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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Secure boot, trusted boot and remote attestation for ARM TrustZone-based IoT Nodes

With the extensive application of IoT techniques, IoT devices have become ubiquitous in daily lives. Meanwhile, attacks against IoT devices have emerged to compromise IoT devices by tampering with system pre-installed programs or injecting new malware. To mitigate these attacks, integrity enforcement of IoT systems has been proposed. The integrity of an IoT device system includes load-time integrity and runtime integrity. In this paper, we design an IoT system based on ARM TrustZone to enforce the system integrity. First, we establish the root of trust and propose a hybrid booting approach consisting of both secure boot and trusted boot to enforce the system load-time integrity. Second, we investigate a paging-based process integrity measurement method to measure the NW processes and conduct remote attestation based on the measurement results ensuring the NW runtime process integrity. We implement an IoT prototype system on a NXP i.MX6Q SABRE SD development board to assess its feasibility. Finally, real-world experiment results demonstrate that our prototype introduces negligible performance overhead to the original system.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

SeqMask: Behavior Extraction Over Cyber Threat Intelligence Via Multi-Instance Learning

Abstract Identification and extraction of Tactics, Techniques and Procedures (TTPs) for Cyber Threat Intelligence (CTI) restore the full picture of cyber attacks and guide the analysts to assess the system risk. Existing frameworks can hardly provide uniform and complete processing mechanisms for TTPs information extraction without adequate knowledge background. A multi-instance learning approach named SeqMask is proposed in this paper as a solution. SeqMask extracts behavior keywords from CTI evaluated by the semantic impact, and predicts TTPs labels by conditional probabilities. Still, the framework has two mechanisms to determine the validity of keywords. One using expert experience verification. The other verifies the distortion of the classification effect by blocking existing keywords. In the experiments, SeqMask reached 86.07% and 73.99% in F1 scores for TTPs classifications. For the top 20% of keywords, the expert approval rating is 92.20%, where the average repetition of keywords whose scores between 100% and 90% is 60.02%. Particularly, when the top 65% of the keywords were blocked, the F1 decreased to about 50%; when removing the top 50%, the F1 was under 31%. Further, we also validate the possibility of extracting TTPs from full-size CTI and malware whose F1 are improved by 2.16% and 0.81%.

Ge, Wenhan↗
DOE OSTI2016

Towards Reducing the Data Exfiltration Surface for the Insider Threat

Unauthorized data exfiltrations from both insiders and outsiders are costly and damaging. Network communication resources can be used for transporting data illicitly out of the enterprise or cloud. Combined with built-in malware copying utilities, we define this as comprising the Data Exfiltration Surface (DXS). For securing valuable data, it is desirable to reduce the DXS and maintain controls on the egress points. Our approach is to host the data in a protected enclave that includes novel software Data Diode (SDD) installed on a secured, border gateway. The SDD allows copying data into the enclave systems but denies data from being copied out. Simultaneously, it permits remote access with remote desktop and console applications. Our tests demonstrate that we are able to effectively reduce the DXS and we are able to protect data from being exfiltrated through the use of the SDD.

Schlicher, Bob G↗
DOE OSTI2025

AI-based Cyber Event OSINT via Twitter Data

Open-Source Intelligence (OSINT) is largely regarded as a necessary component for cybersecurity intelligence gathering to secure network systems. With the advancement of artificial intelligence (AI) and increasing usage of social media, like Twitter, we have a unique opportunity to obtain and aggregate information from social media. In this study, we propose an AI-based scheme capable of automatically pulling information from Twitter, filtering out security-irrelevant tweets, performing natural language analysis to correlate the tweets about each cybersecurity event (e.g., a malware campaign), and validating the information. This scheme has many applications, such as providing a means for security operators to gain insight into ongoing events and helping them prioritize vulnerabilities to deal with. To give examples of the possible uses, we present three case studies demonstrating the event discovery and investigation processes.

Dale, Dakota↗
DOE OSTI2019

A Multidimensional Holistic Framework for the Security of Distributed Energy and Control Systems

The digitization of smart grid distributed generation and industrial control systems has prompted utilities to deploy tools with ubiquitous communications that potentially widen the attack surface. The utilities still continue to rely on the traditional cybersecurity technologies, such as firewalls, anti-malware tools, and passwords that do not ensure security across all dimensions of the information assurance model required for a strong cybersecurity business process. This paper proposes a multidimensional holistic framework that addresses this gap through advanced technologies, intelligent algorithms, and continued assessments. To show proof, the layered defense model, a solution dimension of the framework, is integrated into the National Renewable Energy Laboratory's Security and Resilience Testbed to replicate a utility's enterprise and substation networks. The model is used to evaluate the security and resilience of microgrid control systems, and, based on the insights gathered, recommend best practices for utility cybersecurity analysts for a strong business process. Here, with this baseline, the paper conceptually introduces intelligence-driven solutions comprising contextual data analysis and machine learning to respond to advanced persistent threats sponsored by campaign efforts.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

Pattern Discovery in Internet Background Radiation

Internet Background Radiation (IBR) is observed in empty network address spaces. No traffic should arrive there, but it does in overwhelming quantities, gathering evidences of attacks, malwares and misconfigurations. The study of IBR helps to detect spreading network problems, common vulnerabilities and attack trends. However, network traffic data evolves quickly and is of high volume and diversity, i.e., an outstanding big data challenge. When used to assist network security, it also requires the online classification of dynamic streaming data. In this paper, we introduce an AGgregation & Mode (AGM) vector to represent network traffic. The AGM format characterizes IP hosts by extracting aggregated and mode values of IP header fields, and without inspecting payloads. We performed clustering and statistical analysis to explore six months of IBR from 2012 with the AGM mapping. The discovered patterns allow building a classification of IBR, which identifies phenomena that have been actively polluting the Internet for years. The AGM representation is light and tailored for monitoring and pattern discovery. We show that AGM vectors are suitable to analyze large volumes of network traffic: they capture permanent operations, such as long term scanning, as well as bursty events from targeted attacks and short term incidents.

Iglesias, Felix↗
DOE OSTI2017

Akatosh: Automated Cyber Incident Verification and Impact Analysis

Akatosh, a U.S. Department of Homeland Security Transition to Practice Program (TTP) project developed by Oak Ridge National Laboratory with industry and academic partnership, enables automated, real-time forensic analysis of endpoints after malware-attacks and other cyber security incidents by automatically maintaining detailed snapshots of host-level activity on endpoints over time. It achieves this by integrating intrusion detection systems (IDS) with forensic tools. The combination allows Akatosh to collect vast amounts of endpoint data and assists in verifying, tracking, and analyzing endpoints in real time. This provides operations personnel and analysts as well as managers and executives with continuous feedback on the impact of malicious software and other security incidents on endpoints in their network.

Smith, Jared M.↗
DOE OSTI2018

Recurrent Neural Network Attention Mechanisms for Interpretable System Log Anomaly Detection

Deep learning has recently demonstrated state-of-the art performance on key tasks related to the maintenance of computer systems, such as intrusion detection, denial of service attack detection, hardware and software system failures, and malware detection. In these contexts, model interpretability is vital for administrator and analyst to trust and act on the automated analysis of machine learning models. Deep learning methods have been criticized as black box oracles which allow limited insight into decision factors. In this work we seek to "bridge the gap'' between the impressive performance of deep learning models and the need for interpretable model introspection. To this end we present recurrent neural network (RNN) language models augmented with attention for anomaly detection in system logs. Our methods are generally applicable to any computer system and logging source. By incorporating attention variants into our RNN language models we create opportunities for model introspection and analysis without sacrificing state-of-the art performance. We demonstrate model performance and illustrate model interpretability on an intrusion detection task using the Los Alamos National Laboratory (LANL) cyber security dataset, reporting upward of 0.99 area under the receiver operator characteristic curve despite being trained only on a single day's worth of data.

Anomaly detection, Attention, Recurrent Neural Net↗
DOE OSTI2018

Tools, Techniques, and Methodologies: A Survey of Digital Forensics for SCADA Systems

Security aspects of SCADA environments and the systems within are increasingly a center of interest to researchers and security professionals. As the rise of sophisticated and nation-state malware targeting such systems flourishes, traditional digital forensics tools struggle to transfer the same capabilities to systems lacking typical volatile memory primitives, monitoring software, and the compatible operating-system primitives necessary for conducting forensic investigations. Even worse, SCADA systems are typically not designed and implemented with security in mind, nor were they purpose-built to monitor and record system data at the granularity associated with traditional IT systems. Rather, these systems are often built to control field devices and drive industrial processes. More succinctly, SCADA systems were not designed with a primary goal of interacting with the digital world. Consequently, forensics investigators well-versed in the world of digital forensics and incident response face an array of challenges that prevent them from conducting effective forensic investigation in environments with vast amounts of critical infrastructure. In order to bring SCADA systems within the reach of the armies of digital forensics professionals and tooling already available, both researchers and practitioners need a guide to the current state-of-the-art techniques, a road-map to the challenges lying on the path forward, and insight into the future directions R&D must move towards. To that end, this paper presents a survey into the literature on digital forensics applied to SCADA systems. We cover not only the challenges to applying digital forensics to SCADA like most other reviews, but also the range of proposed frameworks, methodologies, and actual implementations in literature.

Asmar Awad, Rima↗
DOE OSTI2021

Metagames and Hypergames for Deception-Robust Control

Cyber-physical systems (CPSs) consist of computing and communication devices integrated with physical components such as sensors and actuators. Increasing connectivity to the Internet for remote monitoring and control has made CPSs more vulnerable to deliberate attacks, which are distinctly different from random perturbations in the system. This provides a way for purely cyber attacks to have physical consequences. Stuxnet is a prominent example of such an attack, one in which the malware acted over an extended period of time while deliberately remaining undetected. Such attacks can be described as Advanced Persistent Threats (APTs) -- long-term, stealthy attacks. Here, we extend our previous work on hypergames to develop defender strategies that are robust to deception and do not rely on attack detection. We prove that the defender can bound the attacker payoff with these strategies even when the attacker can choose between different attack modes, and we numerically demonstrate our approach on a realistic building control system. Finally, we discuss next steps in extending this approach towards an operational capability.

hypergames, cyber-physical systems, robust control↗
DOE OSTI2023

Accelerating Random Forest Classification on GPU and FPGA

Random Forests (RFs) are a commonly used machine learning method for classification and regression tasks spanning a variety of application domains, including bioinformatics, business analytics, and software optimization. While prior work has focused primarily on improving performance of the training of RFs, many applications, such as malware identification, cancer prediction, and banking fraud detection, require fast RF classification. In this work, we accelerate RF classification on GPU and FPGA. In order to provide efficient support for large datasets, we propose a hierarchical memory layout suitable to the GPU/FPGA memory hierarchy. We design three RF classification code variants based on that layout, and we investigate GPU- and FPGA-specific considerations for these kernels. Our experimental evaluation, performed on an Nvidia Xp GPU and on a Xilinx Alveo U250 FPGA accelerator card using publicly available datasets on the scale of millions of samples and tens of features, covers various aspects. First, we evaluate the performance benefits of our hierarchical data structure over the standard compressed sparse row (CSR) format. Second, we compare our GPU implementation with cuML, a machine learning library targeting Nvidia GPUs. Third, we explore the performance/accuracy tradeoff resulting from the use of different tree depths in the RF. Finally, we perform a comparative performance analysis of our GPU and FPGA implementations. Our evaluation shows that for high accuracy targets, our GPU implementation yields 5-9x speedup over CSR, and up to a 2x speedup over cuML.

FPGA, Xilinx FPGA, GPU, Random Forest classificati↗
DOE OSTI2022

Binary Driller

Binary Driller (BD) is a visualization tool which uses the data produced from the Troglodyte tool developed on the Deep Learning Malware project. Binary Driller performs function matching using the provided function embeddings (function representations), then displays the matches for each function in a layout that mimics the location of each function within the binary.

Cutsha, Michael↗
DOE OSTI2025

OGhidra

This is an AI driven Binary Analysis tool. It uses locally hosted Agentic AI's to automatically reverse engineer binaries to find malware and vulnerabilities.

Wang, Enoch↗
DOE OSTI2014

Hybrid methods for cybersecurity analysis :

Early 2010 saw a signi cant change in adversarial techniques aimed at network intrusion: a shift from malware delivered via email attachments toward the use of hidden, embedded hyperlinks to initiate sequences of downloads and interactions with web sites and network servers containing malicious software. Enterprise security groups were well poised and experienced in defending the former attacks, but the new types of attacks were larger in number, more challenging to detect, dynamic in nature, and required the development of new technologies and analytic capabilities. The Hybrid LDRD project was aimed at delivering new capabilities in large-scale data modeling and analysis to enterprise security operators and analysts and understanding the challenges of detection and prevention of emerging cybersecurity threats. Leveraging previous LDRD research e orts and capabilities in large-scale relational data analysis, large-scale discrete data analysis and visualization, and streaming data analysis, new modeling and analysis capabilities were quickly brought to bear on the problems in email phishing and spear phishing attacks in the Sandia enterprise security operational groups at the onset of the Hybrid project. As part of this project, a software development and deployment framework was created within the security analyst work ow tool sets to facilitate the delivery and testing of new capabilities as they became available, and machine learning algorithms were developed to address the challenge of dynamic threats. Furthermore, researchers from the Hybrid project were embedded in the security analyst groups for almost a full year, engaged in daily operational activities and routines, creating an atmosphere of trust and collaboration between the researchers and security personnel. The Hybrid project has altered the way that research ideas can be incorporated into the production environments of Sandias enterprise security groups, reducing time to deployment from months and years to hours and days for the application of new modeling and analysis capabilities to emerging threats. The development and deployment framework has been generalized into the Hybrid Framework and incor- porated into several LDRD, WFO, and DOE/CSL projects and proposals. And most importantly, the Hybrid project has provided Sandia security analysts with new, scalable, extensible analytic capabilities that have resulted in alerts not detectable using their previous work ow tool sets.

Dunlavy, Daniel M.↗
DOE OSTI2015

UVI Cyber-security Workshop Workshop Analysis.

The cybersecurity consortium, which was established by DOE/NNSA’s Minority Serving Institutions Partnerships Program (MSIPP), allows students from any of the partner schools (13 HBCUs, two national laboratories, and a public school district) to have all consortia options available to them, to create career paths and to open doors to DOE sites and facilities to student members of the consortium. As a part of this year consortium activities, Sandia National Laboratories and the University of Virgin Islands conducted a week long cyber workshop that consisted of three courses; Digital Forensics and Malware Analysis, Python Programming, and ThunderBird Cup. These courses are designed to enhance cyber defense skills and promote learning within STEM related fields.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Watchdog Project

The Watchdog Project completed 100% of the project Statement of Project Objective (SOPO). The Watchdog project was a very aggressive project looking to accomplish commercialization of technology that had never been commercialized, as a result it took six years to complete not the original three that were planned. No additional federal funds were requested from the original proposal and SEL contributed the additional cost share required to complete the project. The result of the Watchdog Project is the world’s first industrial rated Software Defined Network (SDN) switch commercially available. This technology achieved the SOPOO and DOE Roadmap goals to have strong network access control, improve reliability and network performance, and give the asset owner the ability to minimize attack surface before and during an attack. The Watchdog project is an alliance between CenterPoint Energy Houston Electric, Pacific Northwest National Laboratories (PNNL), and Schweitzer Engineering Laboratories, Inc. (SEL). SEL is the world’s leader in microprocessor-based electronic equipment for protecting electric power systems. PNNL performs basic and applied research to deliver energy, environmental, and national security for our nation. CenterPoint Energy is the third largest publicly traded natural gas delivery company in the U.S and third largest combined electricity and natural gas delivery company. The Watchdog Project efforts were combined with the SDN Project efforts to produce the entire SDN system solution for the critical infrastructure. The Watchdog project addresses Topic Area of Interest 5: Secure Communications, for the DEFOA- 0000359 by protecting the control system local area network itself and the communications coming from and going to the electronic devices on the local network. Local area networks usually are not routed and have little or no filtering capabilities. Combine this with the fact control system protocols are designed with inherent trust the control system owners have very little choice on how to protect communications on the local network. The Watchdog project reduces security risks in electric sector control system local area networks (LANs) by providing: Network access control (NAC) Multi-Layer firewall (physical through transport layer) Containment of malware or unauthorized traffic spreading across the network White list protocols and application message types filtering Configurable, proactive traffic engineering The Watchdog project achieved all of the above by developing an SDN switch.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

After Action Report: Idaho National Laboratory Continuity of Operations Program 2017 Exercise September 26, 2017

On September 26, 2017, the Battelle Energy Alliance, LLC (BEA) Continuity of Operations Program (COOP) conducted an annual exercise to demonstrate the COOP responder’s ability to maintain COOP’s primary mission essential function (PMEF) and essential supporting activities (ESAs) during a national loss of commercial power caused by a malware attack to the United States power grid. The continuity emergency management team (CEMT) and continuity emergency response group (CERG) responded to CERG notifications and worked through simulated challenges, using COOP’s 3-D scenario visualization tool (3DSVT), that challenged COOP’s PMEF, essential support activities (ESAs), and impacted mission critical personnel’s human capital. The PMEF and ESAs were successfully maintained as the CERG developed incident action plans through several phases of the scenario, addressing appropriate needs, concerns and strategic planning.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2018

Intel x86 Assembly

These slides will be used to teach malware analysis at various training events.

97 MATHEMATICS AND COMPUTING↗