Detecting Anomalies in Network Traffic Using Fuzzy Logic with A Comparative Analysis Against Deep Learning Techniques
Dr. N. Padmaja, G. Sailaja, K. V. Siva Prasad Reddy, Nallamekala Harshanvitha
Network traffic anomaly detection plays a crucial role in cybersecurity by identifying suspicious
activities that may indicate cyberattacks, malware infections, or unauthorized access. Traditional rulebased methods often struggle with evolving attack patterns, necessitating more adaptive and intelligent
approaches. This study explores the effectiveness of Fuzzy Logic and Deep Learning models (LSTM,
Autoencoder, CNN) for detecting anomalies in network traffic. Fuzzy Logic offers an interpretable rulebased framework for handling uncertainty, while deep learning models leverage data-driven learning
for improved anomaly detection accuracy. Using publicly available datasets such as NSL-KDD and
CICIDS 2017, we evaluate these methods based on key metrics such as accuracy, precision, recall,
F1-score. The results indicate that while Fuzzy Logic provides reasonable accuracy (85.2%), deep
learning models—particularly CNN (94.1%) and LSTM (92.4%)—demonstrate superior performance.
CNN outperforms other models due to its ability to recognize spatial patterns in network traffic, while
LSTM effectively captures sequential dependencies. These findings highlight the trade-off between
interpretability and accuracy, suggesting that deep learning models are more effective for real-time
and large-scale anomaly detection, whereas Fuzzy Logic remains a viable option where transparency
is prioritized.
