Permission-based Android malware detectors exhibit asymmetric domain shift with accuracy dropping from over 92% intra-domain to as low as 73% cross-domain, but hybrid training on common features restores 88-97% accuracy.
Explainable artificial intelligence applications in cyber security: State- of-the-art in research
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Standard classifiers with SMOTE oversampling are reported to raise intrusion-detection accuracy on the KDD Cup 1999 dataset when applied to Zero Trust IoT environments.
citing papers explorer
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Diagnosing and Mitigating Domain Shift in Permission-Based Android Malware Detection
Permission-based Android malware detectors exhibit asymmetric domain shift with accuracy dropping from over 92% intra-domain to as low as 73% cross-domain, but hybrid training on common features restores 88-97% accuracy.
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Advanced Anomaly Detection and Threat Intelligence in Zero Trust IoT Environments Using Machine Learning
Standard classifiers with SMOTE oversampling are reported to raise intrusion-detection accuracy on the KDD Cup 1999 dataset when applied to Zero Trust IoT environments.