Statistics and Data Science Forcybersecurity

Alfred Hero,Soummya Kar,Jose Moura,Joshua Neil,H. Vincent Poor,Melissa Turcotte,Bowei Xi

HARVARD DATA SCIENCE REVIEW（2023）

Cited 5|Views6

Abstract

Cybersecurity is an ever-important aspect of our interconnected world, but security defenses lag behind the adversaries who with increasing sophistication seek to disrupt cybersystems. The emergence of massively distributed systems such as the Internet of Things (IoT) has opened up new vulnerabilities that go beyond traditional protective measures such as firewalls, password protection, and single point-of-attack responses. To address these emerging vulnerabilities, data science has much to contribute, including methods of distributed statistical inference, data fusion, anomaly detection, and adversarial machine learning.

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Adversarial machine learning,enterprise cybersecurity,resilient distributed networks,information theoretic security

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Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

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