A Novel Random Neural Network Based Approach for Intrusion Detection Systems

Computer security and privacy of user specific data is a prime concern in day to day communication. The mass use of internet connected systems has given rise to many vulnerabilities which includes attacks on smart devices. Regular occurrence of such events has made the availability of scalable Intrusion Detection System (IDS) a perilous challenge. An intelligent IDS should be able to stop the malicious activity before it destabilizes the core network and to achieve this goal we propose a novel Random Neural Network based Intrusion Detection System (RNN-IDS) in this paper. The performance is evaluated by training different numbers of input and hidden layer neurons with learning rates on benchmark NSL-KDD dataset for binary classification. To validate the feasibility of proposed scheme, results were compared with existing systems and its performance was evaluated by the detection of novel attacks while obtaining an accuracy of 94.50%.

[1]  Abbas Javed,et al.  Experimental testing of a random neural network smart controller using a single zone test chamber , 2015, IET Networks.

[2]  Ji Wan,et al.  Deep Learning for Content-Based Image Retrieval: A Comprehensive Study , 2014, ACM Multimedia.

[3]  Gerardo Rubino,et al.  A study of real-time packet video quality using random neural networks , 2002, IEEE Trans. Circuits Syst. Video Technol..

[4]  Abbas Javed,et al.  Intelligent Intrusion Detection in Low-Power IoTs , 2016, ACM Trans. Internet Techn..

[5]  Abbas Javed,et al.  Smart Random Neural Network Controller for HVAC Using Cloud Computing Technology , 2017, IEEE Transactions on Industrial Informatics.

[6]  M. A. Jabbar,et al.  Random Forest Modeling for Network Intrusion Detection System , 2016 .

[7]  Abbas Javed,et al.  Energy demand prediction through novel random neural network predictor for large non-domestic buildings , 2017, 2017 Annual IEEE International Systems Conference (SysCon).

[8]  Anamika Yadav,et al.  Performance analysis of NSL-KDD dataset using ANN , 2015, 2015 International Conference on Signal Processing and Communication Engineering Systems.

[9]  Mohiuddin Ahmed,et al.  A survey of network anomaly detection techniques , 2016, J. Netw. Comput. Appl..

[10]  Abbas Javed,et al.  Design and Implementation of a Cloud Enabled Random Neural Network-Based Decentralized Smart Controller With Intelligent Sensor Nodes for HVAC , 2017, IEEE Internet of Things Journal.

[11]  Karan Bajaj,et al.  Improving the Intrusion Detection using Discriminative Machine Learning Approach and Improve the Time Complexity by Data Mining Feature Selection Methods , 2013 .

[12]  Geoffrey E. Hinton,et al.  Speech recognition with deep recurrent neural networks , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[13]  Jinoh Kim,et al.  A survey of deep learning-based network anomaly detection , 2017, Cluster Computing.

[14]  Yue Wu,et al.  A New Intrusion Detection System Based on KNN Classification Algorithm in Wireless Sensor Network , 2014, J. Electr. Comput. Eng..

[15]  Hadi Larijani,et al.  Random neural network based novel decision making framework for optimized and autonomous power control in LTE uplink system , 2016, Phys. Commun..

[16]  Erol Gelenbe,et al.  Analog hardware implementation of the random neural network model , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[17]  Tara N. Sainath,et al.  FUNDAMENTAL TECHNOLOGIES IN MODERN SPEECH RECOGNITION Digital Object Identifier 10.1109/MSP.2012.2205597 , 2012 .

[18]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[19]  Yuefei Zhu,et al.  A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks , 2017, IEEE Access.