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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 OSTI2016

In-situ trainable intrusion detection system

A computer implemented method detects intrusions using a computer by analyzing network traffic. The method includes a semi-supervised learning module connected to a network node. The learning module uses labeled and unlabeled data to train a semi-supervised machine learning sensor. The method records events that include a feature set made up of unauthorized intrusions and benign computer requests. The method identifies at least some of the benign computer requests that occur during the recording of the events while treating the remainder of the data as unlabeled. The method trains the semi-supervised learning module at the network node in-situ, such that the semi-supervised learning modules may identify malicious traffic without relying on specific rules, signatures, or anomaly detection.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Time Sequence Machine Learning-Based Data Intrusion Detection for Smart Voltage Source Converter-Enabled Power Grid

Smart inverters of distributed energy resources can enable cloud computing, condition monitoring, result visualization, remote control, and peer-to-peer energy trading in advanced power systems. However, the advent of data injection attacks in the communication architecture can alter measurement characteristics of power grids and have devastating consequences. In this article, we propose a time sequence machine learning-based anomaly detection methodology for detecting cyber intrusion into control signal setpoints and dc voltage signal measurement bias of the voltage source converter (VSC) in wind generators. We first investigated the effects of four types of denial of service, tampering signal, and stealthy-type data intrusion attacks on smart VSCs and overall wind farms. We then proposed a novel time sequence machine learning-based intrusion detection framework that can be implemented to detect different cyberattacks in the VSCs. The performance of the proposed framework has been compared with that of autoencoder and clustering-based intrusion detection framework. The proposed framework was validated by using the IEEE 39 bus power system in the presence of four wind farms in different locations. Using several metrics for intrusion detection performance, we validated the effectiveness of the proposed framework.

42 ENGINEERING↗
NASA NTRS1980

Passive intrusion detection system

An intrusion detection system is described in which crystal oscillators are used to provide a frequency which varies as a function of fluctuations of a particular environmental property of the atmosphere, e.g., humidity, in the protected volume. The system is based on the discovery that the frequency of an oscillator whose crystal is humidity sensitive, varies at a frequency or rate which is within a known frequency band, due to the entry of an intruder into the protected volume. The variable frequency is converted into a voltage which is then filtered by a filtering arrangement which permits only voltage variations at frequencies within the known frequency band to activate an alarm, while inhibiting the alarm activation when the voltage frequency is below or above the known frequency band.

Laue, E. G.↗
DOE OSTI2020

Distributed Intrusion Detection System using Semantic-based Rules for SCADA in Smart Grid

Cyber-physical system (CPS) security for the smart grid enables secure communication for the SCADA and wide-area measurement system data. Power utilities world-wide use various SCADA protocols, namely DNP3, Modbus, and IEC 61850, for the data exchanges across substation field devices, remote terminal units (RTUs), and control center applications. Adversaries may exploit compromised SCADA protocols for the reconnaissance, data exfiltration, vulnerability assessment, and injection of stealthy cyberattacks to affect power system operation. In this paper, we propose an efficient algorithm to generate robust rule sets. We integrate the rule sets into an intrusion detection system (IDS), which continuously monitors the DNP3 data traffic at a substation network and detects intrusions and anomalies in real-time. To enable CPS-aware wide-area situational awareness, we integrated the methodology into an open-source distributed-IDS (D-IDS) framework. The D-IDS facilitates central monitoring of the detected anomalies from the geographically distributed substations and to the control center. The proposed algorithm provides an optimal solution to detect network intrusions and abnormal behavior. Different types of IDS rules based on packet payload, packet flow, and time threshold are generated. Further, IDS testing and evaluation is performed with a set of rules in different sequences. The detection time is measured for different IDS rules, and the results are plotted. All the experiments are conducted at Power Cyber Lab, Iowa State University, for multiple power grid models. After successful testing and evaluation, knowledge and implementation are transferred to field deployment.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Proactive Intrusion Detection and Mitigation System

SAND2023-05661O The proactive intrusion detection and mitigation system (PIDMS) provides grid-edge situational awareness for cybersecurity defense by capturing real-time distributed energy resource (DER) network traffic and performance data with a novel approach that improves the detection and prevention of cyber-physical attacks. The PIDMS addresses the grid-edge security gap with real-time analysis of both network traffic and photovoltaic performance data to deliver a novel, cyber-physical intrusion detection system (IDS) approach that increases the accuracy and effectiveness of detection and mitigation. This hybrid IDS analysis enables dual monitoring that increases the workload of the adversary; both cyber and physical data would have to be simultaneously spoofed to evade detection. Furthermore, monitoring and analyzing cyber data are insufficient in some cases. For example, in an insider threat aimed at disrupting inverter grid-support functions where proper credentials and authentication are achieved, only the altered PV performance would indicate abnormal behavior. All in all, the PIDMS provides novel capabilities for: • Distributed, real-time cyber-physical detection and mitigation analysis • Cybersecurity defense for grid-edge systems • Analysis framework that can provide situational awareness across the transmission, distribution, and DER systems The PIDMS sensor is designed to collect cyber-physical data, process the data using machine-learning algorithms, detect abnormal events, and deploy mitigations. With these goals, the main functional PIDMS objectives are: • Capability to collect cyber-physical data • Onboard storage of cyber-physical data • Peer-to-peer communication • Computationally efficient machine-learning algorithms • Online cyber-physical data analysis • Alerting/visualization capabilities • Mitigation deployment capability with bump-in-the-wire (BITW) implementation Each of these functional objectives enable PIDMS to perform effective cyber-physical intrusion detection and mitigation. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Jones, Christian↗
DOE OSTI2022

HIDES (Hybrid Intrusion Detection for Energy Systems) [SWR-19-65]

Hybrid Intrusion Detection for Energy Systems (HIDES) is a conglomerate detector that incorporates three major components: (1) Signature-based detection that involves information technology (IT) related anomaly detection rules, (2) Behavior-based detection that utilizes specially crafted rulesets for detecting abnormal behavior regarding the power system's supervisory control and data acquisition (SCADA) communication streams, and (3) Learning-based detection that combines SCADA communication with phasor measurements to identify otherwise non-detectible attack vectors. The software itself interconnects intrusion detection engine (IDE) enabling communications between the generated OT and IT alerts with phasor measurements for the learning-based detection system. The resulting detection system can detect a wide array of IT, OT, and physical attacks in a real-time setting.

Rivera, Joshua↗
DOE OSTI2019

Intrusion detection apparatus, system and methods

Described herein are various technologies for detection and mitigation of rogue terminal attacks on multiplex data buses. An intrusion detection device is incorporated between a bus controller and a bus of a multiplex data bus. The intrusion detection device receives message that are communicated among the bus controller and a plurality of remote terminals (by way of the bus). The intrusion detection device determines whether messages are unauthorized based upon origins of the messages and predefined rules. When a message is determined to be unauthorized, the intrusion detection device outputs a notification that the unauthorized message has been detected and can block the unauthorized message.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Hardware intrusion detection system

An apparatus for intrusion detection includes processing circuitry, a switch, signal detection circuitry, and an analog-to-digital converter (“ADC”). The processing circuitry is coupled to send a challenge signal to a device when the device is coupled to the processing circuitry. The switch is coupled to be enabled and disabled by the processing circuitry. The switch is for coupling to the device to receive a response signal in response to the challenge signal sent by the processing circuitry. The signal detection circuitry is coupled to receive the response signal in via the switch, when the processing circuitry enables the switch. The ADC is coupled to take measurements of the signal detection circuitry at a first output. The processing circuitry is coupled to the ADC and configured to analyze whether an intruder is present in the device based on the measurements of the signal detection circuitry.

42 ENGINEERING↗
DOE OSTI2021

Hardware intrusion detection system

An apparatus for intrusion detection includes processing circuitry, a switch, signal detection circuitry, and an analog-to-digital converter (“ADC”). The processing circuitry is coupled to send a challenge signal to a device when the device is coupled to the processing circuitry. The switch is coupled to be enabled and disabled by the processing circuitry. The switch is for coupling to the device to receive a response signal in response to the challenge signal sent by the processing circuitry. The signal detection circuitry is coupled to receive the response signal in via the switch, when the processing circuitry enables the switch. The ADC is coupled to take measurements of the signal detection circuitry at a first output. The processing circuitry is coupled to the ADC and configured to analyze whether an intruder is present in the device based on the measurements of the signal detection circuitry.

Edwards, Nathan J.↗
DOE OSTI2021

Review of Intrusion Detection Methods and Tools for Distributed Energy Resources

Recent trends in the growth of distributed energy resources (DER) in the electric grid and newfound malware frameworks that target internet of things (IoT) devices is driving an urgent need for more reliable and effective methods for intrusion detection and prevention. Cybersecurity intrusion detection systems (IDSs) are responsible for detecting threats by monitoring and analyzing network data, which can originate either from networking equipment or end-devices. Creating intrusion detection systems for PV/DER networks is a challenging undertaking because of the diversity of the attack types and intermittency and variability in the data. Distinguishing malicious events from other sources of anomalies or system faults is particularly difficult. New approaches are needed that not only sense anomalies in the power system but also determine causational factors for the detected events. In this report, a range of IDS approaches were summarized along with their pros and cons. Using the review of IDS approaches and subsequent gap analysis for application to DER systems, a preliminary hybrid IDS approach to protect PV/DER communications is formed in the conclusion of this report to inform ongoing and future research regarding the cybersecurity and resilience enhancement of DER systems.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2025

Edge ML for CAN bus intrusion detection in AVs

Autonomous Vehicles (AVs) are revolutionizing transportation, but their reliance on interconnected cyber-physical systems exposes them to unprecedented cybersecurity risks. This study addresses the critical challenge of detecting real-time cyber intrusions in self-driving vehicles by leveraging a dataset from the Udacity self-driving car project. We simulate four high-impact attack vectors, Denial of Service (DoS), spoofing, replay, and fuzzy attacks, by injecting noise into spatial features (e.g., bounding box coordinates) to replicate adversarial scenarios. We develop and evaluate two lightweight neural network architectures (NN-1 and NN-2) alongside a logistic regression baseline (LG-1) for intrusion detection. The models achieve exceptional performance, with NN-2 attaining an AUC score of 93.15% and 93.15% accuracy, demonstrating their suitability for edge deployment in AV environments. Through explainable AI techniques, we uncover unique forensic fingerprints of each attack type, such as spatial corruption in fuzzy attacks and temporal anomalies in replay attacks, offering actionable insights for feature engineering and proactive defense. Visual analytics, including confusion matrices, ROC curves, and feature importance plots, validate the models' robustness and interpretability. This research sets a new benchmark for AV cybersecurity, delivering a scalable, field-ready toolkit for Original Equipment Manufacturers (OEMs) and policymakers. By aligning intrusion fingerprints with SAE J3061 automotive security standards, we provide a pathway for integrating machine learning into safety-critical AV systems. Our findings underscore the urgent need for security-by-design AI, ensuring that AVs not only drive autonomously but also defend autonomously. This work bridges the gap between theoretical cybersecurity and life-preserving engineering, offering a leap toward safer, more secure autonomous transportation.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2002

Intrusion detection: systems and models

This paper puts forward a review of state of the art and state of the applicability of intrusion detection systems, and models. The paper also presents a classfication of literature pertaining to intrusion detection.

intrusion detection risk security↗
DOE OSTI2016

Cybersecurity Intrusion Detection and Monitoring for Field Area Network: Final Report

This report summarizes the key technical accomplishments, industry impact and performance of the I2-CEDS grant entitled “Cybersecurity Intrusion Detection and Monitoring for Field Area Network”. Led by Applied Communication Sciences (ACS/Vencore Labs) in conjunction with its utility partner Sacramento Municipal Utility District (SMUD), the project accelerated research on a first-of-its-kind cybersecurity monitoring solution for Advanced Meter Infrastructure and Distribution Automation field networks. It advanced the technology to a validated, full-scale solution that detects anomalies, intrusion events and improves utility situational awareness and visibility. The solution was successfully transitioned and commercialized for production use as SecureSmart™ Continuous Monitoring. Discoveries made with SecureSmart™ Continuous Monitoring led to tangible and demonstrable improvements in the security posture of the US national electric infrastructure.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Intrusion Detection System Alarm Station Operator Interface Improvements

To address Alarm Station operator performance, Portable Intrusion Detection System team gathered information concerning AS operator data needs when assessing alarms. The purpose was to improve the Portable Intrusion Detection System operator interface to ensure that critical information was quickly presented and easily accessible. To gather the data, the team used a Goal Directed Task Analysis approach. The method of analysis was to prepare a set of interview questions, interview selected AS operator experts, conduct the interviews, create a goal/decision/information hierarchy based on information gathered, and then apply the results to the operator interface. In applying the results, the team had to consider not only the Goal Directed Task Analysis -determined information needs of the Alarm Station operator end-user population, but also account for customer requirements and differences in domain. The constraints in implementing all situation awareness recommendations are summarized and initial potential solutions presented.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2018

Security Evaluation of Two Intrusion Detection Systems in Smart Grid SCADA Environment

The increased complexity and interconnectivity of SCADA infrastructure in the power system have exposed it to the multitude of vulnerabilities. There is a growing emphasis towards developing an efficient intrusion detection system (IDS) to strengthen the security of the SCADA control system. This is a research-in-progress paper which presents the application of two anomaly-based intrusion detection systems (AIDS) in detecting the stealthy cyber-attack on the SCADA control system. We have applied the IDS tools Snort and Bro, in designing the IDS and later, compared their performances in terms of detection rate and latency in the alert packets with a motive of selecting better IDS for the SCADA security. Specifically, the timing-based rule is applied to identify the malicious packets based on the high temporal frequency in the network traffic. For the case study, we have implemented the SCADA based protection scheme which performs an autonomous protection to mitigate the system disturbances. We first implemented the stealthy cyber-attack which compromised the SCADA controller followed by data integrity attack on the system generator. Next, we perform the impact analysis during the attack followed by performance evaluation of IDS tools. Our experimental results show that the IDS tools are efficient in detecting cyber-attacks within an acceptable time frame for different sizes of network packets.

24 POWER TRANSMISSION AND DISTRIBUTION↗