Transfer Learning for Security: Challenges and Future Directions
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Abstract
Many machine learning and data mining algorithms rely on the assumption that the training and testing data share the same feature space and distribution. However, this assumption may not always hold. For instance, there are situations where we need to classify data in one domain, but we only have sufficient training data available from a different domain. The latter data may follow a distinct distribution. In such cases, successfully transferring knowledge across domains can significantly improve learning performance and reduce the need for extensive data labeling efforts. Transfer learning (TL) has thus emerged as a promising framework to tackle this challenge, particularly in security-related tasks. This paper aims to review the current advancements in utilizing TL techniques for security. The paper includes a discussion of the existing research gaps in applying TL in the security domain, as well as exploring potential future research directions and issues that arise in the context of TL-assisted security solutions.Keywords
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This publication has 15 references indexed in Scilit:
- IDSGAN: Generative Adversarial Networks for Attack Generation Against Intrusion Detection2022
- DTMIC: Deep transfer learning for malware image classificationJournal of Information Security and Applications, 2022
- MCFT-CNN: Malware classification with fine-tune convolution neural networks using traditional and transfer learning in Internet of ThingsFuture Generation Computer Systems, 2021
- Diagnosing Rotating Machines With Weakly Supervised Data Using Deep Transfer LearningIEEE Transactions on Industrial Informatics, 2019
- Real-Time Sensor Anomaly Detection and Identification in Automated VehiclesIEEE Transactions on Intelligent Transportation Systems, 2019
- Deep Transfer Learning Based on Sparse Autoencoder for Remaining Useful Life Prediction of Tool in ManufacturingIEEE Transactions on Industrial Informatics, 2018
- Deep visual domain adaptation: A surveyNeurocomputing, 2018
- Highly Accurate Machine Fault Diagnosis Using Deep Transfer LearningIEEE Transactions on Industrial Informatics, 2018
- Decision Trees for Mining Data Streams Based on the Gaussian ApproximationIEEE Transactions on Knowledge and Data Engineering, 2013
- An efficient intrusion detection system based on support vector machines and gradually feature removal methodExpert Systems with Applications, 2012