Abstract
The distributed denial-of-service (DDOS) exploit is one of the most catastrophic assaults on the Internet, disrupting the performance of critical administrations offered by numerous organizations. These attacks have become increasingly complicated, and their number has been steadily increasing, making it harder to detect and respond to such assaults As a result, a sharp security system (IDS) is necessary to detect and control any unexpected system traffic behavior. In a DDOS Assaults, the intruder delivers a stream of packets to the server while exploiting known or unknown flaws and vulnerabilities.
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References
Li H, Liu D (2010) Research on intelligent intrusion prevention system based on snort. In: International conference on computer, mechatronics, control and electronic engineering (CMCE), vol 1. IEEE, pp 251–253
Li J, Liu Y, Gu L (2010) DDos attack detection based on neural network. In: 2nd international symposium on aware computing (ISAC). IEEE, pp 196–199
Suresh M, Anitha R (2011) Evaluating machine learning algorithms for detecting DDoS attacks. Commun Comput Inform Sci 441–452. https://doi.org/10.1007/978-3-642-22540-6_42
Livadas C, Walsh R, Lapsley DE, Strayer WT (2006) Using machine learning techniques to identify botnet traffic. In: Proceedings of 2006 31st IEEE conference on local computer networks, pp 967–974
Suresh M, Anitha R (2011) Evaluating machine learning algorithms for detecting DDoS attacks. In: Wyld DC, Wozniak M, Chaki N, Meghanathan N, Nagamalai D (eds) Advances in network security and applications. CNSA 2011. Communications in computer and information science, vol 196. Springer, Berlin, Heidelberg
Wu Y-C, Tseng H-R, Yang W, Jan R-H (2011) DDoS detection and traceback with decision tree and grey relational analysis. Int J Ad Hoc Ubiquitous Comput 7(2)
Yusof AR, Udzir NI, Selamat A (2016) An evaluation on KNN-SVM algorithm for detection and prediction of DDoS attack. In: Fujita H, Ali M, Selamat A, Sasaki J, Kurematsu M (eds) Trends in applied knowledge-based systems and data science. IEA/AIE 2016. Lecture notes in computer science, vol 9799. Springer, Cham
Zekri M, El Kafhali S, Aboutabit N, Saadi Y (2017, October) DDoS attack detection using machine learning techniques in cloud computing environments. In: 2017 3rd international conference of cloud computing technologies and applications (CloudTech). IEEE, pp 1–7
Bujlow T, Riaz T, Pedersen JM (2012, January) A method for classification of network traffic based on C5.0 machine learning algorithm. In: 2012 international conference on computing, networking and communications (ICNC). IEEE, pp 237–241
Pandya R, Pandya J (2015) Article: C5.0 algorithm to improved decision tree with feature selection and reduced errorpruning. Int J Comput Appl 117(16):18–21
Bhuyan MH, Bhattacharyya DK, Kalita JK (2011) Surveying port scans and their detection methodologies. Comput J 54:1565–1581
Das V, Pathak V, Sharma S, Sreevathsan, Srikanth MVVNS, Gireesh Kumar T (2010) Network intrusion detection system based on machine learning algorithms. Int J Comput Sci Inform Technol (IJCSIT) 2(6)
Alkasassbeh M, Al-Naymat G, Hassanat ABA, Almseidin M (2016) Detecting distributed denial of service attacks using data mining techniques. Int J Adv Comput Sci Appl (IJACSA) 7(1)
Bains JK, Kaki KK, Sharma K (2013) Intrusion detection system with multi layer using Bayesian networks. Int J Comput Appl 67(5). ISSN 0975-8887
Gavrilis D, Dermatas E (2005) Real-time detection of distributed denial-of-service attacks using RBF networks and statistical features. Comput Netw 48:235–245. https://doi.org/10.1016/j.comnet.2004.08.014
Sofi I, Mahajan A, Mansotra V (2017) Machine learning techniques used for the detection and analysis of modern types of DDoS attacks. IRJET 4(6)
Balsrengadurali C, Saraswathi S (2013) Fuzzy based detection and prediction of DDoS attacks in IEEE 802.15.4 low rate wireless personal area network. IJCSI Int J Comput Sci 10(6)(1)
Bains JK, Kaki KK, Sharma K (2013) Intrusion detection system with multi-layer using Bayesian networks. Int J Comput Appl 67(5). ISSN 0975-8887
Ismanto H, Wardoyo R (2016) Comparison of running time between c4.5 and k-nearest neighbor (k-nn) algorithm on deciding mainstay area clustering. Int J Adv Intell Inform 2(1):1–6
Hariharan M, Abhishek HK, Prasad BG (2019) DDoS attack detection using C5.0 machine learning algorithm. Int J Wirel Microw Technol (IJWMT) 9(1):52–59
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Jeevan Pradeep, K., Mishra, P. (2023). A Survey of Learning Techniques for Detecting DDOS Assaults. In: Fong, S., Dey, N., Joshi, A. (eds) ICT Analysis and Applications. Lecture Notes in Networks and Systems, vol 517. Springer, Singapore. https://doi.org/10.1007/978-981-19-5224-1_14
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