Volume 15 , Issue 1 , PP: 365-384, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Alaa Q. Raheema 1 *
Doi: https://doi.org/10.54216/JCIM.150129
DoS (denial of service) attacks address a remarkable new risk to cloud services and can really hurt cloud providers and their clients. DoS attacks can similarly achieve lost pay and security vulnerabilities due to system crashes, service power outages, and data breaks. Regardless, despite the fact that machine learning methods are the subject of assessment to distinguish DoS attacks, there has not been a ton of progress around here. In like manner, additional investigation is expected around here to make the best models for perceiving DoS attacks in cloud conditions. This change is proposed to search for a significant convolutional generative-arranged network as a significant learning model given further creating DoS attacks in the cloud. A proposed model of significant learning organizations (RNN) is used to fathom the spatiotemporal objects of organization traffic data, hence tracking down different models that show DoS attacks. Plus, to make RNN-LSTM all the more obvious for defending against attacks, it is acquired from a broad assortment of organization opportunity data. In addition, the model is dealt with by in reverse joint exertion and stochastic slope drop is the way into the current effortlessness of scaling among clear and saw traffic volumes. Test results show that the proposed model beats the latest particular attacks, relies upon denial of service, and undoubtedly shows misleading positive results.
DOS Attack , Cloud Networks , Recurrence Neural Networks (RNN-LSTM) , Attack Detection , Intrusion Detection System
[1] Agarwal, N.; Hussain, S. Z. (2018). A Closer Look at Intrusion Detection System for Web Applications. Security and Communication Networks, 2018, pp 1-27.
[2] Darch Abed Dawar, A. (2024). Enhancing Wireless Security and Privacy: A 2-Way Identity Authentication Method for 5G Networks. International Journal of Mathematics, Statistics, and Computer Science, 2, 183–198. https://doi.org/10.59543/ijmscs.v2i.9073
[3] Somani, G.; Gaur, M. S.; Sanghi, D.; Conti, M.; Buyya, R. (2017). DDoS attacks in cloud computing: Issues, taxonomy, and future directions. Computer Communications 107, pp. 30-48..
[4] Firas Mahdi Muhsin Al-Salbi, “Investigation of QoS Multicast Routing Based on Intelligent Multiple Constrained”, www.ccsenet.org/cis Computer and Information Science Vol. 4, No. 4; July 2011, 64 ISSN 1913-8989 E-ISSN 1913-8997
[5] Deshpande, P.; Sharma, S. C.; Peddoju, S. K.; Junaid. S. (2018). HIDS: A host based intrusion detection system for cloud computing environment. International Journal of System Assurance Engineering and Management 9(3), pp. 567-576.
[6] Seyed Mohammad Mousavi and Marc St-Hilaire, (2019). Early detection of DDoS attacks against SDN controllers. In Computing, Networking and Communications (ICNC), 2019 International Conference on, pages 77–81. IEEE.
[7] Jun Li, (2018). Drawbridge: software-defined ddos-resistant traffic engineering. In ACM SIGCOMM Computer Communication Review, volume 44, pages 591–592. ACM.
[8] Li, S. H., Kao, Y. C., Zhang, Z. C., Chuang, Y. P., & Yen, D. C. (2015). A network behavior-based botnet detection mechanism using PSO and K-means, ACM Transactions on Management Information Systems (TMIS), 6(1), 3.
[9] Prasad, K. M., Reddy, A. R. M., & Rao, K. V. (2018). DoS and DDoS attacks: defense, detection and traceback mechanisms-a survey. Global Journal of Computer Science and Technology.
[10] Aggarwal, A., & Gupta, A. (2015). Survey on data mining and IP traceback technique in DDoS attack. International Journal of Engineering and Computer Science, 4(06).
[11] Somani, G., Gaur, M. S., Sanghi, D., Conti, M., & Buyya, R. (2017). DDoS attacks in cloud computing: Issues, taxonomy, and future directions. Computer Communications, 107, 30-48.
[12] Kostas Giotis, et. al., (2017). Combining openflow and sflow for an effective and scalable anomaly detection and mitigation mechanism on sdn environments. Computer Networks, 62:122–136, 2017.
[13] Javed Ashraf and Seemab Latif, (2018 ). Handling intrusion and DDoS attacks in software defined networks using machine learning techniques. In Software Engineering Conference (NSEC), National, pages 55–60. IEEE, 2014.
[14] Damian Jankowski and Marek Amanowicz, (2018). Intrusion detection in software defined networks with self-organized maps. Journal of Telecommunications and Information Technology.
[15] Bing Wang, et al (2019). Ddos attack protection in the era of cloud computing and softwaredefined networking. Computer Networks, 81:308–319.
[16] Hamid TABATABAEE, et al., (2019). Dynamic task scheduling modeling in unstructured heterogeneous multiprocessor systems, Journal of Zhejiang university – Science (Computers & Electronics) Vol.15, No.6, pp 423 – 434.
[17] Uma Boregowda and Venugopal R Chakravarthy, (2018). A Hybrid Task Scheduler for DAG Applications on A Cluster of Processors, Fourth International Conference on Advances in computing and communications, Vol. 10, pp.143-146, August 2018.
[18] Jing Liu, et al. (2018). Minimizing system cost with efficient task assignment on heterogeneous multicore processors considering time constraint, IEEE Transactions on parallel and distributed systems, Vol.2, .No.8 ,August 2018.
[19] Vinaykumar, et al., (2019). A Novel Task Scheduling Algorithm for Heterogeneous computing, International Journal of Computer Applications, Vol.85, No.18, pp. January 2019.
[20] YAGOUBI Belabbas and KADRI Walid, (2019). Optimized Scheduling Approach for Scientific Applications Based on Clustering in Cloud Computing Environment, September 2019, Scalable Computing 20(3):527-540, DOI: 10.12694/scpe.v20i3.1548
[21] Wang, et al., (2019). Reliability-Driven Reputation Based Scheduling for Public-Resource Computing Using GA, Conference: The IEEE 23rd International Conference on Advanced Information Networking and Applications, AINA 2019, Bradford, United Kingdom, May 26-29.
[22] Nasr, et. al., (2018). Performance Enhancement of Scheduling Algorithm in Heterogeneous Distributed Computing Systems, June 2015 ,International Journal of Advanced Computer Science and Applications 06(05):88-96, DOI: 10.14569/IJACSA.2015.060514, License CC BY-NC-ND 4.0.
[23] Chronaki, Riko and Badia, (2015). Criticality-Aware Dynamic Task Scheduling for Heterogeneous Architectures, June 2015, DOI: 10.1145/2751205.2751235, Conference: the 29th ACM.
[24] David, et al., (2021).Detection of Denial of Service Attack (DOS), April 2021, Project: Detection of Denial of Service Attack.
[25] Mazhar Javed Awan, et al., (2021). Real-Time DDoS Attack Detection System Using Big Data Approach, Sustainability 2021, 13, 10743. https://doi.org/10.3390/su131910743, https://www.mdpi.com/journal/sustainability.
[26] Kushwah, G. S.; Ali, S. T. (2017). Detecting DDoS attacks in cloud computing using ANN and black hole optimization. In: 2nd International Conference on Telecommunication and Networks (TEL-NET), IEEE 2017, pp. 1-5.
[27] Ramin Fadaei Fouladi, et al., (2018). Statistical Measures: Promising Features for Time Series Based DDoS Attack Detection, Proceedings 2018, 2, 96; doi:10.3390/proceedings2020096 www.mdpi.com/journal/proceedings.
[28] D.J. Bernstein et al., (2020), Cryptographic Competitions, (DFG, German Research Foundation) under Germany's Excellence Strategy-EXC 2002 CASA 390781972 "Cyber Security in the Age of Large-Scale Adversaries", by the U.S. Na tional Science Foundation under grant 1913167, and by the Cisco University Research Program. "Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation" (or other funding agencies). Permanent ID of this document: b7af715576cc229aaf8c532ea89bb6acelc91a65. Date: 2020.12.25.
[29] Rajeev Singh and T. P. Sharma , (2020). Present Status of Distributed Denial of Service (DDoS) Attacks in Internet World, August 2019, International Journal of Mathematical, Engineering and Management Sciences 4(4):1008-1017.
[30] Zonayed Ahmed et al., (2017). Defense against SYN Flood Attack using LPTR-PSO: A Three Phased Scheduling Approach, January 2017, International Journal of Advanced Computer Science and Applications 8(9):433-441, DOI: 10.14569/IJACSA.2017.080957
[31] He, X.; Dai, H.; Ning, P. (2016). Faster learning and adaptation in security games by exploiting information asymmetry. IEEE Transactions on Signal Processing 64(13), pp. 3429-3443.
[32]Kajal, A.; Nandal, S. K. (2019). A Hybrid Algorithm using neural network and artificial bee colony for cyber security threats. International Journal of Innovative Technology and Exploring Engineering, 8(12), pp. 1-6.
[33] K. Igor and A. Ulanov, “Agent-based simulation of DDOS attacks and defense mechanisms,” International Journal of Computing vol. 4, pp. 113-123, 2014.
[34] K. Sharma and B. Gupta, “Taxonomy of Distributed Denial of Service (DDoS) Attacks and Defense Mechanisms in Present Era of Smartphone Devices, “International Journal of E-Services and Mobile Applications (IJESMA),”vol. 10, pp. 58-74, 2018.
[35] A. Saied, R. Overill and T. Radzik, “Detection of known and unknown DDoS attacks using Artificial Neural Networks,” Neurocomputing, vol. 172, pp. 385-393, 2016.
[36] Abhishek Kajal, Sunil Kumar Nandal, “A Hybrid Approach For Cyber Security: Improved Intrusion Detection System Using ANN-SVM, August 2020, Indian Journal of Computer Science and Engineering 11(4): DOI:10.21817/indjcse/2020/v11i4/201104300.
[37] N. Z., Bawany, J. A., Shamsi & K. Salah, “DDoS attack detection and mitigation using SDN: methods, practices, and solutions “. Arabian Journal for Science and Engineering, 42(2), 425-441, 2017.
[38] J. Ye, Cheng, X., J. Zhu, L. Feng & L. Song, “A DDoS attack detection method based on SVM in software defined network. Security and Communication Networks, 2018.
[39] Tsai C. F.; Hsu, Y. F.; Lin, C. Y.; Lin, W. Y. (2019). Intrusion detection by machine learning: A review. Expert Systems with Applications, 36(10), pp. 11994–12000.
[40] Lima F.; de, F. S.; Silveira, F. A.; Junior, A. D. M. B.; Solar, G. V.; Silveira, L. F. (2019). Smart detection: an online approach for DoS/DDoS attack detection using machine learning. Security and Communication Networks 2019.
[41] Watson, M. R.; Marnerides, A. K.; Mauthe, A.; Hutchison, D. (2018). Malware detection in cloud computing infrastructures. IEEE Transactions on Dependable and Secure Computing 13(2), pp. 192-205.
[42] Hosseini, S.; Azizi, M. (2019). The hybrid technique for DDoS detection with supervised learning algorithms. Computer Networks 158, pp. 35-45.
[43] Velliangiri, S.; Karthikeyan, P.; Kumar, V. (2020). Detection of distributed denial of service attack in cloud computing using the optimization-based deep networks. Journal of Experimental & Theoretical Artificial Intelligence, pp. 1-20.
