Intrusion Detection System Based on Neural Networks Using Bipolar Input with Bipolar Sigmoid Activation Function | Zendy

Adel Issa | Zendy; Adnan Mohsin Abdulazeez | Zendy

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Intrusion Detection System Based on Neural Networks Using Bipolar Input with Bipolar Sigmoid Activation Function

Author(s) -

Adel Issa,

Adnan Mohsin Abdulazeez

Publication year - 2011

Publication title -

maǧallaẗ al-rāfidayn li-ʿulūm al-ḥāsibāt wa-al-riyāḍiyyāẗ/al-rafidain journal for computer sciences and mathematics

Language(s) - English

Resource type - Journals

eISSN - 2311-7990

pISSN - 1815-4816

DOI - 10.33899/csmj.2011.163644

Subject(s) - sigmoid function , computer science , intrusion detection system , activation function , artificial neural network , backpropagation , function (biology) , connection (principal bundle) , data mining , artificial intelligence , intrusion , pattern recognition (psychology) , engineering , geochemistry , geology , structural engineering , evolutionary biology , biology

Vulnerabilities in common security components such as firewalls are inevitable. Intrusion Detection Systems (IDS) are used as another wall to protect computer systems and to identify corresponding vulnerabilities. The purpose of this paper is to use Backpropagation algorithm for IDS by applying bipolar input "input is represented as (1, -1)", and bipolar sigmoid activation function. The KDD Cup 99 dataset is used in this paper. Number of train dataset is 4947 connection records, and number of test dataset is 3117 connection records. The results of the proposed method show that the PSP is 88.32 and CPT equal to 0.286.

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