Multi-object tracking (MOT) predominantly follows the tracking-by-detection paradigm, where Kalman filters serve as the standard motion predictor due to computational efficiency but inherently fail on non-linear motion patterns. Conversely, recent data-driven motion predictors capture complex non-linear dynamics but suffer from limited domain generalization and computational overhead. Through extensive analysis, we reveal that even in datasets dominated by non-linear motion, Kalman filter outperforms data-driven predictors in up to 34% of cases, demonstrating that real-world tracking scenarios inherently involve both linear and non-linear patterns. To leverage this complementarity, we propose PlugTrack, a novel framework that adaptively fuses Kalman filter and data-driven motion predictors through multi-perceptive motion understanding. Our approach employs multi-perceptive motion analysis to generate adaptive blending factors. PlugTrack achieves significant performance gains on MOT17/MOT20 and state-of-the-art on DanceTrack without modifying existing motion predictors. To the best of our knowledge, PlugTrack is the first framework to bridge classical and modern motion prediction paradigms through adaptive fusion in MOT.

The extremely diffused architecture of the Internet enables the propagation of malware and presents a significant challenge for the development of defenses against such malware propagation. Although machine learning-based malware detection models can improve approaches in response to this problem, their detection rates vary according to their features and classification methods. Single machine learning approaches for malware detection can vary in effectiveness according to the suitability of their classifiers despite the use of an appropriate training dataset. Some classifiers result in high detection rates with a malicious training dataset but have low detection rates with a benign training dataset, and false positive rates are particularly dependent on the use of appropriate classifiers. In this paper, we propose a machine learning-based hybrid decision model that can achieve a high detection rate with a low false positive rate. This hybrid model combines a random forest and a deep learning model using 12 hidden layers to determine malware and benign files, respectively. This model also includes certain proposed voting rules to make final decisions. In an experiment involving 6,395 atypical samples, this hybrid decision model achieved a higher detection rate (85.1% and standard deviation of 0.006) than that of the prior model (65.5%) without voting rules.