Towards a relation extraction framework for cyber-security concepts

In order to assist security analysts in obtaining information pertaining to their network, such as novel vulnerabilities, exploits, or patches, information retrieval methods tailored to the security domain are needed. As labeled text data is scarce and expensive, we follow developments in semi-supervised Natural Language Processing and implement a bootstrapping algorithm for extracting security entities and their relationships from text. The algorithm requires little input data, specifically, a few relations or patterns (heuristics for identifying relations), and incorporates an active learning component which queries the user on the most important decisions to prevent drifting from the desired relations. Preliminary testing on a small corpus shows promising results, obtaining precision of .82.Comment: 4 pages in Cyber & Information Security Research Conference 2015, AC

Semi-supervised source localization with deep generative modeling

We propose a semi-supervised localization approach based on deep generative modeling with variational autoencoders (VAEs). Localization in reverberant environments remains a challenge, which machine learning (ML) has shown promise in addressing. Even with large data volumes, the number of labels available for supervised learning in reverberant environments is usually small. We address this issue by performing semi-supervised learning (SSL) with convolutional VAEs. The VAE is trained to generate the phase of relative transfer functions (RTFs), in parallel with a DOA classifier, on both labeled and unlabeled RTF samples. The VAE-SSL approach is compared with SRP-PHAT and fully-supervised CNNs. We find that VAE-SSL can outperform both SRP-PHAT and CNN in label-limited scenarios.Comment: Published in proceedings of IEEE International Workshop on Machine Learning for Signal Processing. arXiv admin note: substantial text overlap with arXiv:2101.1063

An Exploration of Semi-supervised Text Classification

Author's accepted manuscriptGood performance in supervised text classification is usually obtained with the use of large amounts of labeled training data. However, obtaining labeled data is often expensive and time-consuming. To overcome these limitations, researchers have developed Semi-Supervised learning (SSL) algorithms exploiting the use of unlabeled data, which are generally easy and free to access. With SSL, unlabeled and labeled data are combined to outperform Supervised-Learning algorithms. However, setting up SSL neural networks for text classification is cumbersome and frequently based on a trial and error process. We show that the hyperparameter configuration significantly impacts SSL performance, and the learning rate is the most influential parameter. Additionally, increasing model size also improves SSL performance, particularly when less pre-processing data are available. Interestingly, as opposed to feed-forward models, recurrent models generally reach a performance threshold as pre-processing data size increases. This article expands the knowledge on hyperparameters and model size in relation to SSL application in text classification. This work supports the use of SSL work in future NLP projects by optimizing model design and potentially lowering training time, particularly if time-restricted.acceptedVersio

PACE: Pattern Accurate Computationally Efficient Bootstrapping for Timely Discovery of Cyber-Security Concepts

Public disclosure of important security information, such as knowledge of vulnerabilities or exploits, often occurs in blogs, tweets, mailing lists, and other online sources months before proper classification into structured databases. In order to facilitate timely discovery of such knowledge, we propose a novel semi-supervised learning algorithm, PACE, for identifying and classifying relevant entities in text sources. The main contribution of this paper is an enhancement of the traditional bootstrapping method for entity extraction by employing a time-memory trade-off that simultaneously circumvents a costly corpus search while strengthening pattern nomination, which should increase accuracy. An implementation in the cyber-security domain is discussed as well as challenges to Natural Language Processing imposed by the security domain.Comment: 6 pages, 3 figures, ieeeTran conference. International Conference on Machine Learning and Applications 201