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Subject: search.filters.subject.Concept drift

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    Overcoming Concept Drift and Catastrophic Forgetting: Designing and Evaluating Deep Learning Architectures for Behavioral Malware Detection using Sysmon Data
    (2025-01-01)
    Thaibkhuang, Surapit
    ;
    Laungwilawan, Sorawit
    ;
    Pomsathit, Auttapon
    ;
    Ruangdech, Suppanat
    ;
    Pongpisutsopa, Suchittra
    In dynamic cybersecurity environments, traditional signature-based malware detection systems struggle to identify novel threats due to their reliance on static patterns. This study proposes a deep learning-based approach for behavioral malware detection using Sysmon logs, addressing two critical challenges: concept drift and catastrophic forgetting. The main contribution lies in a comprehensive comparative evaluation of four neural architectures CNN-only, CNN-BiLSTM with and without attention were evaluated alongside two continual learning strategies: fine-tuning and rehearsal. Experimental results demonstrate that fine-tuning enhances adaptability to new threats but severely degrades performance on previously learned data. In contrast, rehearsal-based incremental learning effectively mitigates forgetting while maintaining high detection accuracy across evolving datasets. Hybrid models incorporating attention mechanisms showed superior robustness. These findings underscore the importance of combining suitable neural architectures with continual learning techniques to build resilient and adaptive Endpoint Detection and Response (EDR) systems capable of handling real-world malware evolution. Future work includes collecting comprehensive real-world behavioral datasets, applying advanced continual learning strategies and exploring Graph Neural Networks or Transformers to improve detection robustness and adaptability.
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    Meta-cognitive recurrent kernel online sequential extreme learning machine with kernel adaptive filter for concept drift handling
    (2020-02-01)
    Liu, Zongying
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    Loo, Chu Kiong
    ;
    Pasupa, Kitsuchart
    ;
    Seera, Manjeevan
    This paper proposes a multi-step prediction model for time series prediction, i.e. Meta-cognitive Recurrent Kernel Online Sequential Extreme Learning Machine with Drift Detector Mechanism (Meta-RKOS-ELM<inf>ALD</inf>). Recurrent multi-step algorithm is applied to release the limitation in the number of prediction steps, and Drift Detector Mechanism (DDM) is used to overcome the problem of concept drift in the prediction model. The new meta-cognitive strategy decides the way of the incoming data during training, which decreases the training computation of prediction model and solves the parameter dependency. In our evaluation, we use a total of six artificial data sets and three real-world data sets (Standard & Poor's 500 Index, Shanghai Stock Exchange Composite Index, and Ozone Concentration in Toronto) to prove the ability of kernel filters, the detecting ability of concept drift detector, and situation of applying meta-cognitive strategy in our proposed model. Experiments results indicate that the Meta-KOS-ELM<inf>ALD</inf> with DDM has better forecasting ability in various predicting periods with the shortest learning time, as compared with other algorithms.
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    Real-time financial data prediction using meta-cognitive recurrent kernel online sequential extreme learning machine
    (2019-01-01)
    Liu, Zongying
    ;
    Loo, Chu Kiong
    ;
    Pasupa, Kitsuchart
    This paper proposes a novel algorithm called Meta-cognitive Recurrent Kernel Online Sequential Extreme Learning Machine with a kernel filter and a modified Drift Detector Mechanism (Meta-RKOS-ELM<inf>ALD</inf>-DDM). The algorithm aims to tackle a well-known concept drift problem in time series prediction by utilising the modified concept drift detector mechanism. Moreover, the new meta-cognitive learning strategy is employed to solve parameter dependency and reduce learning time. The experimental results show that the proposed method can achieve better performance than the conventional algorithm in a set of financial datasets.
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    Handling concept drift in time-series data: Meta-cognitive recurrent recursive-kernel OS-ELM
    (2018-01-01)
    Liu, Zongying
    ;
    Loo, Chu Kiong
    ;
    Pasupa, Kitsuchart
    This paper proposes a meta-cognitive recurrent multi-step-prediction model called Meta-cognitive Recurrent Recursive Kernel Online Sequential Extreme Learning Machine with a new modified Drift Detector Mechanism (Meta-RRKOS-ELM-DDM). This model combines the strengths of Recurrent Kernel Online Sequential Extreme Learning Machine (RKOS-ELM) with the recursive kernel method and a new meta-cognitive learning strategy. We apply Drift Detector Mechanism to solve concept drift problem. Recursive kernel method successfully replaces the normal kernel method in RKOS-ELM and generates a fixed reservoir with optimised information. The new meta-cognitive learning strategy can reduce the computational complexity. The experimental results show that Meta-RRKOS-ELM-DDM has a superior prediction ability in different predicting horizons than the others.