with DoS assaults and the threat landscape, you must continually studying and developing new tactics.
Downloads
References
Perez-Diaz, Valdovinos, Choo, Zhu (2020). A Flexible SDN-Based Architecture for Identifying and Mitigating Low-Rate DDoS Attacks Using Machine Learning. IEEE Access, (8), 155859-155872. https://doi.org/10.1109/access.2020.3019330
Phan, Park (2019). Efficient Distributed Denial-of-Service Attack Defense in SDN-Based Cloud. IEEE Access, (7), 18701-18714. https://doi.org/10.1109/access.2019.2896783
[Dong, Sarem (2020). DDoS Attack Detection Method Based on Improved KNN With the Degree of DDoS Attack in Software-Defined Networks. IEEE Access, (8), 5039-5048. https://doi.org/10.1109/access.2019.2963077
Sambangi, Gondi (2020). A Machine Learning Approach for DDoS (Distributed Denial of Service) Attack Detection Using Multiple Linear Regression.. https://doi.org/10.3390/proceedings2020063051
B. Agarwal and N. Mittal, “Hybrid Approach for Detection of Anomaly Network Traffic using Data Mining Techniques,” Procedia Technol., vol. 6, pp. 996–1003, 2012, doi: 10.1016/j.protcy.2012.10.121.
A. Aljuhani, “Machine Learning Approaches for Combating Distributed Denial of Service Attacks in Modern Networking Environments,” IEEE Access, vol. 9, pp. 42236–42264, 2021, doi: 10.1109/ACCESS.2021.3062909.
Y. Wei, J. Jang-Jaccard, F. Sabrina, A. Singh, W. Xu, and S. Camtepe, “AE-MLP: A Hybrid Deep Learning Approach for DDoS Detection and Classification,” IEEE Access, vol. 9, pp. 146810–146821, 2021, doi: 10.1109/ACCESS.2021.3123791.
M. Zekri, S. El Kafhali, N. Aboutabit, and Y. Saudi, “DDoS attack detection using machine learning techniques in cloud computing environments,” Proc. 2017 Int. Conf. Cloud Comput. Technol. Appl. CloudTech 2017, vol. 2018-Janua, pp. 1–7, 2018, doi: 10.1109/CloudTech.2017.8284731.
Wankhede and D. Kshirsagar, “DoS Attack Detection Using Machine Learning and Neural Network,” Proc. - 2018 4th Int. Conf. Comput.Commun. Control Autom. ICCUBEA 2018, 2018, doi: 10.1109/ICCUBEA.2018.8697702.
X. Yuan, C. Li, and X. Li, “DeepDefense: Identifying DDoS Attack via Deep Learning,” 2017 IEEE Int. Conf. Smart Comput. SMARTCOMP 2017, pp. 1–8, 2017, doi: 10.1109/SMARTCOMP.2017.7946998.
M. Tayyab, B. Belaton, and M. Anbar, “ICMPV6-based DOS and DDoS attacks detection using machine learning techniques, open challenges, and blockchain applicability: A review,” IEEE Access, vol. 8, pp. 170529–170547, 2020, doi: 10.1109/ACCESS.2020.3022963.
M. Barati, A. Abdullah, N. I. Udzir, and ..., “Distributed Denial of Service detection using hybrid machine learning technique,” Biometrics …, pp. 268–273, 2014, [Online]. Available: https://ieeexplore.ieee.org/abstract/document/7013133/
B. Zhou, J. Li, J. Wu, S. Guo, Y. Gu, and Z. Li, “Machine-learning-based online distributed denial-of-service attack detection using spark streaming,” IEEE Int. Conf. Commun., vol. 2018-May, 2018, doi: 10.1109/ICC.2018.8422327.
A. R. A. Yusof, N. I. Udzir, A. Selamat, H. Hamdan, and M. T. Abdullah, “Adaptive feature selection for denial of services (DoS) attack,” 2017 IEEE Conf. Appl. Inf. Netw. Secur. AINS 2017, vol. 2018-Janua, pp. 81–84, 2017, doi: 10.1109/AINS.2017.8270429.
O. Rahman, M. A. G. Quraishi, and C. H. Lung, “DDoS attacks detection and mitigation in SDN using machine learning,” Proc. - 2019 IEEE World Congr. Serv. Serv. 2019, vol. 2642–939X, pp. 184–189, 2019, doi: 10.1109/SERVICES.2019.00051.
P. Shamsolmoali and M. Zareapoor, “Statistical-based filtering system against DDOS attacks in cloud computing,” Proc. 2014 Int. Conf. Adv. Comput.Commun. Informatics, ICACCI 2014, pp. 1234–1239, 2014, doi: 10.1109/ICACCI.2014.6968282.
Ö. ASLAN, “A Methodology to Detect Distributed Denial of Service Attacks,” BilişimTeknol.Derg., vol. 15, no. 2, pp. 149–158, 2022, doi: 10.17671/gazibtd.1002178.
S. Nandi, S. Phadikar, and K. Majumder, “Detection of DDoS Attack and Classification Using a Hybrid Approach,” ISEA-ISAP 2020 - Proc. 3rd ISEA Int. Conf. Secur. Priv. 2020, pp. 41–47, 2020, doi: 10.1109/ISEA-ISAP49340.2020.234999.
S. Sheng, C. Wu, and X. Dong, “Research on Visualization Systems for DDoS Attack Detection,” Proc. - 2018 IEEE Int. Conf. Syst. Man, Cybern. SMC 2018, pp. 2986–2991, 2019, doi: 10.1109/SMC.2018.00507.
F. S. De Lima Filho, F. A. F. Silveira, A. De Medeiros Brito Junior, G. Vargas-Solar, and L. F. Silveira, “Smart Detection: An Online Approach for DoS/DDoS Attack Detection Using Machine Learning,” Secur. Commun. Networks, vol. 2019, 2019, doi: 10.1155/2019/1574749.
M. Kozlowski and B. Ksiezopolski, “A new method of testing machine learning models of detection for targeted DDoS attacks,” Proc. 18th Int. Conf. Secur.Cryptogr.SECRYPT 2021, no.Secrypt, pp. 728–733, 2021, doi: 10.5220/0010574507280733.
Y. Tao and S. Yu, “DDoS attack detection at local area networks using information theoretical metrics,” Proc. - 12th IEEE Int. Conf. Trust.Secur. Priv. Comput. Commun.Trust. 2013, pp. 233–240, 2013, doi: 10.1109/TrustCom.2013.32.
S. Peneti and Hemalatha, “DDOS Attack Identification using Machine Learning Techniques,” 2021 Int. Conf. Comput.Commun. Informatics, ICCCI 2021, 2021, doi: 10.1109/ICCCI50826.2021.9402441.
Y. Khosroshahi and E. Ozdemir, “Detection of Sources Being Used in DDoS Attacks,” Proc. - 6th IEEE Int. Conf. Cyber Secur. Cloud Comput. CSCloud 2019 5th IEEE Int. Conf. Edge Comput. Scalable Cloud, EdgeCom 2019, pp. 163–168, 2019, doi: 10.1109/CSCloud/EdgeCom.2019.000-1.
C. M. Bao, “Intrusion detection based on one-class SVM and SNMP MIB data,” 5th Int. Conf. Inf. Assur. Secur. IAS 2009, vol. 2, pp. 346–349, 2009, doi: 10.1109/IAS.2009.124.
T. Shon, Y. Kim, C. Lee, and J. Moon, “A machine learning framework for network anomaly detection using SVM and GA,” Proc. from 6th Annu.IEEE Syst. Man Cybern. Inf. Assur. Work. SMC 2005, vol. 2005, pp. 176–183, 2005, doi: 10.1109/IAW.2005.1495950.
G. SankaraRao et al., "Security Attacks DoS/DDoS attack Detection in Networks," NeuroQuantology, vol. 20, no. 11, pp. 8452-8463, Sep. 2022, doi: 10.48047/nq.2022.20.11.NQ66839.
J. Galeano-Brajones, J. Carmona-Murillo, J. Valenzuela-Valdes, F. Luna, "Detection and Mitigation Of Dos And Ddos Attacks In Iot-based StatefulSdn: An Experimental Approach", Sensors, vol. 20, no. 3, p. 816, 2020. https://doi.org/10.3390/s20030816
R. Chaganti, B. Bhushan, R. Vinayakumar, "The Role Of Blockchain In Ddos Attacks Mitigation: Techniques, Open Challenges and Future Directions",, 2022. https://doi.org/10.48550/arxiv.2202.03617
F. Alhaidari, A. Alrehan, "A Simulation Work For Generating a Novel Dataset To Detect Distributed Denial Of Service Attacks On Vehicular Ad Hoc Network Systems", International Journal of Distributed Sensor Networks, vol. 17, no. 3, p. 155014772110002, 2021. https://doi.org/10.1177/15501477211000287
R. Abubakar, X. Huang, M. Javed, "An Intelligent Agent-based Detection System ForDdos Attacks Using Automatic Feature Extraction and Selection", Sensors, vol. 23, no. 6, p. 3333, 2023. https://doi.org/10.3390/s23063333
J. Zhang, L. Qidi, R. Jiang, X. Li, "A Feature Analysis Based Identifying Scheme Using Gbdt For Ddos With Multiple Attack Vectors", Applied Sciences, vol. 9, no. 21, p. 4633, 2019. https://doi.org/10.3390/app9214633
M. Khandelwal, D. Gupta, P. Bhale, "Dos Attack Detection Technique Using Back Propagation Neural Network",, 2016. https://doi.org/10.1109/icacci.2016.7732185
B. Bousalem, V. Silva, R. Langar, S. Cherrier, "Deep Learning-based Approach For Ddos Attacks Detection and Mitigation In 5g And Beyond Mobile Networks",, 2022. https://doi.org/10.1109/netsoft54395.2022.9844053
A. Chartuni, J. Márquez, "Multi-classifier Of Ddos Attacks In Computer Networks Built On Neural Networks", Applied Sciences, vol. 11, no. 22, p. 10609, 2021. https://doi.org/10.3390/app112210609
M. M. Rasheed, A. K. Faieq, and A. A. Hashim, "Development of a new system to detect denial of service attack using machine learning classification," Indonesian Journal of Electrical Engineering and Computer Science, vol. 23, no. 2, pp. 1068-1072, Aug. 2021.
DOI: 10.11591/ijeecs.v23.i2.pp1068-1072.
M. Alkasassbeh, et al., "Detecting distributed denial of service attacks using data mining techniques," International Journal of Advanced Computer Science and Applications, vol. 7, no. 1, pp. 128-134, 2016
A. Bonguet and M. Bellaiche, "A Survey Of Denial-of-service and Distributed Denial Of Service Attacks And Defenses In Cloud Computing," Future Internet, vol. 3, no. 9, p. 43, 2017. [Online]. Available: https://doi.org/10.3390/fi9030043
R. Doriguzzi-Corin, S. Millar, S. Scott-Hayward, J. Rincon, D. Siracusa, "Lucid: a Practical, Lightweight Deep Learning Solution For Ddos Attack Detection", IEEE Trans. Netw. Serv. Manage., vol. 17, no. 2, p. 876-889, 2020. https://doi.org/10.1109/tnsm.2020.2971776
M. Hefeeda, A. Habib, "Detecting Dos Attacks and Service Violations In Qos-enabled Networks", Handbook of Security and Networks, p. 191-220, 2011. https://doi.org/10.1142/9789814273046_0007
G. S. Rao et al., "A Novel Approach for Detection of DoS / DDoS Attack in Network Environment using Ensemble Machine Learning Model," Int. J. Recent Innov. Trends Comput. Commun., vol. 11, no. 9, pp. Page Numbers, 2023. doi: 10.17762/ijritcc.v11i9.8340.
B. Hari Krishna, "A Soft Voting Classifier based Intrusion Detection for Network Security Enhancement," Industrial Engineering Journal, vol. 52, no. 6, pp. 1-6, June 2023.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
