Malware detection is an interesting and valuable domain to work in because it
has significant real-world impact and unique machine-learning challenges. We
investigate existing long-range techniques and benchmarks and find that they're
not very suitable in this problem area. In this paper, we introduce Holographic
Global Convolutional Networks (HGConv) that utilize the properties of
Holographic Reduced Representations (HRR) to encode and decode features from
sequence elements. Unlike other global convolutional methods, our method does
not require any intricate kernel computation or crafted kernel design. HGConv
kernels are defined as simple parameters learned through backpropagation. The
proposed method has achieved new SOTA results on Microsoft Malware
Classification Challenge, Drebin, and EMBER malware benchmarks. With log-linear
complexity in sequence length, the empirical results demonstrate substantially
faster run-time by HGConv compared to other methods achieving far more
efficient scaling even with sequence length $\geq 100,000$.

我们提出了一种新的全局卷积网络方法，利用全息降维表示（HRR）来编码和解码序列元素的特征，并且在微软恶意软件分类挑战、Drebin 和 EMBER 恶意软件基准测试中取得了最新的研究成果，时间复杂度高效。

全息全球卷积网络用于恶意软件检测中的长距离预测任务

Holographic Global Convolutional Networks for Long-Range Prediction  Tasks in Malware Detection

Existing integrity verification approaches for deep models are designed for
private verification (i.e., assuming the service provider is honest, with
white-box access to model parameters). However, private verification approaches
do not allow model users to verify the model at run-time. Instead, they must
trust the service provider, who may tamper with the verification results. In
contrast, a public verification approach that considers the possibility of
dishonest service providers can benefit a wider range of users. In this paper,
we propose PublicCheck, a practical public integrity verification solution for
services of run-time deep models. PublicCheck considers dishonest service
providers, and overcomes public verification challenges of being lightweight,
providing anti-counterfeiting protection, and having fingerprinting samples
that appear smooth. To capture and fingerprint the inherent prediction
behaviors of a run-time model, PublicCheck generates smoothly transformed and
augmented encysted samples that are enclosed around the model's decision
boundary while ensuring that the verification queries are indistinguishable
from normal queries. PublicCheck is also applicable when knowledge of the
target model is limited (e.g., with no knowledge of gradients or model
parameters). A thorough evaluation of PublicCheck demonstrates the strong
capability for model integrity breach detection (100% detection accuracy with
less than 10 black-box API queries) against various model integrity attacks and
model compression attacks. PublicCheck also demonstrates the smooth appearance,
feasibility, and efficiency of generating a plethora of encysted samples for
fingerprinting.

本研究提出了一种公共的完整性验证方案，可以在运行时检验深度模型的完整性，同时提供防伪保护和指纹样本平滑化，具有较强的模型完整性检测能力。

公开验证：运行时深度模型服务的公共诚信验证

PublicCheck: Public Integrity Verification for Services of Run-time Deep Models

Text classification is a significant branch of natural language processing,
and has many applications including document classification and sentiment
analysis. Unsurprisingly, those who do text classification are concerned with
the run-time of their algorithms, many of which depend on the size of the
corpus' vocabulary due to their bag-of-words representation. Although many
studies have examined the effect of preprocessing techniques on vocabulary size
and accuracy, none have examined how these methods affect a model's run-time.
To fill this gap, we provide a comprehensive study that examines how
preprocessing techniques affect the vocabulary size, model performance, and
model run-time, evaluating ten techniques over four models and two datasets. We
show that some individual methods can reduce run-time with no loss of accuracy,
while some combinations of methods can trade 2-5% of the accuracy for up to a
65% reduction of run-time. Furthermore, some combinations of preprocessing
techniques can even provide a 15% reduction in run-time while simultaneously
improving model accuracy.

我们提供了一项综合研究，研究了 10 种预处理技术如何影响词汇量大小、模型性能和模型运行时间等因素，发现一些组合技术可以在不降低正确率的情况下缩短运行时间，并且还有一些组合方法可以缩短运行时间同时提高模型准确性。