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

The Microsoft Malware Classification Challenge was announced in 2015 along
with a publication of a huge dataset of nearly 0.5 terabytes, consisting of
disassembly and bytecode of more than 20K malware samples. Apart from serving
in the Kaggle competition, the dataset has become a standard benchmark for
research on modeling malware behaviour. To date, the dataset has been cited in
more than 50 research papers. Here we provide a high-level comparison of the
publications citing the dataset. The comparison simplifies finding potential
research directions in this field and future performance evaluation of the
dataset.

比较引用 Microsoft 恶意软件分类挑战数据集的出版物，为该领域的潜在研究方向和未来数据集的性能评估提供高水平的比较。