An improved rule induction based denial of service attacks classification model

Abstract For assessing the quality of any internet and cloud computing services; accessibility is presusmed a significant factor among other Quality of Service (QoS) factors. Distributed Denial of Service attack (DDoS) is considered a significant threat pertaining to all contemporary and emerging online-based services. Intelligent solutions centered on the utilization of data mining methods are looming on the horizon as possible solutions to counter this kind of attacks. Rule Induction (RI), which is a well-known data mining method is regarded as a possible approach for developing an intelligent DDoS detection system. The current article offers an “Improved RI algorithm” (IRI) which decreases the searching space for generating classification rules by removing all unimportant candidate rule-items along the way of creating the classification model. The main advantage of IRI is producing a group of rules that can be described as concise, easy to understand, and easy-to-implement. In addition, the classifiers generated by IRI are more compact in size which is heavily weighted when producing any classification system. The proposed algorithm is then applied for detecting DDoS attacks (IRIDOS). Empirical evaluations using the UNSW-NB15 dataset that has been obtained from the University of New South Wales confirmed the robustness of IRIDOS.

[1]  Shin-Ming Cheng,et al.  DDoS Attacks in Experimental LTE Networks , 2020, AINA Workshops.

[2]  Vikash Kumar,et al.  Statistical Analysis of the UNSW-NB15 Dataset for Intrusion Detection , 2019, Computational Intelligence in Pattern Recognition.

[3]  Rami Mustafa A. Mohammad,et al.  A lifelong spam emails classification model , 2020, Applied Computing and Informatics.

[4]  Wuu Yang,et al.  DDoS Detection and Traceback with Decision Tree and Grey Relational Analysis , 2009, 2009 Third International Conference on Multimedia and Ubiquitous Engineering.

[5]  Sherali Zeadally,et al.  Using Cloud Computing to Implement a Security Overlay Network , 2013, IEEE Security & Privacy.

[6]  Fouad Amine Guenane,et al.  DDOS Mitigation Cloud-Based Service , 2015, 2015 IEEE Trustcom/BigDataSE/ISPA.

[7]  Rodrigo Braga,et al.  Lightweight DDoS flooding attack detection using NOX/OpenFlow , 2010, IEEE Local Computer Network Conference.

[8]  Shahrzad Zargari,et al.  Feature selection in UNSW-NB15 and KDDCUP'99 datasets , 2017, 2017 IEEE 26th International Symposium on Industrial Electronics (ISIE).

[9]  Khaled Salah.  A Queueing Model to Achieve Proper Elasticity for Cloud Cluster Jobs , 2013, 2013 IEEE Sixth International Conference on Cloud Computing.

[10]  Xin Xu,et al.  Defending DDoS Attacks Using Hidden Markov Models and Cooperative Reinforcement Learning , 2007, PAISI.

[11]  Khaled Salah,et al.  IoT security: Review, blockchain solutions, and open challenges , 2017, Future Gener. Comput. Syst..

[12]  Akihiro Nakao,et al.  DDoS defense as a network service , 2010, 2010 IEEE Network Operations and Management Symposium - NOMS 2010.

[13]  Rami Mustafa A. Mohammad,et al.  A Neural Network based Digital Forensics Classification , 2018, 2018 IEEE/ACS 15th International Conference on Computer Systems and Applications (AICCSA).

[14]  Ajith Abraham,et al.  A Profile Based Network Intrusion Detection and Prevention System for Securing Cloud Environment , 2013, Int. J. Distributed Sens. Networks.

[15]  Lei Wei,et al.  FlowRanger: A request prioritizing algorithm for controller DoS attacks in Software Defined Networks , 2015, 2015 IEEE International Conference on Communications (ICC).

[16]  Nur Izura Udzir,et al.  A Cloud-based Intrusion Detection Service framework , 2012, Proceedings Title: 2012 International Conference on Cyber Security, Cyber Warfare and Digital Forensic (CyberSec).

[17]  Narmeen Zakaria Bawany,et al.  DDoS Attack Detection and Mitigation Using SDN: Methods, Practices, and Solutions , 2017, Arabian Journal for Science and Engineering.

[18]  Richard E. Overill,et al.  Detection of known and unknown DDoS attacks using Artificial Neural Networks , 2016, Neurocomputing.

[19]  Jordi Torres,et al.  Adaptive distributed mechanism against flooding network attacks based on machine learning , 2008, AISec '08.

[20]  Yan Li,et al.  An Efficient DDoS TCP Flood Attack Detection and Prevention System in a Cloud Environment , 2017, IEEE Access.

[21]  Khaled Salah,et al.  Efficient and dynamic scaling of fog nodes for IoT devices , 2017, The Journal of Supercomputing.

[22]  Rabindra K. Barik,et al.  A deep learning based intelligent framework to mitigate DDoS attack in fog environment , 2019, J. King Saud Univ. Comput. Inf. Sci..

[23]  Rami M. Mohammad,et al.  An Enhanced Multiclass Support Vector Machine Model and its Application to Classifying File Systems Affected by a Digital Crime , 2019, J. King Saud Univ. Comput. Inf. Sci..

[24]  Sung Y. Shin,et al.  Smart IoT monitoring framework based on oneM2M for fog computing , 2018, SAC.

[25]  Jadzia Cendrowska,et al.  PRISM: An Algorithm for Inducing Modular Rules , 1987, Int. J. Man Mach. Stud..

[26]  Marc St-Hilaire,et al.  Early detection of DDoS attacks against SDN controllers , 2015, 2015 International Conference on Computing, Networking and Communications (ICNC).

[27]  A. Nur Zincir-Heywood,et al.  Supervised learning to detect DDoS attacks , 2014, 2014 IEEE Symposium on Computational Intelligence in Cyber Security (CICS).

[28]  Vitaly Klyuev,et al.  An Intelligent DDoS Attack Detection System Using Packet Analysis and Support Vector Machine , 2014 .

[29]  Yongdae Kim,et al.  A machine learning framework for network anomaly detection using SVM and GA , 2005, Proceedings from the Sixth Annual IEEE SMC Information Assurance Workshop.

[30]  Nour Moustafa,et al.  UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set) , 2015, 2015 Military Communications and Information Systems Conference (MilCIS).

[31]  Rami M. Mohammad,et al.  A comparison of machine learning techniques for file system forensics analysis , 2019, J. Inf. Secur. Appl..

[32]  Khaled Salah,et al.  Industrial internet of things: Recent advances, enabling technologies and open challenges , 2020, Comput. Electr. Eng..

[33]  S. Mercy Shalinie,et al.  Detection of DDoS attacks using Enhanced Support Vector Machines with real time generated dataset , 2011, 2011 Third International Conference on Advanced Computing.

[34]  Davide Anguita,et al.  Big Data Analytics in the Cloud: Spark on Hadoop vs MPI/OpenMP on Beowulf , 2015, INNS Conference on Big Data.

[35]  Jan Vykopal,et al.  Future of DDoS Attacks Mitigation in Software Defined Networks , 2014, AIMS.

[36]  Yuan Zhang,et al.  DDoS Attacks Detection Using Machine Learning Algorithms , 2018, IFTC.

[37]  Jieren Cheng,et al.  Adaptive DDoS Attack Detection Method Based on Multiple-Kernel Learning , 2018, Secur. Commun. Networks.

[38]  Max Bramer,et al.  Improving Modular Classification Rule Induction with G-Prism Using Dynamic Rule Term Boundaries , 2017, SGAI Conf..

[39]  Jin Ye,et al.  A DDoS Attack Detection Method Based on SVM in Software Defined Network , 2018, Secur. Commun. Networks.

[40]  Angelos Stavrou,et al.  20 Years of DDoS: a Call to Action , 2019, ArXiv.

[41]  Ada Gavrilovska,et al.  Towards IoT-DDoS Prevention Using Edge Computing , 2018, HotEdge.

[42]  Raja Srinivasa Reddy Boddu,et al.  Waikato Environment for Knowledge Analysis , 2019 .

[43]  Brij B. Gupta,et al.  A Comprehensive Survey on DDoS Attacks and Recent Defense Mechanisms , 2020 .

[44]  Ghazi Al-Naymat,et al.  Detecting Distributed Denial of Service Attacks Using Data Mining Techniques , 2016 .

[45]  Shashank Srivastava,et al.  Research Trends in Security and DDoS in SDN , 2016, Secur. Commun. Networks.

[46]  Nick Feamster,et al.  Machine Learning DDoS Detection for Consumer Internet of Things Devices , 2018, 2018 IEEE Security and Privacy Workshops (SPW).

[47]  Michela Antonelli,et al.  A novel associative classification model based on a fuzzy frequent pattern mining algorithm , 2015, Expert Syst. Appl..

[48]  Hyogon Kim,et al.  Controller scheduling for continued SDN operation under DDoS attacks , 2015 .

[49]  Gurpreet Singh,et al.  Prediction of Coronary Heart Disease using Machine Learning: An Experimental Analysis , 2019, ICDLT.

[50]  Song Guo,et al.  Can We Beat DDoS Attacks in Clouds? , 2014, IEEE Transactions on Parallel and Distributed Systems.

[51]  Ruby B. Lee,et al.  Machine Learning Based DDoS Attack Detection from Source Side in Cloud , 2017, 2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud).

[52]  Khaled Salah,et al.  Impact of CPU Utilization Thresholds and Scaling Size on Autoscaling Cloud Resources , 2013, 2013 IEEE 5th International Conference on Cloud Computing Technology and Science.