Intrusion Detection System in Ad Hoc Networks with Artificial Neural Networks and Algorithm K-Means

There has been a great technological advance in the infrastructure of mobile technologies. The increase in the use of wireless local area networks and the use of services from satellites is also noticeable. The high rate of use of mobile devices for various purposes brings the need to monitor the wireless networks to ensure the integrity and confidentiality of the information. Therefore it is necessary to quickly and efficiently identify the normal and abnormal traffic of these wireless networks so that their administrators can take action. This paper presents a proposal for an Ad Hoc Wireless Intrusion Detection System composed of two stages, based on data grouping through the K-Means and Artificial Neural Networks algorithm through the Multilayer Perceptron algorithm, for the detection and classification of anomalies caused by attacks on the networks of Computers.

[1]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

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

[3]  J. Amudhavel,et al.  A Survey on Intrusion Detection System: State of the Art Review , 2016 .

[5]  R. M. Chandrasekaran,et al.  Intrusion detection using neural based hybrid classification methods , 2011, Comput. Networks.

[6]  Subhi R. M. Zeebaree,et al.  Combination of K-means clustering with Genetic Algorithm : A review , 2017 .

[7]  Jonathan Loo,et al.  Mobile Ad Hoc Networks: Current Status and Future Trends , 2011 .

[8]  K. Nirmala,et al.  Security Challenges in Mobile Ad Hoc Networks - A Survey , 2016 .

[9]  Ed Wilson Tavares Ferreira.  Proposta de um sistema de detecção e classificação de intrusão em redes de computadores baseado em transformadas wavelets e redes neurais artificiais , 2009 .

[10]  Alexandre R. S. Romariz,et al.  Data Analysis of Wireless Networks Using Computational Intelligence , 2018, J. Commun..

[11]  R. de Oliveira,et al.  Intrusion Detection System with Wavelet and Neural Artifical Network Approach for Networks Computers , 2011, IEEE Latin America Transactions.

[12]  Aldecir Xavier de Marins.  Protocolos de roteamento para redes móveis comparativo : OLSR X AODV , 2017 .

[13]  Ferat Sahin,et al.  A survey on feature selection methods , 2014, Comput. Electr. Eng..

[14]  Simon Haykin,et al.  Neural Networks and Learning Machines , 2010 .