Network-on-Chip (NoC) is widely used as the internal communication fabric in
today's multicore System-on-Chip (SoC) designs. Security of the on-chip
communication is crucial because exploiting any vulnerability in shared NoC
would be a goldmine for an attacker. NoC security relies on effective
countermeasures against diverse attacks. We investigate the security strength
of existing anonymous routing protocols in NoC architectures. Specifically,
this paper makes two important contributions. We show that the existing
anonymous routing is vulnerable to machine learning (ML) based flow correlation
attacks on NoCs. We propose a lightweight anonymous routing that use traffic
obfuscation techniques which can defend against ML-based flow correlation
attacks. Experimental studies using both real and synthetic traffic reveal that
our proposed attack is successful against state-of-the-art anonymous routing in
NoC architectures with a high accuracy (up to 99%) for diverse traffic
patterns, while our lightweight countermeasure can defend against ML-based
attacks with minor hardware and performance overhead.

该论文研究了网络片上互连（NoC）架构中现有匿名路由协议的安全性，并提出了一种轻量级匿名路由方案，利用流量混淆技术来防御基于机器学习的攻击。实验结果表明，该方案相对于现有的最新匿名路由方案在不同流量模式下具有高达 99% 的准确性，且仅具有较小的硬件和性能开销。

使用流量相关攻击破解 NoC 的匿名性

Breaking NoC Anonymity using Flow Correlation Attack

Recent captioning models are limited in their ability to scale and describe
concepts unseen in paired image-text corpora. We propose the Novel Object
Captioner (NOC), a deep visual semantic captioning model that can describe a
large number of object categories not present in existing image-caption
datasets. Our model takes advantage of external sources -- labeled images from
object recognition datasets, and semantic knowledge extracted from unannotated
text. We propose minimizing a joint objective which can learn from these
diverse data sources and leverage distributional semantic embeddings, enabling
the model to generalize and describe novel objects outside of image-caption
datasets. We demonstrate that our model exploits semantic information to
generate captions for hundreds of object categories in the ImageNet object
recognition dataset that are not observed in MSCOCO image-caption training
data, as well as many categories that are observed very rarely. Both automatic
evaluations and human judgements show that our model considerably outperforms
prior work in being able to describe many more categories of objects.

本文提出了 Novel Object Captioner (NOC) 模型，利用外部数据源和语义嵌入使模型适用于不在现有图像字幕数据集中出现的数百种物体分类，并表现出对现有罕见分类的能力，自动评估和人类判断均表明，该模型在描述物体方面比之前的工作表现更好。