An Adversarial Neural Cryptography Approach to Integrity Checking: Learning to Secure Data Communications

Securing communications is an increasingly challenging problem. While communication channels can be secured using strong ciphers, attackers who gain access to the channel can still perform certain types of attacks. One way to mitigate such attacks is to verify the integrity of exchanging messages between two parties or more. While there are robust integrity check mechanisms currently, these lack variety, and very few are based on machine learning. This paper presents a methodology for performing an integrity check inspired by recent advances in neural cryptography. We provide formal, mathematical functions and an optimization problem for training an adversarial neural cryptography architecture. The proposed neural architectures can adequately solve the problem. In our experiments, a receiver can verify if incoming messages are authentic or altered with an accuracy greater than 99%. This work expands the repertoire of integrity checking methodologies, provides a unique perspective based on neural networks and facilitates data security and privacy. Paper: [ bib , pdf ]

Training model for integrity check

A review of Earth Artificial Intelligence

In recent years, Earth system sciences are urgently calling for innovation on improving accuracy, enhancing model intelligence level, scaling up operation, and reducing costs in many subdomains amid the exponentially accumulated datasets and the promising artificial intelligence (AI) revolution in computer science. This paper presents work led by the NASA Earth Science Data Systems Working Groups and ESIP machine learning cluster to give a comprehensive overview of AI in Earth sciences. It holistically introduces the current status, technology, use cases, challenges, and opportunities, and provides all the levels of AI practitioners in geosciences with an overall big picture and to “blow away the fog to get a clearer vision” about the future development of Earth AI. The paper covers all the major spheres in the Earth system and investigates representative AI research in each domain. Widely used AI algorithms and computing cyberinfrastructure are briefly introduced. The mandatory steps in a typical workflow of specializing AI to solve Earth scientific problems are decomposed and analyzed. Eventually, it concludes with the grand challenges and reveals the opportunities to give some guidance and pre-warnings on allocating resources wisely to achieve the ambitious Earth AI goals in the future. [pdf, bib]

Challenges and opportunities.