This technical article will teach you how to pre-process data, create your own neural networks, and train and evaluate models using the US-CERT's simulated insider threat dataset. The methods and solutions are designed for non-domain experts; particularly cyber security professionals. We will start our journey with the raw data provided by the dataset and provide examples of different pre-processing methods to get it "ready" for the AI solution to ingest. We will ultimately create models that can be re-used for additional predictions based on security events. Throughout the article, I will also point out the applicability and return on investment depending on your existing Information Security program in the enterprise. Note: To use and replicate the pre-processed data and steps we use, prepare to spend 1–2 hours on this page. Stay with me and try not to fall asleep during the data pre-processing portion. What many tutorials don't state is that if you're starting from scratch; data pre-processing takes up to 90% of your time when doing projects like these. The author provides these methods, insights, and recommendations *as is* and makes no claim of warranty.

We consider the problem of jointly estimating multiple related zero-mean Gaussian distributions from data. We propose to jointly estimate these covariance matrices using Laplacian regularized stratified model fitting, which includes loss and regularization terms for each covariance matrix, and also a term that encourages the different covariances matrices to be close. This method `borrows strength' from the neighboring covariances, to improve its estimate. With well chosen hyper-parameters, such models can perform very well, especially in the low data regime. We propose a distributed method that scales to large problems, and illustrate the efficacy of the method with examples in finance, radar signal processing, and weather forecasting.

We use convolutional neural networks (CNNs) to analyze authorship questions surrounding the works of Leonardo da Vinci -- in particular, Salvator Mundi, the world's most expensive painting and among the most controversial. Trained on the works of an artist under study and visually comparable works of other artists, our system can identify likely forgeries and shed light on attribution controversies. Leonardo's few extant paintings test the limits of our system and require corroborative techniques of testing and analysis.

There have been more than 850,000 confirmed cases and over 48,000 deaths from the human coronavirus disease 2019 (COVID-19) pandemic, caused by novel severe acute respiratory syndrome coronavirus (SARS-CoV-2), in the United States alone. However, there are currently no proven effective medications against COVID-19. Drug repurposing offers a promising way for the development of prevention and treatment strategies for COVID-19. This study reports an integrative, network-based deep learning methodology to identify repurposable drugs for COVID-19 (termed CoV-KGE). Specifically, we built a comprehensive knowledge graph that includes 15 million edges across 39 types of relationships connecting drugs, diseases, genes, pathways, and expressions, from a large scientific corpus of 24 million PubMed publications. Using Amazon AWS computing resources, we identified 41 repurposable drugs (including indomethacin, toremifene and niclosamide) whose therapeutic association with COVID-19 were validated by transcriptomic and proteomic data in SARS-CoV-2 infected human cells and data from ongoing clinical trials. While this study, by no means recommends specific drugs, it demonstrates a powerful deep learning methodology to prioritize existing drugs for further investigation, which holds the potential of accelerating therapeutic development for COVID-19.

Classifier metrics are metrics used to evaluate the performance of machine learning classifiers -- models that put each training example into one of several discrete categories. Confusion Matrix is a matrix used to indicate a classifier's predictions on labels. It contains four cells, each corresponding to one combination of a predicted true or false and an actual true or false. Many classifier metrics are based on the confusion matrix, so it's helpful to keep an image of it stored in your mind. Sensitivity/Recall is the number of positives that were accurately predicted.

The last time I traveled along Stadium Way I was headed to a Dodger game, but on Monday afternoon I drove to the fire training center near the ballpark for a much less enjoyable experience. Just a cotton swab and a five-minute drive-through, with results to follow in a few days. I was conflicted about being tested, for two reasons. First, while we definitely needed to ramp up testing back at the beginning of this crisis, I'm wondering if the county has now gone overboard in offering free testing to all residents, whether or not they have symptoms. Second, I'm pretty sure that my minor allergy-like symptoms are just that: allergies.

Moog Inc. has automated the evaluation of copper (Cu) alloy grain size using a deep-learning convolutional neural network (CNN). The proof-of-concept automated image acquisition and batch-wise image processing offers the potential for significantly reduced labor, improved accuracy of grain evaluation, and decreased overall turnaround times for approving Cu alloy bar stock for use in flight critical aircraft hardware. A classification accuracy of 91.1% on individual sub-images of the Cu alloy coupons was achieved. Process development included minimizing the variation in acquired image color, brightness, and resolution to create a dataset with 12300 sub-images, and then optimizing the CNN hyperparameters on this dataset using statistical design of experiments (DoE). Over the development of the automated Cu alloy grain size evaluation, a degree of "explainability" in the artificial intelligence (XAI) output was realized, based on the decomposition of the large raw images into many smaller dataset sub-images, through the ability to explain the CNN ensemble image output via inspection of the classification results from the individual smaller sub-images.

Personal photos of individuals when shared online, apart from exhibiting a myriad of memorable details, also reveals a wide range of private information and potentially entails privacy risks (e.g., online harassment, tracking). To mitigate such risks, it is crucial to study techniques that allow individuals to limit the private information leaked in visual data. We tackle this problem in a novel image obfuscation framework: to maximize entropy on inferences over targeted privacy attributes, while retaining image fidelity. We approach the problem based on an encoder-decoder style architecture, with two key novelties: (a) introducing a discriminator to perform bi-directional translation simultaneously from multiple unpaired domains; (b) predicting an image interpolation which maximizes uncertainty over a target set of attributes. We find our approach generates obfuscated images faithful to the original input images, and additionally increase uncertainty by 6.2$\times$ (or up to 0.85 bits) over the non-obfuscated counterparts.

A branch of artificial intelligence called machine learning is all around us. It's employed by Facebook to help curate content (and target us with ads), Google uses it to filter millions of spam messages each day, and it's part of what enabled the OpenAI bot to beat the reigning Dota 2 champions last year in two out of three matches. There are seemingly endless uses. Adding one more to the pile, Microsoft and Intel have come up with a clever machine learning framework that is surprisingly accurate at detecting malware through a grayscale image conversion process. Microsoft detailed the technology in a blog post (via ZDNet), which it calls static malware-as-image network analysis, or STAMINA.

Adversarial examples are data points misclassified by neural networks. Originally, adversarial examples were limited to adding small perturbations to a given image. Recent work introduced the generalized concept of unrestricted adversarial examples, without limits on the added perturbations. In this paper, we introduce a new category of attacks that create unrestricted adversarial examples for object detection. Our key idea is to generate adversarial objects that are unrelated to the classes identified by the target object detector. Different from previous attacks, we use off-the-shelf Generative Adversarial Networks (GAN), without requiring any further training or modification. Our method consists of searching over the latent normal space of the GAN for adversarial objects that are wrongly identified by the target object detector. We evaluate this method on the commonly used Faster R-CNN ResNet-101, Inception v2 and SSD Mobilenet v1 object detectors using logo generative iWGAN-LC and SNGAN trained on CIFAR-10. The empirical results show that the generated adversarial objects are indistinguishable from non-adversarial objects generated by the GANs, transferable between the object detectors and robust in the physical world. This is the first work to study unrestricted false positive adversarial examples for object detection.