Framework for Fuzzy Rule Based Automatic Intrusion Response Selection System (FRAIRSS) using Fuzzy Analytic Hierarchy Process and Fuzzy TOPSIS

Article type: Research Article

Authors: Singh, Dileep Kumar^{; *} | Kaushik, Praveen

Affiliations: Department of Computer Science and Engineering, MANIT Bhopal, MP, India

Correspondence: [*] Corresponding author. Dileep Kumar Singh, Department of CSE, MANIT Bhopal (MP), India. E-mail: dileep.133212004@manit.ac.in.

Abstract: Intrusion Detection System (IDS) detects the intrusions and produces alerts. Automated Intrusion Response System (AIRS) selects and triggers the appropriate response based on some criteria to mitigate the intrusion without delay. The big challenges in the automated response selection process are a precise measurement of importance weight for each criterion and response prioritization for the specific category of attacks. Analytic hierarchy process (AHP) uses the pair-wise comparison of each criterion and does not require the accurate quantification but is unable to handle the vagueness or uncertainty in the importance judgment. This paper presents the framework called Fuzzy Rule-Based Automatic Intrusion Response Selection System (FRAIRSS) for automated response selection. Fuzzy AHP model has been created in order to deal with precise measurement and uncertainty in the importance judgment of each criterion. Fuzzy TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) multi-criteria decision making (MCDM) approach has been applied in order to resolve the response prioritization. Fuzzy Rule-based inference system is modeled to select the appropriate response from the prioritized response sets for each category of attacks. The framework has been simulated in MATLAB with various attack scenarios and it is found that FRAIRSS is selecting most appropriate response under the given attack scenarios.

Keywords: Intrusion response system, Fuzzy AHP, Fuzzy Rule-Based inference system, response prioritization, importance weight of response selection criteria

DOI: 10.3233/JIFS-18350

Journal: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 2559-2571, 2018