  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 $\geq 100,000$.
