Lempel-Ziv Networks
Rebecca Saul, Mohammad Mahmudul Alam, John Hurwitz, Edward Raff, Tim, Oates, James Holt

TL;DR
This paper explores the adaptation of the Lempel-Ziv Jaccard Distance into a deep learning framework, the Lempel-Ziv Network, aiming to improve sequence processing for long sequences, but finds it does not outperform standard LSTMs.
Contribution
The paper introduces the Lempel-Ziv Network as a deep learning analog of LZJD for continuous domains, providing a proof of concept and discussing baseline tuning issues.
Findings
Lempel-Ziv Network does not outperform LSTMs on tested tasks.
Successful proof of concept achieved for deep-learning adaptation.
Highlights issues with baseline tuning in new research areas.
Abstract
Sequence processing has long been a central area of machine learning research. Recurrent neural nets have been successful in processing sequences for a number of tasks; however, they are known to be both ineffective and computationally expensive when applied to very long sequences. Compression-based methods have demonstrated more robustness when processing such sequences -- in particular, an approach pairing the Lempel-Ziv Jaccard Distance (LZJD) with the k-Nearest Neighbor algorithm has shown promise on long sequence problems (up to steps) involving malware classification. Unfortunately, use of LZJD is limited to discrete domains. To extend the benefits of LZJD to a continuous domain, we investigate the effectiveness of a deep-learning analog of the algorithm, the Lempel-Ziv Network. While we achieve successful proof of concept, we are unable to improve meaningfully on…
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Taxonomy
TopicsAdvanced Malware Detection Techniques · Anomaly Detection Techniques and Applications · Network Security and Intrusion Detection
MethodsSigmoid Activation · Tanh Activation · Long Short-Term Memory
