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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Independent malware detection architecture

A system and method (referred to as the system) detect malware by training a rule-based model, a functional based model, and a deep learning-based model from a memory snapshot of a malware free operating state of a monitored device. The system extracts a feature set from a second memory snapshot captured from an operating state of the monitored device and processes the feature set by the rule-based model, the functional-based model, and the deep learning-based model. The system identifies identifying instances of malware on the monitored device without processing data identifying an operating system of the monitored device, data associated with a prior identification of the malware, data identifying a source of the malware, data identifying a location of the malware on the monitored device, or any operating system specific data contained within the monitored device.

Smith, Jared M.↗
DOE OSTI2021

Tensor Text-Mining Methods for Malware Identification and Detection, Malware Dynamics Characterization, and Hosts Ranking

Malware is one of the most persistent and costly cyber threats endangering reputation, confidentiality, integrity, and availability for organizations and national security. Consequently, many of the incident detection and prevention systems, and incident responders have begun to utilize machine learning as a helper in the fight against malware and other cyber threats. However, cyber defenders rely on interpretability and generalizability, yet the popular machine learning methods are black-box and often use traditional supervised solutions that do not generalize to novel malware. Therefore, there is a need to improve the existing solutions. At the same time, the majority of the prior research ignored essential evaluation criteria when reporting the results of their methods, which disables the safe reproducibility of the methods in a production environment. Tensor decomposition, on the other hand, enables interpretable unsupervised analysis of the large-scale data for the discovery of hidden patterns. Our findings, performed on real-world and large-scale experiments, show that tensor factorization-based methods yield performance results that surpasses or competes with existing supervised solutions with the added benefit of interpretability and generalizability. With the ability to analyze complex and large-scale data using tensors, we report results that reflect real-world production environments. We propose to develop new game- changing tools for malware identification and characterization that can trace malware evolution, rank the infected or malicious hosts, and streamline the work of incident response teams, malware analysts, and incident detection and prevention systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Device-Centric Firmware Malware Detection for Smart Inverters using Deep Transfer Learning

Since future power grids are inverter-dominant grids and inverters are getting smarter by incorporating remote access and seamless firmware update, it is anticipated that malware attackers will directly target smart inverters. However, malware threats targeting smart inverters have been less studied yet. This paper explores potential malware attacks targeting smart inverters and proposes a deep transfer-learning (DTL)-based malware detection framework for smart inverters. The proposed DTL method can significantly reduce development time and efforts for an artificial intelligence-based malware detection algorithm while improving detection accuracy. The experimental result shows that the proposed method achieves 98% of firmware malware detection accuracy. Furthermore, this approach will be transformative to other smart grid devices enabling seamless firmware update.

artificial intelligence↗
DOE OSTI2018

Towards Malware Detection via CPU Power Consumption: Data Collection Design and Analytics

This paper presents an experimental design and data analytics approach aimed at power-based malware detection on general-purpose computers. Leveraging the fact that malware executions must consume power, we explore the postulate that malware can be accurately detected via power data analytics. Our experimental design and implementation allow for programmatic collection of CPU power profiles for fixed tasks during uninfected and infected states using five different rootkits. To characterize the power consumption profiles, we use both simple statistical and novel, sophisticated features. We test a one-class anomaly detection ensemble (that baselines non-infected power profiles) and several kernel-based SVM classifiers (that train on both uninfected and infected profiles) in detecting previously unseen malware and clean profiles. The anomaly detection system exhibits perfect detection when using all features and tasks, with smaller false detection rate than the supervised classifiers. The primary contribution is the proof of concept that baselining power of fixed tasks can provide accurate detection of rootkits. Moreover, our treatment presents engineering hurdles needed for experimentation and allows analysis of each statistical feature individually. This work appears to be the first step towards a viable power-based detection capability for general-purpose computers, and presents next steps toward this goal.

Bridges, Robert↗
DOE OSTI2020

System and method for monitoring power consumption to detect malware

A system and method (referred to as the system) detects malware, viruses, and/or malicious activity by generating a direct current source power consumption profile by causing a monitored device to execute a fully automated recurrent software operation. The system receives by an automated detection system, the direct current source power consumption profile generated by an intelligent power sensor and generates by a detection engine, a power security profile that identifies suspicious code by profiling direct current consumed by monitored type devices. The system executes a detection engine remote from the monitored device that identifies an infected device.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

A Dynamic Hierarchical Attention Framework for Multimodal Malware Detection

The increasing use of Android in the worldwide mobile ecosystem has come along with a significant increase in advanced malware, highlighting the critical necessity for efficient, scalable, and adaptable detection systems. Despite recent advancements in machine learning improving malware detection, the majority of current solutions are limited to one, two, or three data modalities, hence neglecting the comprehensive behavioral spectrum of contemporary multi-vector threats. This thesis presents the first comprehensive multimodal framework for Android malware detection, which combines textual, time-series (temporal), graph-based (structural), and visual information using an innovative hierarchical attention mechanism and Dynamic Fusion Controller (DFC). Our methodology consistently classifies and processes modalities as either sequential or structural, facilitating content-adaptive weighting and resilient cross-modal representation learning. We advance the implementation of cutting-edge time series techniques, such as MiniRocket, for malware detection, hence creating new opportunities for temporal analysis in cybersecurity. Comprehensive experimental assessment shows that our framework performs exceptionally well, with 99.46% classification accuracy and 97.15% detection accuracy, significantly outperforming existing approaches through effective multimodal integration and hierarchical attention mechanisms.

Nazmin, Tamanna↗
DOE OSTI2025

Adversarial Binaries: AI-guided Instrumentation Methods for Malware Detection Evasion

Adversarial binaries are executable files that have been altered without loss of function by an AI agent in order to deceive malware detection systems. Progress in this emergent vein of research has been constrained by the complex and rigid structure of executable files. Although prior work has demonstrated that these binaries deceive a variety of malware classification models which rely on disparate feature sets, a consensus as to the best approach has not been reached, either in terms of the optimization algorithms or the instrumentation methods. Furthermore, although inconsistencies in the data sets, target classifiers, and functionality verification methods make head-to-head comparisons difficult, here we extract lessons learned and make recommendations for future research.

malware obfuscation↗
DOE OSTI2019

Heartbeat Malware Detection

Legacy signature or heuristics-based solutions for malware infections and cyber-attacks are unable to keep up with the flood of new polymorphic malware samples, or to address the powerful and stealthy tactics of kernel-level rootkits. Heartbeat responds to this problem by focusing instead on the physical behavior of the device being protected, under the hypothesis that malware infection will produce a measurable change in the power consumption state of a device that can be picked up by an outside detector.

Dawson, Joel A↗
DOE OSTI2022

Heartbeat: Detecting Malware by Periodic Power Signal Injection and Monitoring

Rootkits and other stealthy malware attempt to conceal their presence on a computer by making changes to the host computer’s operating environment. ORNL’s Heartbeat technology detects these changes, and thus the malware itself. Heartbeat operates by directly monitoring the DC power consumption of the computer while a set of operations, the “heartbeat,” is executed periodically. These operations exercise parts of the operating system that are common targets of malware tampering. The power consumption during these heartbeat events is monitored and then compared to a previously learned baseline, with any significant deviation detected and analyzed. This technology has been tested and validated in a laboratory environment, and ORNL is currently seeking a deployment partner to allow for further in-context development and testing of this technology.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Towards Architecture and OS-Independent Malware Detection via Memory Forensics

In this work, we take a fundamentally different approach to the problem of analyzing a device for compromises via malware; our approach is OS and instruction architecture independent and relies only on having the raw binary data extracted from the memory dump of a device. Our system leverages a multi-hundred TB dataset of both compromised host memory dumps extracted from the MalRec dataset [8] and the first known dataset of benign host memory dumps running normal, non-compromised software. After an average of 30 to 45 seconds of pre-processing on a single memory dump, our system leverages both traditional machine learning and deep learning algorithms to achieve an average of 98% accuracy of detecting a compromised host.

Petrik, Rachel L.↗
DOE OSTI2024

Advanced Data Science Model for Detecting Intelligent Malware

This study focused on developing a robust artificial intelligence (AI) model capable of detecting and characterizing advanced malware in Internet of Things (IoT) devices using network data. By analyzing network traffic with various machine learning (ML) models, our AI model can identify and characterize malicious activities to significantly improve malware detection accuracy and reliability as compared to traditional methods. The developed AI/ML model was trained using network data from IoT devices, leveraging classifiers such as Random Forest, Gradient Boosting, AdaBoost, and others to optimize detection performance. This project demonstrates a scalable framework for real-time malware detection and characterization in IoT networks, capable of identifying infected devices and facilitating the necessary steps to remove or isolate them, thereby preventing further infections. Although digital twin (DT) integration is not yet implemented in the current model, it represents a promising future enhancement. By creating a virtual replica of physical IoT devices, DT technology would allow for real-time monitoring and analysis without directly accessing operational technology, thus reducing the risk of compromising or reducing the performance of actual devices. This integration would further enhance the security of IoT ecosystems, combining AI technology to better flag and detect indications of malware-infected devices within a nuclear system environment.

42 ENGINEERING↗