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Mind the Gap: On Bridging the Semantic Gap between Machine Learning and Malware Analysis

AISec 2020 - Proceedings of the 13th ACM Workshop on Artificial Intelligence and Security

Smith, Michael R.; Johnson, Nicholas; Ingram, Joe B.; Carbajal, Armida J.; Haus, Bridget I.; Domschot, Eva; Ramyaa, Ramyaa; Lamb, Chris; Verzi, Stephen J.; Kegelmeyer, William P.

Machine learning (ML) techniques are being used to detect increasing amounts of malware and variants. Despite successful applications of ML, we hypothesize that the full potential of ML is not realized in malware analysis (MA) due to a semantic gap between the ML and MA communities-as demonstrated in the data that is used. Due in part to the available data, ML has primarily focused on detection whereas MA is also interested in identifying behaviors. We review existing open-source malware datasets used in ML and find a lack of behavioral information that could facilitate stronger impact by ML in MA. As a first step in bridging this gap, we label existing data with behavioral information using open-source MA reports-1) altering the analysis from identifying malware to identifying behaviors, 2)~aligning ML better with MA, and 3)~allowing ML models to generalize to novel malware in a zero/few-shot learning manner. We classify the behavior of a malware family not seen during training using transfer learning from a state-of-the-art model for malware family classification and achieve 57%-84% accuracy on behavioral identification but fail to outperform the baseline set by a majority class predictor. This highlights opportunities for improvement on this task related to the data representation, the need for malware specific ML techniques, and a larger training set of malware samples labeled with behaviors.

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TYPE Conference Presentation YEAR 2020
DOIOSTIScopus

Permeability prediction of porous media using convolutional neural networks with physical properties

CEUR Workshop Proceedings

Yoon, Hongkyu; Melander, Darryl J.; Verzi, Stephen J.

Permeability prediction of porous media system is very important in many engineering and science domains including earth materials, bio-, solid-materials, and energy applications. In this work we evaluated how machine learning can be used to predict the permeability of porous media with physical properties. An emerging challenge for machine learning/deep learning in engineering and scientific research is the ability to incorporate physics into machine learning process. We used convolutional neural networks (CNNs) to train a set of image data of bead packing and additional physical properties such as porosity and surface area of porous media are used as training data either by feeding them to the fully connected network directly or through the multilayer perception network. Our results clearly show that the optimal neural network architecture and implementation of physics-informed constraints are important to properly improve the model prediction of permeability. A comprehensive analysis of hyperparameters with different CNN architectures and the data implementation scheme of the physical properties need to be performed to optimize our learning system for various porous media system.

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TYPE Conference Poster YEAR 2020
OSTIScopus

Physical Security Assessment Using Temporal Machine Learning

Proceedings - International Carnahan Conference on Security Technology

Sahakian, Meghan A.; Verzi, Stephen J.; Birch, Gabriel C.; Stubbs, Jaclynn J.; Woo, Bryana L.; Kouhestani, Camron G.

Nuisance and false alarms are prevalent in modern physical security systems and often overwhelm the alarm station operators. Deep learning has shown progress in detection and classification tasks, however, it has rarely been implemented as a solution to reduce the nuisance and false alarm rates in a physical security systems. Previous work has shown that transfer learning using a convolutional neural network can provide benefit to physical security systems by achieving high accuracy of physical security targets [10]. We leverage this work by coupling the convolutional neural network, which operates on a frame-by-frame basis, with temporal algorithms which evaluate a sequence of such frames (e.g. video analytics). We discuss several alternatives for performing this temporal analysis, in particular Long Short-Term Memory and Liquid State Machine, and demonstrate their respective value on exemplar physical security videos. We also outline an architecture for developing an ensemble learner which leverages the strength of each individual algorithm in its aggregation. The incorporation of these algorithms into physical security systems creates a new paradigm in which we aim to decrease the volume of nuisance and false alarms in order to allow the alarm station operators to focus on the most relevant threats.

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TYPE Conference Poster YEAR 2018
DOIOSTIScopus
Results 51–75 of 164
Results 51–75 of 164
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