Ada-Boosted Locally Enhanced Probabilistic Neural Network for IoT Intrusion Detection

This paper proposes an intelligent and compact Probabilistic Neural Network which integrates locally enhanced semi-parametric base classifiers with AdaBoosting for intrusion detection system in IoT environment. The proposed model is to provide an improved intrusion detection at an affordable computational complexity. The proposed model is applied to the benchmark data sets for experiment and shows comparable intrusion detection performance at a reduced computational cost for real time use.

[1]  Yuval Elovici,et al.  Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection , 2018, NDSS.

[2]  Yang Song,et al.  Efficient Multiclass Boosting Classification with Active Learning , 2007, SDM.

[3]  Mohammad S. Obaidat,et al.  EnClass: Ensemble-Based Classification Model for Network Anomaly Detection in Massive Datasets , 2017, GLOBECOM 2017 - 2017 IEEE Global Communications Conference.

[4]  T. Jan,et al.  Vector quantized radial basis function neural network with embedded multiple local linear models for financial prediction , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

[5]  Antonio Martínez-Álvarez,et al.  Feature selection by multi-objective optimisation: Application to network anomaly detection by hierarchical self-organising maps , 2014, Knowl. Based Syst..

[6]  Wathiq Laftah Al-Yaseen,et al.  Multi-level hybrid support vector machine and extreme learning machine based on modified K-means for intrusion detection system , 2017, Expert Syst. Appl..

[7]  Itzhak Levin,et al.  KDD-99 classifier learning contest LLSoft's results overview , 2000, SKDD.

[8]  Mamun Bin Ibne Reaz,et al.  A novel SVM-kNN-PSO ensemble method for intrusion detection system , 2016, Appl. Soft Comput..

[9]  Ron Kohavi,et al.  Bias Plus Variance Decomposition for Zero-One Loss Functions , 1996, ICML.

[10]  Kah Phooi Seng,et al.  Minimalist security and privacy schemes based on enhanced AES for integrated WISP sensor networks , 2013, Int. J. Commun. Networks Distributed Syst..

[11]  Robert E. Schapire,et al.  A Brief Introduction to Boosting , 1999, IJCAI.

[12]  Donald F. Specht,et al.  Probabilistic neural networks , 1990, Neural Networks.

[13]  Jun Gao,et al.  Online Adaboost-Based Parameterized Methods for Dynamic Distributed Network Intrusion Detection , 2014, IEEE Transactions on Cybernetics.

[14]  Kotagiri Ramamohanarao,et al.  Layered Approach Using Conditional Random Fields for Intrusion Detection , 2010, IEEE Transactions on Dependable and Secure Computing.

[15]  Miheev Vladimir,et al.  The MP13 approach to the KDD'99 classifier learning contest , 2000 .

[16]  Bo Yang,et al.  Hybrid flexible neural‐tree‐based intrusion detection systems , 2007, Int. J. Intell. Syst..

[17]  Bernhard Pfahringer,et al.  Winning the KDD99 classification cup: bagged boosting , 2000, SKDD.

[18]  Charles Elkan,et al.  Results of the KDD'99 classifier learning , 2000, SKDD.

[19]  Christoph Schroth,et al.  The Internet of Things in an Enterprise Context , 2009, FIS.

[20]  Anthony Zaknich,et al.  Introduction to the modified probabilistic neural network for general signal processing applications , 1998, IEEE Trans. Signal Process..

[21]  Ramesh C. Agarwal,et al.  PNrule: A New Framework for Learning Classifier Models in Data Mining (A Case-Study in Network Intrusion Detection) , 2001, SDM.

[22]  Donald F. Specht,et al.  A general regression neural network , 1991, IEEE Trans. Neural Networks.

[23]  Rodrigo Roman,et al.  Securing the Internet of Things , 2017, Smart Cards, Tokens, Security and Applications, 2nd Ed..

[24]  Friedemann Mattern,et al.  From the Internet of Computers to the Internet of Things , 2010, From Active Data Management to Event-Based Systems and More.