This project aims to achieve trustworthy deep learning (TDL)-based distributed computation among wireless connected Naval devices (sensors, UAVs, mobile phones, ships, etc.) through the seamless integration of cryptography, a complimentary classifier, and communications (C4). Distributed Deep Learning (DDL) runs in a distributed Naval network setting and is vulnerable to attacks that have knowledge of DDL meta-parameters. A simple generative adversarial network (GAN)-based model can cause the DDL to leak label information to adversaries. Key-based gradient encryption method does not include a complete DDL network security protocol. Blockchain may be used to protect DL; however, this approach does not have learning-based input control and cannot resist adaptive spiral attacks in a complex DDL setting. These elements enable a new challenging attack that is unique to the DDL environment: the adaptive grey-box spiral (AGBS) attack. Our project goal is to develop and demonstrate C4, a powerful new method that seamlessly integrates applied Cryptography, distributed deep Computation, a Complementary Classifier with ensemble Bayesian learning, and a Naval Communication scheme with Blockchain protocol.
Benefit: All 9 topics under the Navy Technology Acceleration objectives for AI/ML would benefit from distributed deep learning (DDL) defense schemes: (1) Manned and unmanned aircraft could employ a concurrent, divide-and-combine DDL computation model to enable real-time learning of big data using a deep defense strategy. (2) A ship fleet could collaborate using secure pattern learning of surrounding objects, weather, and threats. (3) Naval Special Warfare operators could employ secure wireless communications under enemy jamming attack. (4) Naval satellite sensors could employ localized processing and global fusion to detect intrusion.
Keywords: Ensemble Bayesian Learning, Ensemble Bayesian Learning, algorithm, Blockchain, ensemble, Distributed Deep Learning, AGBS, spiral attack
