1 citations · 1 across the 4 of their papers we have counts for
4 papers
SEED: Semi-supervised Continual MalwarE Detection for Tackling ConcEpt Drift on a BuDget
Suresh Kumar Amalapuram, Bikraj Shresta, Siva Ram murthy Chebiyam +2
Machine learning based malware detectors become obsolete over time due to concept drift in benign and malware applications. Recent methods rely on fully labeled data and use hierar…
CITADEL: A Semi-Supervised Active Learning Framework for Malware Detection Under Continuous Distribution Drift
Md Ahsanul Haque, Md Mahmuduzzaman Kamol, Suresh Kumar Amalapuram +2
Android malware detection systems suffer severe performance degradation over time due to concept drift caused by evolving malicious and benign app behaviors. Although recent method…
LAMDA: A Longitudinal Android Malware Benchmark for Concept Drift Analysis
Md Ahsanul Haque, Ismail Hossain, Md Mahmuduzzaman Kamol +4
Machine learning (ML)-based malware detection systems often fail to account for the dynamic nature of real-world training and test data distributions. In practice, these distributi…
SOUL: A Semi-supervised Open-world continUal Learning method for Network Intrusion Detection
Suresh Kumar Amalapuram, Shreya Kumar, Bheemarjuna Reddy Tamma +1
Fully supervised continual learning methods have shown improved attack traffic detection in a closed-world learning setting. However, obtaining fully annotated data is an arduous t…