DDoS attacks in cloud environment

Shefali Madan; Anita Anita; Ashif Ali

doi:10.53730/ijhs.v6nS4.9457

Authors

Shefali Madan
shefalimadan@gmail.com
Associate Professor, Echelon Institute of Technology, Faridabad
Anita Associate Professor, Echelon Institute of Technology, Faridabad
Ashif Ali Assistant Professor, Echelon Institute of Technology, Faridabad

Keywords:

DDoS, HTTP flood molest, cloud security worries, cloud computing, ping death, slow loris, SYN flood assaults

Abstract

Network communication is gaining day by day in different ways. Cloud is one of the most recent and latest environments in communication. Whereas this environment is a facilitator for the user to access his/her information from anywhere as and when required. But this technological enhancement is also opening the door for new attacks. In this paper, we have conducted an extensive study on the Distributed Denial of Service Attack (DDoS) as well as the techniques which are used up till now for detection as well as prevention of those attacks. We also have thoroughly presented the details of some very frequent techniques and in the end, we have also discussed some research gaps. This study will facilitate the new research in this era to find out the research problems and provide the optimal solutions for those problems.

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References

Yang, L.; Shami, A. A Lightweight Concept Drift Detection and Adaptation Framework for IoT Data Streams. IEEE Internet ThingsMag. 2021, 4, 96–101.

Qaddoura, R.; Al-Zoubi, A.M.; Almomani, I.; Faris, H. A Multi-Stage Classification Approach for IoT Intrusion Detection Basedon Clustering with Oversampling. Appl. Sci. 2021, 11, 3022.

Shi, W.C.; Sun, H.M. DeepBot: A time-based botnet detection with deep learning. Soft. Comput. 2020, 24, 16605–16616.

Nguyen, H.-T.; Ngo, Q.-D.; Le, V.-H. IoT Botnet Detection Approach Based on PSI graph and DGCNN classifier. In Proceedingsof the 2018 IEEE International Conference on Information Communication and Signal Processing (ICICSP), Singapore, 28–30September 2018; pp. 118–122.

McDermott, C.D.; Majdani, F.; Petrovski, A.V. Botnet Detection in the Internet of Things using Deep Learning Approaches. InProceedings of the 2018 International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil, 8–13 July 2018; pp. 1–8.

Stiawan, D.; Suryani, M.E.; Susanto; Idris, M.Y.; Aldalaien, M.N.; Alsharif, N.; Budiarto, R. Ping Flood Attack Pattern RecognitionUsing a K-Means Algorithm in an Internet of Things (IoT) Network. IEEE Access 2021, 9, 116475–116484.

Al-Haija, Q.A.; Smadi, A.A.; Allehyani, M.F. Meticulously Intelligent Identification System for Smart Grid Network Stability toOptimize Risk Management. Energies 2021, 14, 6935.

Chandra, B.E.; Karthikeyan, E. Sigmis: A feature selection algorithm using the correlation-based method. J. Algorithms Comput. Technol. 2012, 6, 385–394.

Singh, D.; Birmohan, S. Investigating the impact of data normalization on classification performance. Appl. Soft Comput. 2020,97, 105524.

Al-Haija, Q.A.; Alsulami, A.A. High-Performance Classification Model to Identify Ransomware Payments for HeterogeneousBitcoin Networks. Electronics 2021.

Abu Al-Haija, Q.; Krichen, M.; Abu Elhaija, W. Machine-Learning-Based Darknet Traffic Detection System for IoT Applications.Electronics 2022, 11, 556.

Stamp, M. A survey of machine learning algorithms and their application in information security. In Guide to Vulnerability Analysisfor Computer Networks and Systems; Springer: Cham, Switzerland, 2018; pp. 33–55.

Timˇcenko, V.; Gajin, S. Ensemble classifiers for supervised anomaly-based network intrusion detection. In Proceedings of the2017 13th IEEE International Conference on Intelligent Computer Communication and Processing (ICCP), Cluj-Napoca, Romania,7–9 September 2017; pp. 13–19.

Gaikwad, D.P.; Thool, R.C. Intrusion detection system using bagging with partial decision tree based classifier. Procedia Comput. Sci.2015, 49, 92–98.

Al-Haija, Q.A.; Ishtaiwi, A. Multiclass Classification of Firewall Log Files Using Shallow Neural Network for Network SecurityApplications. In Soft Computing for Security Applications. Advances in Intelligent Systems and Computing; Ranganathan, G.,Fernando, X., Shi, F., El-Allioui, Y., Eds.; Springer: Singapore, 2022; Volume 1397.

Aygun, R.C.; Yavuz, A.G. Network anomaly detection with stochastically improved autoencoder based models. In Proceedingsof the 4th International Conference on Cyber Security and Cloud Computing, New York, NY, USA, June 2017; pp. 193–198.

Kumar, A.; Lim, T.J. EDIMA: Early detection of IoT malware network activity using machine learning techniques. In Proceedingsof the 2019 IEEE 5th World Forum on Internet of Things (WF-IoT), Limerick, Ireland, 15–18 April 2019; pp. 289–294.

Ioannou, C.; Vassiliou, V. Classifying Security Attacks in IoT Networks Using Supervised Learning. In Proceedings of the 201915th International Conference on Distributed Computing in Sensor Systems (DCOSS), Santorini Island, Greece, 29–31 May2019; pp. 652–658.

Gao, X.; Shan, C.; Hu, C.; Niu, Z.; Liu, Z. An Adaptive Ensemble Machine Learning Model for Intrusion Detection. IEEE Access2019, 7, 82512–82521.

Abu Al-Haija, Q.; Sabatto, S.Z. An Efficient Deep-Learning-Based Detection and Classification System for Cyber-Attacks in IoTCommunication Networks. Electronics 2020, 9, 2152.

Jung, W.; Zhao, H.; Sun, M.; Zhou, G. IoT botnet detection via power consumption modeling. Smart Health 2020, 15, 100103.

Ashraf, J.; Keshk, M.; Moustafa, N.; Abdel-Basset, M.; Khurshid, H.; Bakhshi, A.D.; Mostafa, R.R. IoTBoT-IDS: A novel statisticallearning-enabled botnet detection framework for protecting networks of smart cities. Sustain. Cities Soc. 2021, 72, 103041.

Abu Al-Haija, Q.; al-Badawi, A.; Bojja, G.R. Boost-Defence for resilient IoT networks: A head-to-toe approach. Expert Syst. 2022,39, e12934.

B.S.K. Devi and T. Subbulakshmi, “A Comparative Analysis of Security Methods for DDoS attacks in the Cloud Computing Environment”, Indian Journal of Science and Technology, vol. 9, no. 34, pp. 1-7, 2016.

M. Sebastian, Major DDoS attack on Dyn disrupts AWS Twitter Spotify and more, October 2016.

A. Kumar et al., “Early Detection of Mirai-Like IoT Bots in Large-Scale Networks through Sub-Sampled Packet Traffic Analysis”, 2019.

J. Choi, C. Choi, B. Ko and P. Kim, “A Method of DDoS Attack Detection using HTTP Packet Pattern and Rule Engine in Cloud Computing Environment”, Soft Computing, vol. 18, no. 9, pp. 1697-1703, 2014.

A.S. Navaz, V. Sangeetha and C. Prabhadevi, “Entropy-based Anomaly Detection System to Prevent DDoS Attacks in Cloud”, International Journal of Computer Applications, vol. 62, no. 14, 2013.

V. Chouhan and S.K. Peddoju, “Packet Monitoring Approach to Prevent DDoS Attack in Cloud Computing”, International Journal of Computer Science and Electrical Engineering, vol. 1, no. 1, pp. 38-42, 2012.

W. Dou, Q. Chen and J. Chen, “A confidence-based filtering method for DDoS attack defense in cloud environment”, Future Generation. Systems., vol. 29, no. 7, pp. 1838-1850, 2013.

P. Shamsolmoali and M. Zareapoor, “Statistical-based filtering system against DDOS Attacks in cloud computing”, Proceedings of the International Conference on Advances in Computing Communications and Informatics, pp. 1234-1239, 2014.

F.A. Guenane, B. Jaafar, M. Nogueira, and G. Pujolle, “Autonomous architecture for managing firewalling cloud-based service”, Proceedings of the International Conference and Workshop on the Network of the Future, pp. 1-5, 2014.

Kshirsagar, Deepak, and Sandeep Kumar. "A feature reduction based reflected and exploited DDoS attacks detection system." Journal of Ambient Intelligence and Humanized Computing 13, no. 1 (2022): 393-405.

Kautish, Sandeep, A. Reyana, and Ankit Vidyarthi. "SDMTA: Attack Detection and Mitigation Mechanism for DDoS Vulnerabilities in Hybrid Cloud Environment." IEEE Transactions on Industrial Informatics (2022).

Gaur, Vimal, and Rajneesh Kumar. "Analysis of Machine Learning Classifiers for Early Detection of DDoS Attacks on IoT Devices." Arabian Journal for Science and Engineering 47, no. 2 (2022): 1353-1374.

Aziz, Israa T., Ihsan H. Abdulqadder, and Thakwan A. Jawad. "Distributed Denial of Service Attacks on Cloud Computing Environment‎." Cihan University-Erbil Scientific Journal 6, no. 1 (2022): 47-52.

Raich, Anagha, and Vijay Gadicha. "Overview of passive attacks in cloud environment." In AIP Conference Proceedings, vol. 2424, no. 1, p. 030004. AIP Publishing LLC, 2022.

Chaudhary, Deepali, Kriti Bhushan, and Brij B. Gupta. "Survey on DDoS attacks and defense mechanisms in cloud and fog computing." International Journal of E-Services and Mobile Applications (IJESMA) 10, no. 3 (2018): 61-83.

Aydın, Hakan, Zeynep Orman, and Muhammed Ali Aydın. "A Long Short-Term Memory (LSTM)-Based Distributed Denial of Service (DDoS) Detection and Defense System Design in Public Cloud Network Environment." Computers & Security (2022): 102725.

Agrawal, Ankit, Rajiv Singh, Manju Khari, S. Vimal, and Sangsoon Lim. "Autoencoder for Design of Mitigation Model for DDOS Attacks via M-DBNN." Wireless Communications and Mobile Computing 2022 (2022).

Nyandra, M., Suryasa, W. (2018). Holistic approach to help sexual dysfunction. Eurasian Journal of Analytical Chemistry, 13(3), pp. 207–212.

Suryasa, W. (2019). Historical Religion Dynamics: Phenomenon in Bali Island. Journal of Advanced Research in Dynamical and Control Systems, 11(6), 1679-1685.

Wijayanti, N. (2021). Factors related to behavior the community in disposing of garbage. International Journal of Health & Medical Sciences, 4(1), 74-79. https://doi.org/10.31295/ijhms.v4n1.1226