# ALOHA: Auxiliary Loss Optimization for Hypothesis Augmentation

arXiv: https://arxiv.org/abs/1903.05700 · HTML: https://mlsanity.com/paper/1903.05700/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2019.

## Built on by (most cited first, 9 of 9)

- [Leveraging Uncertainty for Improved Static Malware Detection Under Extreme False Positive Constraints](https://mlsanity.com/paper/2108.04081/) (2021)
- [Quo Vadis: Hybrid Machine Learning Meta-Model based on Contextual and Behavioral Malware Representations](https://mlsanity.com/paper/2208.12248/) (2022)
- [Efficient Malware Analysis Using Metric Embeddings](https://mlsanity.com/paper/2212.02663/) (2022)
- [Transformers for End-to-End InfoSec Tasks: A Feasibility Study](https://mlsanity.com/paper/2212.02666/) (2022)
- [Towards a Practical Defense against Adversarial Attacks on Deep Learning-based Malware Detectors via Randomized Smoothing](https://mlsanity.com/paper/2308.08906/) (2023)
- [URET: Universal Robustness Evaluation Toolkit (for Evasion)](https://mlsanity.com/paper/2308.01840/) (2023)
- [Stealing and Evading Malware Classifiers and Antivirus at Low False Positive Conditions](https://mlsanity.com/paper/2204.06241/) (2022)
- [Magnificent Minified Models](https://mlsanity.com/paper/2306.10177/) (2023)
- [Machine Learning Transferability for Malware Detection](https://mlsanity.com/paper/2603.26632/) (2026)

## Abstract (arXiv)

Malware detection is a popular application of Machine Learning for Information Security (ML-Sec), in which an ML classifier is trained to predict whether a given file is malware or benignware. Parameters of this classifier are typically optimized such that outputs from the model over a set of input samples most closely match the samples' true malicious/benign (1/0) target labels. However, there are often a number of other sources of contextual metadata for each malware sample, beyond an aggregate malicious/benign label, including multiple labeling sources and malware type information (e.g., ransomware, trojan, etc.), which we can feed to the classifier as auxiliary prediction targets. In this work, we fit deep neural networks to multiple additional targets derived from metadata in a threat intelligence feed for Portable Executable (PE) malware and benignware, including a multi-source malicious/benign loss, a count loss on multi-source detections, and a semantic malware attribute tag loss. We find that incorporating multiple auxiliary loss terms yields a marked improvement in performance on the main detection task. We also demonstrate that these gains likely stem from a more informed neural network representation and are not due to a regularization artifact of multi-target learning. Our auxiliary loss architecture yields a significant reduction in detection error rate (false negatives) of 42.6% at a false positive rate (FPR) of 10-3 when compared to a similar model with only one target, and a decrease of 53.8% at 10-5 FPR.