FUZZY-LOGIC ALGORITHM FOR RISK ASSESSMENT IN WI-FI NETWORKS

Abstract

Context. With the increasing use of Wi-Fi wireless networks, the risk of attacks specific to them is also rising. Traditional protection methods, which usually rely on precise thresholds, do not reflect the actual uncertainty of the conditions in which wireless networks operate. Due to the openness of the radio channel, its instability, dispersion, and the presence of noise, a promising direction is the use of fuzzy logic algorithms, which allow for taking into account the incompleteness and ambiguity of data when assessing the risks of Wi-Fi wireless networks.
Objective. Develop a fuzzy logic algorithm for assessing the state of Wi-Fi networks, which allows adaptively determining the level of risk by analyzing wireless network parameters and making decisions regarding security system actions.
Method. A fuzzy-logic-based algorithm for analyzing the operational state of a wireless Wi-Fi network is proposed. The algorithm is based on the integrated analysis of six network parameters using elements of fuzzy logic. It includes the construction of membership functions for the input variables, the formation of a fuzzy IF–THEN rule base, and a defuzzification mechanism that provides a continuous numerical assessment of the network risk level. To evaluate the effectiveness of the proposed approach, a comparative simulation study was conducted against the classical threshold-based decision-making method. The study was carried out in the MathCAD and MATLAB environments to enable cross-validation of the algorithm’s functionality. Three network operation scenarios were considered, with 100 network states simulated for each scenario.
Results. The simulation results obtained in the MathCAD and MATLAB environments coincide up to the third decimal place, confirming the correctness of the software implementation of the algorithm. Comparative analysis showed that the threshold-based method produces binary decisions and is highly sensitive to random fluctuations in network parameters, which leads to an increased number of false alarms. The proposed fuzzy-logic-based algorithm provides a continuous risk assessment, demonstrates lower result variance, and exhibits a stable response to changes in network conditions. Under unstable network operating conditions, the algorithm enables discrimination between noise and interference effects and the initial phases of attacks, while also ensuring a gradual increase in the risk level without abrupt transitions between linguistic levels. The obtained results confirm a reduction in Type I errors and an improvement in decision-making informativeness.
Conclusions. The fuzzy logic-based Wi-Fi network state analysis algorithm proposed in this work enables more adequate decision-making regarding the network’s condition. The use of fuzzy logic allows adjusting decisions depending on changes in network operating conditions in real time and can be integrated into intrusion detection systems or advanced wireless network cybersecurity tools.