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Cyber Threat Landscape for Distribution Systems
INL cyber analysts will present an overview of the current threat landscape for distribution systems. We will begin with examples of known attacks that have occurred recently to motivate the threat analysis and mitigation discussed in the remainder of the presentation. Examples may include the attacks affecting wind plants in Europe in Spring 2022, ransomware attacks on city utilities and commercial distribution systems, and advanced persistent threats (APTs) including the attacks on Ukrainian electric system in 2015 and 2016, as well as more recent evidence of other APT activity. We will present the end-to-end attack paths and discuss the various attacker skills, financing, motivations, and access that contributed to these attacks. In the second part of this presentation, we will discuss mitigating the cyber threats for distribution systems. We will discuss recent publicly disclosed vulnerabilities and explain their relevant context for distribution system security. Likely attacks to affect distribution systems, such as denial-of-service (DoS), ransomware, edge-device compromise, and advanced persistent threats (APTs) will be described. In addition to an overview of these kinds of attacks, we will provide examples of the various ways in which these attacks can start and what systems they can affect. Simple, cost-effective counter-measures to these attacks will be discussed and we will emphasize how these mitigations can reduce threats when properly applied and maintained.
Evidence-based Graph Adversary Mapping (EGRAM) [Poster]
Cybersecurity companies such as CrowdStrike, Dragos, Microsoft and Unit 42 categorize Advanced Persistent Threats (APTs) using their own naming schemes. As a result, these APTs are mapped to different malware sources and campaigns, all from differing sources, leading to inconsistent mapping. Inconsistent mapping causes confusion and adds further obscurity around these groups, making it difficult to track and mitigate APT cyberattacks. The Evidence-based Graph Adversary Mapping (EGRAM) tool remediates the mapping challenge by collecting, updating and converting adversary data and their sources into a valid, codified STIX v2.1 bundle which is then stored in a Neo4j graph database. It utilizes graph traversal methods and centrality analysis to generate actionable information as a Structured Threat Intelligence Graph (STIG), based on user queries. EGRAM exists as Python code and a Jupyter Notebook that acts as a searchable, evidence-based, source of intelligence for APT groups’ artifacts and cyber campaigns.
A Context-Centered Research Approach to Phishing and Operational Technology in Industrial Control Systems
Advanced persistent threats (APT’s) that leverage phishing against operational technology (OT) are cyber-attacks that endanger critical infrastructure assets nationwide. Today phishing, a human focused exploit, constitutes 91% of successful attack vectors against Federal assets. This means Human Introduced Vulnerabilities (HICV’s) are the weakest cyber defense link. The success of these attacks also suggests HICV’s are not well understood nor mitigated. To characterize HICV’s and provide the necessary context in which they exist, this paper introduces a research approach derived from the mature science of Social Ecology. The desired end result of this research is an HICV focused risk assessment framework.
A data analytical approach for assessing the efficacy of Operational Technology active defenses against insider threats
In recent years, the need for Operational Technology (OT) defenses has been recognized, serving as an additional line of defense when Information Technology (IT) defenses are bypassed. This is no longer considered an uncommon possibility when dealing with advanced persistent threat (APT) actors expected to be state-sponsored and receiving insider assistance. In these extreme adversarial situations, OT defenses aim to provide another layer of defense for the system, introduced directly at the physical process level, as described by the sensors data, the system model, and control actions. Just like IT defenses, two schools of thought, i.e., passive and active defenses, have emerged to address this challenge. In active defenses, representing the focus of this paper, known signatures, synthesized based on the system's unique characteristics, are inserted into the system. In contradistinction, passive methods rely solely on observing system behavior in search of patterns of normal behavior with deviations thereof representing abnormal behavior. In their most sophisticated implementations, both passive and active defenses rely on the use of data analytics to identify the patterns and synthesize the observed and/or inserted signatures. Past research has shown that passive defenses may be bypassed by APT actors relying on data analytics and their intimate knowledge of the system to evade detection by respecting the patterns identified by the defenders. Here this manuscript explores the use of active defenses under the assumption that the attacker has privileged access to the system, including access to the system's model and sensors data. Specifically, this manuscript assesses the ability of active defenses to remain invisible to the attackers, and discusses the associated challenges that must be addressed to ensure their resiliency against APT actors.
APT malware static trace analysis through bigrams and graph edit distance
Research and business organizations are vulnerable to attack by malware, particularly advanced persistent threat malware tailored for a specific target. Malware identification is made more difficult because samples can be subtly altered to avoid detection by methods that check for an identical match to known code. Different versions of an original piece of malware form a malware family. And when new malicious software is identified, reverse engineers seek to identify its origin and purpose. Knowing whether new malware is from a known family or a previously unobserved family aids the efficiency of reverse engineers. Furthermore, this article presents a three-stage method to classify new malware into a family by comparing its similarity to existing static traces, and assigning it to the most similar family. First, a fast filtering method creates a shortlist of samples with some similarity to the new malware, using a simple bigram comparison of the instructions. The second stage takes the call graph view of the shortlisted static traces and uses simulated annealing to estimate the graph edit distance, a measure of dissimilarity between graphs. Finally, a random forest classifier combines the previous two results to predict the family to which a new sample belongs. Our paper also considers how to detect when malware is from a new family.
SHARP-Net: Platform for Self-Healing and Attack Resilient PMU Networks
Synchrophasor technology plays a pivotal role in developing the next generation of wide-area monitoring, protection, and control in the smart grid environment. As technology and communications infrastructures evolve, however, so do the attack surfaces in the synchrophasor network that can be exploited by advanced persistent threat (APT) actors to affect power system stability and reliability. In this paper, we propose a novel platform for developing a self-healing and attack-resilient PMU network (SHARP-Net) by instituting a state-of-the-art intrusion detection system (IDS) with an intrusion mitigation system (IMS) and an alert management system (AMS). In particular, the proposed platform detects anomalies during cyberattacks on phasor data concentrators (PDCs) based on the rules defined in the IDS, then the generated alerts are published to the IMS through the AMS. The proposed IMS proceeds to take automated corrective responses to mitigate cyberattacks by reconfiguring the synchrophasor network to isolate the compromised PDCs, and it orchestrates new PDCs to prevent the future propagation of attacks. Further, the IMS restores the system's observability by reconnecting the new PDCs to make the grid attack-resilient. In this work, the SHARP-Net platform is developed by using Python-based libraries, minimega's software-defined network, and virtual machine orchestration. We implement and validate the proposed SHARP-Net architecture by testing a PMU network in the smart grid environment. SHARP-Net showed promising performance in detecting cyberattacks and mitigating them through the network reconfiguration.
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.
Cybersecurity Challenges in Low-Inertia Power-Electronics-Dominated Grids
Here, the low inertia characteristics of the power electronics dominated grid (PEDG) introduces challenges while restoring voltage and frequency to their nominal values. These stability challenges create new cybersecurity vulnerabilities that are not thoroughly discussed in the literature. Cyber events such as false data injection (FDI), denial of service (DoS), man-in-the-middle attacks, stealthy attacks, and advanced persistent threats target PEDG to disrupt grid stability or gain financial benefits. The low inertia of PEDG (< 2s) compared to traditional grids (~10s) exacerbates these vulnerabilities. In response to stealthy attacks on state variables that supervisory layers cannot detect until significant harm occurs, the low inertia characteristics of PEDG offer substantial stealthy attack surfaces. To counteract such threats, PEDG must be equipped with ultra-fast real-time anomaly detection system and trajectory prediction mechanism to achieve effective cyberattack resiliency.
CPS Testbed Architectures for WAMPAC using Industrial Substation and Control Center Platforms and Attack-Defense Evaluation
Advanced persistent threats and cyberattacks can impact wide-area monitoring, protection, and control (WAMPAC) system operation. Many cyber-physical system (CPS) testbeds have been developed for attack-defense experimentation and attack-resiliency tools evaluation for WAMPAC, but they are limited to a simulation-and-emulation based environment. This paper presents a quasi-realistic CPS attack-defense testbed-based framework for WAMPAC applications using the industrial substation and control center platforms such as eTerra integrated with the hardware-in-the-loop CPS smart grid testbed available at Iowa State University. The proposed framework includes various combinations of industry-grade substation and control center platforms, communication topologies, real-time digital simulators, and a novel cyber-physical distributed intrusion-and-anomaly detection system (D-IADS) for WAMPAC applications. The D-IADS includes a master at the control center and geographically distributed sensor devices at each substation. Each D-IADS sensor deployed at a substation or control center network monitors ingress and egress traffic, detect intrusions, and dispatch alerts to the D-IADS master. The D-IADS master centrally monitors and analyze the alerts and controls D-IADS sensors. We considered an EMP60 synthetic CPS grid as a case study to demonstrate the framework and proposed D-IADS for WAMPAC applications against cyberattack vectors such as Man-in-the-Middle DNP3 attack, denial-of-service, and data-integrity attacks.
Sensor and Actuator Attacks on Hierarchical Control Systems with Domain-Aware Operator Theory
Cyber-Physical Systems (CPSs) provide opportunities for cyber attacks to have physical impacts. Advanced Persistent Threats (APTs) are a subclass of cyber threats that act stealthily to avoid detection and enable long-term attacks. Here, we build on our past work in APT modelling to combine deception-based sensor bias attacks and direct actuator manipulations in attacks against a hierarchical control system. That past work used the Koopman operator to develop a data-driven, domain-aware, optimization-based attacker model. Using an expansion of this model, we compute several different attacks, including multiple simultaneous attacks, against a high-fidelity commercial building emulator and compare the impacts of those attacks to each other. One next step of interest is to construct a defender system, built on the same modelling approach, designed to detect and mitigate such attacks.
HIDES: Hybrid Intrusion Detector for Energy Systems
The establishment of a resilient electric grid accompanied by a secure communications network is an ongoing battle as advanced persistent threats continue to exploit existing vulnerabilities in legacy supervisory control and data acquisition system (SCADA) infrastructure. Traditional intrusion detection systems (IDSs) lack consistent performance because of the continuously evolving attack surface of SCADA systems. These shortcomings can be overcome by integrating logical system behavior, protocol-specific knowledge, and data-based learning to develop a comprehensive IDS solution. In this paper, we present a Hybrid Intrusion Detector for Energy Systems by integrating a network-based IDS, state-of-the-art machine learning-based IDS, and model-based IDS to detect unknown and stealthy cyberattacks targeting the SCADA networks. The proposed IDS uses synchrophasor measurements and cyber logs to learn patterns of different scenarios based on spatiotemporal behaviors of power systems. As a proof of concept, we implement and validate the proposed IDS by leveraging resources available at the National Renewable Energy Laboratory's Energy Systems Integration Facility test bed. Experimental results show promising performance in detecting cyberattacks while providing realtime visualization of power system measurements and cyber logs.
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.
Multiple social platforms reveal actionable signals for software vulnerability awareness: A study of GitHub, Twitter and Reddit
Software vulnerabilities are flaws in computer systems that leave users open to attack. In many cases, these vulnerabilities go unnoticed and remain unresolved in codebases. Thus, awareness of software vulnerabilities among the public is crucial to ensure effective cybersecurity practices, the development of high quality software, and ultimately national security. This awareness can be better understood by studying the spread and evolution of software vulnerability discussions in online communities. This work is the first to evaluate and contrast how discussions about software vulnerabilities spread on three social platforms -- Twitter, GitHub, and Reddit. To lay the groundwork, we showcase a novel fundamental framework for measuring information spread that identifies the spread mechanisms and observables across platforms, the units of information, and the groups of measurements that can be applied to focus on a specific phenomena e.g., information cascades. We then analyze and contrast social network topologies for three example social networks and measure the scale and speed of the spread of discussion of specific vulnerabilities to understand how far and how widely they spread, how many users participate in discussions, and the duration of their spread. To demonstrate the awareness of more impactful software vulnerabilities, a subset of our analysis focuses on vulnerabilities targeted during recent major cyber attacks as well as vulnerabilities exploited by advanced persistent threat groups. We discover that usually, vulnerability discussions start on GitHub, before occurring on Twitter and Reddit. While studying how some user-level and content-level characteristics influence vulnerability spread, we observe that Twitter discussions started by users predicted to be humans have larger size, breadth, depth, adoption rate, lifetime, and structural virality compared to those started by users predicted to be bots. On Reddit, we contrast the differences in thread structure that originate from posts with positive, negative and neutral polarity. We find that posts that are positive have larger, deeper and wider discussions compared to negative and neutral posts. We anticipate the results of our analysis to not only increase the understanding of software vulnerability awareness but also inform models for simulating information spread across multiple social environments online.
Center of Excellence for Operational Technology
The Center of Excellence for Operational Technology Traditional Presentation Abstract 2025 National Laboratories Information Technology Summit | Denver, CO Traditional Presentation Session Managing cybersecurity risk in Operational Technology (OT) presents a significant challenge across the Department, and critically, at many of the national laboratories. This includes IT-OT convergence, aging OT systems, cost of updating OT systems, and increased Advanced Persistent Threat efforts against OT including the 16 critical infrastructure sectors as listed in Presidential Policy Directive 21. DoE’s Office of Science and NNSA’s Office of the Chief Information Officer are taking the lead in addressing this challenge to include critical systems, by establishing the Center of Excellence (CoE) for Operational Technology. Championed by NNSA Deputy Chief Information Officer Steven McAndrews and the Office of Science Chief Information Officer Shila Cooch, the CoE for OT was chartered in February 2025 to address the challenges of OT cybersecurity and compliance. The CoE for OT will create partnerships and leverage expertise from across the NNSA National Security Enterprise and DOE Labs, Plants and Sites. The CoE will also collaborate with colleagues in other government agencies, industry partners and academia. The CoE for OT discussion at the National Laboratories Information Technology Summit ’25 will include the genesis of the CoE, stated goals, organizational structure, and the effort to attract OT subject matter experts to join the CoE effort to share knowledge and expertise. The discussion will include opportunities to get involved and contribute to this important effort. This session will be led by CoE for OT Co-Chairs Matt Kwiatkowski, Fermi National Laboratory Chief Information Security Officer, and Steven Weldon, Savannah River National Laboratory Cyber Program Director at the Georgia Cyber Center. The session will be of particular interest to CIOs, CTOs, CISOs, as well as IT and OT practitioners.
Dominion. A game exploring information exploitation
FlipIt is a game theoretic framework published in 2012[1] to investigate optimal strategies for managing security resources in response to Advanced Persistent Threats. It is a two-player game wherein a resource is controlled by exactly one player at any time. A player may move at any time to capture the resource, incurring a move cost, and is informed of the last time their opponent has moved only upon completing their move. Thus, moves may be wasted and takeover is considered \stealthy", with regard to the other player. The game is played for an unlimited period of time, and the goal of each player is to maximize the amount of time they are in control of the resource minus their total move cost, normalized by the current length of play. Marten Van Dijk and others[1] provided an analysis of various player strategies and proved optimal results for certain subclasses of players. We extend their work by providing a reformulation of the original game, wherein the optimal player strategies can be solved exactly, rather than only for certain subclasses. We call this reformulation Dominion, and place it within a broader framework of stealthy move games. We de ne Dominion to occur over a nite time scale (from 0 to 1), and give each player a certain number of moves to make within the time frame. Their expected score in this new scenario is the expected amount of time they have control, and the point of the game is to dominate as much of the unit interval as possible. We show how Dominion can be treated as a two player, simultaneous, constant sum, unit square game, where the gradient of the bene t curves for the players are linear and possibly discontinuous. We derive Nash equilibria for a basic version of Dominion, and then further explore the roles of information asymmetry in its variants. We extend these results to FlipIt and other cyber security applications.
Clean Energy Cybersecurity Accelerator Cohort 1: Authentication and Authorization
In the 2023 National Cybersecurity Strategy, the Biden-Harris Administration defines the need for a "defensible, resilient digital ecosystem where it is costlier to attack systems than defend them." The strategy cites the Clean Energy Cybersecurity Accelerator (CECA) as an exemplary effort to bolster the security and resilience of clean energy generation. These efforts help "secure the clean energy grid of the future and [generate] security best practices that extend to other critical infrastructure sectors" and promise broad and far-reaching impacts to bridge the capabilities of private industry and the needs of energy production. Cohort 1 of CECA launched in the fall of 2022 with a focus on solutions that provide strong authentication and authorization for industrial control systems to mitigate attacks on the energy grid. Authentication and authorization verify that the identity (authentication) and permissions (authorization) of a user or device are aligned with their assigned roles. Weaknesses in either can have serious repercussions. To assess the strength of Cohort 1's solutions, CECA devised threat scenarios grounded in historical precedents: the CECA team reviewed exploits from real-world case studies of state-sponsored actors to match the assessment's attack paths and targets. Cohort 1 results provided the energy industry, product vendors, and related agencies valuable insights into the efficacy and applicability of solutions in common system configurations under realistic threat scenarios. The results of the assessment highlight points for interrogation and improvement in subsequent technology iterations. CECA's evaluations are part of an ongoing conversation and collaboration to bolster U.S. cyber resilience against adversaries today and in the future.
Reinforcement Learning for feedback-enabled cyber resilience
The rapid growth in the number of devices and their connectivity has enlarged the attack surface and made cyber systems more vulnerable. As attackers become increasingly sophisticated and resourceful, mere reliance on traditional cyber protection, such as intrusion detection, firewalls, and encryption, is insufficient to secure the cyber systems. Cyber resilience provides a new security paradigm that complements inadequate protection with resilience mechanisms. A Cyber-Resilient Mechanism (CRM) adapts to the known or zero-day threats and uncertainties in real-time and strategically responds to them to maintain the critical functions of the cyber systems in the event of successful attacks. Feedback architectures play a pivotal role in enabling the online sensing, reasoning, and actuation process of the CRM. Reinforcement Learning (RL) is an important gathering of algorithms that epitomize the feedback architectures for cyber resilience. It allows the CRM to provide dynamic and sequential responses to attacks with limited or without prior knowledge of the environment and the attacker. In this work, we review the literature on RL for cyber resilience and discuss the cyber-resilient defenses against three major types of vulnerabilities, i.e., posture-related, information-related, and human-related vulnerabilities. Here we introduce moving target defense, defensive cyber deception, and assistive human security technologies as three application domains of CRMs to elaborate on their designs. The RL algorithms also have vulnerabilities themselves. We explain the major vulnerabilities of RL and present develop several attack models where the attacker target the information exchanged between the environment and the agent: the rewards, the state observations, and the action commands. We show that the attacker can trick the RL agent into learning a nefarious policy with minimum attacking effort. The paper introduces several defense methods to secure the RL-enabled systems from these attacks. However, there is still a lack of works that focuses on the defensive mechanisms for RL-enabled systems. Last but not least, we discuss the future challenges of RL for cyber security and resilience and emerging applications of RL-based CRMs.