Intrusion detection systems (IDS) play a vital role in protecting networks synovex one grass from malicious attacks.Modern IDS use machine-learning or deep-learning models to deal with the diversity of attacks that malicious users may employ.However, effective machine-learning methods incur a considerable cost in both the pretraining stage and the online detection process itself.
Accordingly, this study proposes a quantitative logarithmic transformation-based intrusion detection system (QLT-IDS) that uses a straightforward statistical approach to analyze network behavior.Compared with machine-learning or deep-learning-based IDS methods, the proposed system requires neither a time-consuming and expensive data collection and training process, nor a GPU-included device to achieve a real-time detection performance.Furthermore, the system can deal not only with North-South attacks, but also East-West attacks, kicker pro comp 10 which pose a significant risk in real-world operations.
The effectiveness of the proposed system is evaluated for both real-world campus network traffic and simulated traffic.The results confirm that QLT-IDS is able to detect a wide range of malicious attacks with a high precision, even under high down-sampling rate of the NetFlow records.