A Brute-Force Attack Detection Method Based on the Combination of Artificial Neural Network and Auto Encoder
Xiaojun Ma, Qingyuan Li, Xiaofei Wang, Quan Wen, Yingming Zeng · 2024
The ongoing advancement of information technology has led to the emergence of a multitude of network attack methodologies and techniques. As a prevalent form of network attack, it is imperative to undertake targeted research on the deliberate disruption of network traffic. Two issues currently impede the efficacy of machine learning and deep learning models in detecting brute force attacks. First, the presence of noise in the traffic data reduces the accuracy and robustness of detection. Second, when an attacker modifies the frequency, intensity, time, and other parameters of their attack to evade detection, the characteristics of the attack traffic become more complex and diverse. This, in turn, results in a notable decline in detection accuracy. In order to address the aforementioned issues, this paper presents a novel detection model that integrates the capabilities of an artificial neural network (ANN) and an auto-encoder (AE). The proposed model leverages the ANN's proficiency in recognizing intricate data patterns and the AE's effectiveness in removing noise, thereby facilitating precise detection of brute-force attacks. The experimental results demonstrate that the proposed detection model exhibits an enhanced accuracy rate of 99.93% in CICIDS-2017, exhibiting superior overall detection performance compared to existing algorithmic models such as AE and DAE. Additionally, it demonstrates a reduced false alarm rate and effective mitigation of the impact of noise and interference on attack data.