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Sebopelo, Rodney Buang
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Computer Science & IT

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Journal : Journal of Information Systems and Informatics

 1 
Adaptive-Delta ADWIN for Balancing Sensitivity and Stability in Streaming IDS Sebopelo, Rodney Buang
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1260

Abstract

In dynamic traffic networks, intrusion detection systems (IDS) must handle dynamic data stream where traffic changes occur, and concept drift is customary. Traditional concept drift detection approaches often experience a challenge between sensitivity and stability, resulting in delayed adaptation and uncontrolled false alarms. This paper proposes an AdaptiveDelta ADWIN framework that tunes sensitivity detectors using online lightweight controllers: Volatility (VC), that tune a delta based on error volatility, and AlertRate Controller (ARC), which modulates the drift alarms frequency. The framework is implemented using Bagging ensemble of Hoeffding Adaptive Trees and evaluated on a network preprocessed traffic dataset. Comparative experiments opposed to a fixed, ultrasensitive delta detector illustrate that adaptive tuning authorizes timely drift detection while maintaining stability, decreasing false alarms by more than 25%, and enhancing predictive overall performance. AdaptiveDelta baseline maintains a stable accuracy approximately 80% 82% accentuating the importance of balancing detection sensitivity with operational stability in streaming IDS implementation. These results highlight the practical value of the proposed framework, which is lightweight, computationally efficient, and suitable for real-time deployment in streaming IDS environments.
Adaptive-Delta ADWIN: A Framework for Stable and Sensitive Intrusion Detection in Streaming Networks Sebopelo, Rodney Buang
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1336

Abstract

Intrusion Detection Systems (IDS) must adapt to network traffic streams where concept drift alters the normal and malicious behaviors. The traditional drift detectors with fixed sensitivity parameter () fail to balance responsiveness and stability, reducing detection reliability. This study introduces Adaptiveāˆ’Delta ADWIN framework that adjusts through two online controllersthe Volatility Controller (VC) and AlertRate Controller (ARC) to improve the sensitivity while maintaining stability. The experiments were evaluated on the CICIDS2017 dataset using multiclass ensemble of Hoeffding Adaptive Trees, the framework achieved 0.930.95, surpassing fixed baselines by up to 6.6%. The false positive and false negative rates were reduced by 50% and 30%. Overall, the results confirm that Adaptive ADWIN enhances multiclass IDS performance between detection sensitivity and operational stability in the realtime network conditions.
Trinity-Controller ADWIN: An Accuracy Guided Sensitivity Control Framework for Streaming Intrusion Detection Sebopelo, Rodney Buang
Journal of Information System and Informatics Vol 8 No 1 (2026): February
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i1.1421

Abstract

Concept drift can severely undermine the reliability of streaming Intrusion Detection Systems (IDS), especially in realistic network traffic where changes are gradual, recurring, and often masked by noise and class imbalance. Widely used statistical drift detectors such as ADWIN provide theoretical guarantees, yet in practice they can exhibit sensitivity oscillations, delayed adaptation under subtle drift, and disruptive reset behavior that leads to prolonged performance dips. This paper presents Trinity-Controller ADWIN, a unified drift-management framework that fuses three complementary signals: a Volatility Controller (VC) for statistically grounded drift detection, an Adaptive Rate Controller (ARC) that dynamically regulates ADWIN sensitivity, and a Performance-Based Controller (PBC) that monitors an Exponential Moving Average (EMA) of online accuracy to detect sustained model degradation. The proposed framework is evaluated using a Hoeffding Adaptive Tree classifier on a time-ordered streaming reconstruction of CICIDS2017, reflecting realistic temporal drift patterns. Across multiple drift regions, Trinity-Controller ADWIN achieves higher long-horizon accuracy stability, faster post-drift recovery, and fewer unnecessary resets than fixed ADWIN, VC-only, and VC+ARC baselines. Notably, in several drift segments the framework preserves post-drift accuracy above 90% of baseline while demonstrating near-zero recovery delay, indicating that adaptation occurs with minimal disruption. Overall, the results show that combining statistical drift evidence with direct performance-aware feedback yields a more robust and operationally reliable streaming IDS under evolving traffic conditions.
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