Abstract
The Internet is often victimized to the distributed denial of service (DDoS) attack, in which purposefully occupies the bandwidth and computing resources in order to deny that services to potential users. The attack situation is to flood the packets hugely to the target system. If the attack is from a single source, then the attack is called as denial of service (DoS) and if attack is from divergent servers, then it is called as DDoS. Over a decade, several researchers succeeded to deliver few significant DDoS detection and prevention strategies by considering the detection and prevention of DDoS attack as research objective. In present level of Internet usage, “how fast and early detection of DDoS attack” is done in streaming network transactions which is still a significant research objective. Unfortunately, the current benchmarking DDoS attack detection strategies are failed to justify the objective called “fast and early detection of DDoS attack.” In order to this, we devised an anomaly based real time prevention (ARTP) of application-layer DDoS attacks (App-DDoS attacks) on Web that is in the aim of achieving fast and early detection. The ARTP is a machine learning approach that is used to achieve the fast and early detection of the App-DDoS by multitude request flood. The experiments were carried out on benchmarking LLDoS dataset, and the results delivered are boosting the significance of the proposed model to achieve the objective of the paper.
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K. Munivara Prasad, A.R.R. Reddy, K.V.G. Rao, DoS and DDoS attacks: defense, detection and traceback mechanisms—a survey. Global J. Comput. Sci. Technol. 14(7-E) (2014)
S.M. Lee, in Distributed Denial of Service: taxonomies of Attacks, Tools, and Countermeasures, Proceedings of the international workshop on security in parallel and distributed systems (San Francisco, 2004), pp. 543–550
S. Byers, A.D. Rubin, D. Kormann, Defending against an internet based attack on physical world. ACM Trans. Internet Technol. 239–254 (2004)
J.M. Estevez-Tapiador, P. García-Teodoro, J. Díaz-Verdejo, in Detection of Web-Based Attacks Through Markovian Protocol Parsing, 10th IEEE symposium on computers and communications (2005), pp. 457–462
V. Jyothsna, V.V.R. Prasad, A review of anomaly based intrusion detection systems. Intern. J. Comput. Appl. 26–35 (2013)
T. Yatagai, T. Isohara, I. Sasase, in Detection of HTTP-GET Flood Attack Based on Analysis of Page Access Behaviour, Proceedings IEEE Pacific RIM conference on communications, computers, and signal processing (2007), pp. 232–235
S.S. Sindhu, Decision tree based light weight intrusion detection using a wrapper approach. Expert Syst. Appl. 129–141 (2012)
A. Shevtekar, N. Ansari, Is it congestion or a DDoS attack? IEEE Commun. Letters 546–548 (2009)
S. Kandula, D. Katabi, M. Jacob, A. Berger, in Botz-4-Sale: surviving Organized DDoS Attacks That Mimic Flash Crowds, Proceedings of the 2nd conference on symposium on networked systems design & implementation (2005), pp. 287–300
C. Katar, Combining multiple techniques for intrusion detection. Intern. J. Comput. Sci. Netw. Secur. 208–218 (2006)
Y. Xie, S.Z. Yu, A large-scale hidden semi-markov model for anomaly detection on user browsing behaviors. IEEE/ACM Trans. Netw. 54–65 (2009)
J.A. Hartigan, Algorithm AS 136: “A k-means clustering algorithm”. J. Roy. Stat. Soc.: Ser C (Appl. Stat.) 100–108 (1979)
M.I. MIT, in Darpa Intrusion Detection Evaluation. Retrieved from Lincoln Laboratory: https://www.ll.mit.edu/ideval/data/1998data.html
D.M. Powers, in Evaluation: from Precision, Recall and F-measure to ROC, Informedness, Markedness and Correlation, 23rd international conference on machine learning (Pitsburg, 2006)
V. Jyothsna, V.V. Rama Prasad, Anomaly based network intrusion detection through assessing feature association impact scale (FAIS). Intern. J. Inform. Comput. Secur. (IJICS) (*in forthcoming article). Inderscience (2016)
V. Jyothsna, V.V. Rama Prasad, FCAAIS: anomaly based network intrusion detection through feature correlation analysis and association impact scale (ICT Express, The Korean Institute of Communications Information Sciences, Elsevier, 2016)
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Munivara Prasad, K., Rama Mohan Reddy, A., Venu Gopal Rao, K. (2018). An Experiential Metrics-Based Machine Learning Approach for Anomaly Based Real Time Prevention (ARTP) of App-DDoS Attacks on Web. In: Dash, S., Naidu, P., Bayindir, R., Das, S. (eds) Artificial Intelligence and Evolutionary Computations in Engineering Systems. Advances in Intelligent Systems and Computing, vol 668. Springer, Singapore. https://doi.org/10.1007/978-981-10-7868-2_10
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DOI: https://doi.org/10.1007/978-981-10-7868-2_10
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