A Review on Intrusion Detection System Based on Various Learning Techniques

In this world of the Internet, security plays an important role as Internet users grow rapidly. Security in the network is one of the modern periods' main issues. In the last decade, the exponential growth and massive use of the Internet have enabled system security vulnerabilities a critical aspect. Intrusion detection system to track unauthorized access as well as exceptional attacks through secured networks. Several experiments on the IDS have been carried out in recent years. And to know the current state of machine learning approaches to address the issue of intrusion detection. IDS is commonly used for the detection and recognition of cyberattacks at the network and host stage, in a timely and automatic manner. This research assesses the creation of a deep neural network (DNN), a form of deep learning model as well as ELM to detect unpredictable and unpredictable cyber-attacks.

[1]  Dianhui Wang,et al.  Extreme learning machines: a survey , 2011, Int. J. Mach. Learn. Cybern..

[2]  Jacek Rumiński,et al.  A survey of neural networks usage for intrusion detection systems , 2020, Journal of Ambient Intelligence and Humanized Computing.

[3]  Peisheng Pan,et al.  Research on Intrusion Detection Based on Improved DBN-ELM , 2019, 2019 International Conference on Communications, Information System and Computer Engineering (CISCE).

[4]  V. Jaiganesh,et al.  Intrusion Detection Systems: A Survey and Analysis of Classification Techniques , 2013 .

[5]  Mei-Ling Shyu,et al.  A Survey on Deep Learning , 2018, ACM Comput. Surv..

[6]  Mohammad Ali Keyvanrad,et al.  A brief survey on deep belief networks and introducing a new object oriented toolbox ( DeeBNet V 3 . 0 ) , 2016 .

[7]  2019 International Conference on Communications, Information System and Computer Engineering (CISCE) , 2019 .

[8]  Marco Ruffini,et al.  An Overview on Application of Machine Learning Techniques in Optical Networks , 2018, IEEE Communications Surveys & Tutorials.

[9]  Abdullah Ahmad,et al.  Comparative Performance of Deep Learning and Machine Learning Algorithms on Imbalanced Handwritten Data , 2018 .

[10]  Zhen Zhang,et al.  An Optimization Method for Intrusion Detection Classification Model Based on Deep Belief Network , 2019, IEEE Access.

[11]  N. S. Gill,et al.  Machine Learning Techniques and Extreme Learning Machine for Early Breast Cancer Prediction , 2020 .

[12]  Worachai Srimuang,et al.  Classification model of network intrusion using Weighted Extreme Learning Machine , 2015, 2015 12th International Joint Conference on Computer Science and Software Engineering (JCSSE).

[13]  Hyeran Byun,et al.  Applications of Support Vector Machines for Pattern Recognition: A Survey , 2002, SVM.

[14]  Emmanuel Dare Alalade Intrusion Detection System in Smart Home Network Using Artificial Immune System and Extreme Learning Machine Hybrid Approach , 2020, 2020 IEEE 6th World Forum on Internet of Things (WF-IoT).

[15]  Junhua Ku,et al.  Intrusion Detection Based on Self-adaptive Differential Evolutionary Extreme Learning Machine , 2017, 2017 International Conference on Computer Network, Electronic and Automation (ICCNEA).

[16]  K. James Mathai,et al.  Performance Comparison of Intrusion Detection System Between Deep Belief Network (DBN)Algorithm and State Preserving Extreme Learning Machine (SPELM) Algorithm , 2019, 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT).

[17]  Dewan Md. Farid,et al.  Application of Machine Learning Approaches in Intrusion Detection System: A Survey , 2015 .

[18]  Haleh Amintoosi,et al.  Intrusion Detection System Based on Gradient Corrected Online Sequential Extreme Learning Machine , 2021, IEEE Access.

[19]  V. Pream Sudha,et al.  A SURVEY ON DEEP LEARNING TECHNIQUES, APPLICATIONS AND CHALLENGES , 2015 .

[20]  Amelia Ritahani Ismail,et al.  Comparative Performance of Deep Learning and Machine Learning Algorithms on Imbalanced Handwritten Data , 2018 .

[21]  Md Zahangir Alom,et al.  Intrusion detection using deep belief networks , 2015, 2015 National Aerospace and Electronics Conference (NAECON).

[22]  Nik Zulkarnaen Khidzir,et al.  A hybrid Particle swarm optimization -Extreme Learning Machine approach for Intrusion Detection System , 2018, 2018 IEEE Student Conference on Research and Development (SCOReD).

[23]  Xinman Zhang,et al.  An Overview of Extreme Learning Machine , 2019, 2019 4th International Conference on Control, Robotics and Cybernetics (CRC).

[24]  Glenn Fung,et al.  Multicategory Proximal Support Vector Machine Classifiers , 2005, Machine Learning.

[25]  J. Pamina,et al.  Survey on Deep Learning Algorithms , 2019 .

[26]  Nerella Sameera,et al.  Intrusion Detection Analytics: A Comprehensive Survey , 2019 .

[27]  Iqbal Gondal,et al.  Survey of intrusion detection systems: techniques, datasets and challenges , 2019, Cybersecurity.

[28]  R. Kalaivani,et al.  Intrusion Detection System – A Survey , 2015 .