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All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Identification of the accident-related critical digital assets based on probabilistic safety analysis results - 225

Digital I and C systems have been developed and installed in nuclear power plants, and due to installation of the digital I and C systems, cyber security concerns are increasing in nuclear industry. However, there are too many critical digital assets (CDAs) to be inspected in digitalized NPPs. In order to reduce the inefficiency of regulation in nuclear facilities, the critical digital assets that are directly related to an accident are elicited by using the probabilistic safety assessment results. Target initial events are selected, and their heading are analyzed through event tree analysis. Among the headings, the headings that can be proceeded directly to the core damage by the cyber-attack are finally selected as the target of deriving the minimum cut-sets. Based on success criteria of each heading, we analyze the fault trees and derive the minimum set-cuts. In terms of original PSA, the value of probability for the cut-sets is important but the probability is not important in terms of cyber security of NPPs. The important factors is the number of basic events consisting of the minimal cut-sets that is proportional to vulnerability. Finally, the process for identifying accident-related CDAs is suggested based on PSA results. The results of this study are expected to be used to derive the linkage between cyber-attack and accident, and to develop the final core digital asset identification methodology and effective regulatory method. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2019

Mechanisms of a coniferous woodland persistence under drought and heat

Predictions of warmer droughts causing increasing forest mortality are becoming abundant, yet fewer studies have investigated the mechanisms of forest persistence. To examine the resistance of forests to warmer droughts, we used a five-year precipitation reduction (~45% removal), heat (+4°C above ambient) and combined drought and heat experiment in an isolated stand of mature Pinus edulis-Juniperus monosperma . Despite severe experimental drought and heating, no trees died, and we observed only minor evidence of hydraulic failure or carbon starvation. Two mechanisms promoting survival were supported. First, access to bedrock water, or ‘hydraulic refugia’ aided trees in their resistance to the experimental conditions. Second, the isolation of this stand amongst a landscape of dead trees precluded ingress by Ips confusus , frequently the ultimate biotic mortality agent of piñon. These combined abiotic and biotic landscape-scale processes can moderate the impacts of future droughts on tree mortality by enabling tree avoidance of hydraulic failure, carbon starvation, and exposure to attacking abiotic agents.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Data-driven Cyberattack Detection for Photovoltaic (PV) Systems through Analyzing Micro-PMU Data

With increasing exposure to software-based sensing and control, Photovoltaic (PV) systems are facing higher risks of cyber attacks. Here, to ensure the system stability and minimize potential economic losses, it is imperative to monitor operating states and detect attacks at the early stage. To meet this demand, Micro-Phasor Measurement Units (μPMU) are increasingly popular in monitoring distribution networks. However, due to the relatively low sampling rate, μPMU has not yet been used to detect and classify cyber-attacks in power electronics enabled smart grid. To our knowledge, this is one of the first attempts to use μPMU to detect cyber attacks that degrade the performance of power electronics systems. We propose to apply data-driven methods on micro-PMU data to implement attack detection. We have evaluated data-driven methods, including decision tree (DT), K-nearest neighbor (KNN), support vector machine (SVM), artificial neural network (ANN), long short-term memory (LSTM) and convolutional neural network (CNN). The proposed CNN model achieves the required performances with the highest 99.23% accuracy and 0.9963 F 1 score.

14 SOLAR ENERGY↗
DOE OSTI2018

Decision Tree Based Anomaly Detection for Remedial Action Scheme in Smart Grid using PMU Data

The advanced and persistent cyber threats facing the critical infrastructure such as the smart grid are exponentially rising which require sophisticated defense strategy. Remedial Action Scheme (RAS), also known as Special Protection Scheme (SPS), relies on the interconnected cyber physical system for automated protection which is exposed to the multitude of vulnerabilities. In this paper, we propose an innovative approach to develop an Intelligent Remedial Action Scheme (IRAS) which can detect and distinguish cyber attacks from the physical disturbances in smart grid and later take smart corrective actions as required to minimize the impact on system reliability and economy. Specifically, we have proposed the decision tree based anomaly detection methodology which can distinguish between the normal tripping during power line faults and malicious tripping attack on the physical relays in the context of RAS. The classification model is developed using differential features of voltage and current phasors. Next, as a proof of concept, we have implemented and validated the proposed methodology in cyber physical environment at Iowa State's PowerCyber testbed. Finally, the proposed methodology is tested on modified IEEE 39 bus system in offline and real-time mode. Our experimental results show that the proposed method is efficient in detecting attacks and performing corrective actions within an acceptable time frame.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

An Intelligent Distributed Ledger Construction Algorithm for IoT

Blockchain is the next generation of secure data management that creates near-immutable decentralized storage. Secure cryptography created a niche for blockchain to provide alternatives to well-known security compromises. However, design bottlenecks with traditional blockchain data structures scale poorly with increased network usage and are extremely computation-intensive. This made the technology difficult to combine with limited devices, like those in Internet of Things networks. In protocols like IOTA, replacement of blockchain's linked-list queue processing with a lightweight dynamic ledger showed remarkable throughput performance increase. However, current stochastic algorithms for ledger construction suffer distinct trade-offs between efficiency and security. This work proposed a machine-learning approach with a multi-arm bandit that resolved these issues and was designed for auditing on limited devices. This algorithm was tested in a reinforcement-learning environment simulating the IOTA ledger's construction with a decision tree. This study showed through regret analysis and experimentation that this approach was secure against impulse manipulation attacks while remaining energy-efficient. Although the IOTA protocol was a pioneer for lightweight distributed ledgers, it is expected that future blockchain protocols will adopt techniques similar to those presented in this work.

multi-arm bandit↗
DOE OSTI2013

Empirical and process-based approaches to climate-induced forest mortality models

Globally, forests store ~45% of carbon sequestered terrestrially, contribute more to the terrestrial sink per area than any other land cover type, and assimilate an important portion of anthropogenic emissions. Forests exert strong biophysical control on climate via surface energy balance, and the hydrological cycle. Widespread forest mortality in response to drought, increased temperatures, and infestation of tree pests has been observed globally, potentially threatening forests' regulation of climate. This threat has prompted great interest in understanding and predicting tree mortality due to climate variability and change, especially drought. Initial tests of hydraulic failure (mortality caused by irreversible loss of xylem conductivity from air embolism), carbon starvation (mortality due to carbohydrate limitation), insect attacks, wildfire, and their interdependence, suggest proximal causes of mortality are likely complex, co-occurring, interrelated, and variable with tree species. While the interdependent roles of carbon and water in plant mortality are consistently observed, this work is continuously prompting new questions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Large-Scale Hydrogen Storage Cyber Risk Assessment

Hydrogen storage systems may become more widely deployed throughout the country, and so it is possible that individual and interconnected systems will be exposed to cyber-attacks. These events can cause physical and financial harm to employees, people in the vicinity of the facility, and the company that owns the facility. The two main ways bad actors may access information or control from a hydrogen storage facility are through information technology and operations technology devices, the former of which refers to data and information from networked devices and the latter of which refers to onsite controls for the physical system. Both types of entryways into the system should be considered when companies conduct cyber risk assessments and when regulators develop or revise relevant codes and standards. This report analyzes cybersecurity risks associated with a generic hydrogen storage system by outlining the system's purpose and the importance of its cybersecurity. The hydrogen storage system architecture and communication protocols are provided to understand potential cyber vulnerabilities. Later, an event tree analysis is performed on hydrogen operation to identify system weaknesses by outlining potential attack scenarios. This report also identifies critical cyber assets related to different hydrogen operations followed by an examination of potential threats, and the impact of cyber assets on those operational assets.

08 HYDROGEN↗
DOE OSTI2024

Large-Scale Hydrogen Storage Cyber Risk Assessment

Hydrogen storage systems are becoming more widely deployed throughout the country, and as their presence continues to grow, it is possible that individual and interconnected systems will be exposed to cyber-attacks. These events can cause physical and financial harm to employees, people in the vicinity, and to the company that owns the facility. The two main mechanisms malicious actors may access information or control from a hydrogen storage facility are through information technology and operations technology devices, the former of which refers to data and information from networked devices and the latter of which refers to onsite controls for the physical system. Both types of entryways into the system should be considered when facility managers conduct cyber risk assessments and when regulators develop or revise relevant codes and standards. This report analyzes cybersecurity risks applicable to a wide variety of hydrogen storage systems by outlining the system's purpose and the importance of its cybersecurity. The hydrogen storage system architecture and communication protocols are provided to understand potential cyber vulnerabilities. Later, an event tree analysis is performed on hydrogen operation to identify system weaknesses by outlining potential attack scenarios. This report also identifies critical cyber assets related to different hydrogen operations followed by an examination of potential threats, and the impact of cyber assets on those operational assets.

08 HYDROGEN↗
DOE OSTI2022

Operation Upshot-Knothole

A thundering boom and sheet of fire announced a dramatic, once in a lifetime event – the firing of a nuclear projectile from a 280 mm cannon. Travelling seven miles downrange, the projectile detonated 500 feet over the Nevada Test Site’s Frenchman Flat with a force of fifteen kilotons. Although the test was successful, the massive cannon, designed for tactical battlefield use, had a critical flaw. It was too large and cumbersome for mobile combat. The cannon test, codenamed Grable, was part of Operation Upshot-Knothole, the fourth test series conducted in Nevada. Beginning with the Annie test on March 17, 1953, and concluding with the Climax test on June 4th, the Operation consisted of three airdrops, seven tower detonations, and the one artillery shot. As with previous Nevada tests, the demarcation between the weapon development series, Upshot, and the DOD effects tests, Knothole, was blurred. All eleven tests generated data in support of weapon development as well as tactical battlefield applications. During Upshot-Knothole, the University of California Radiation Laboratory (UCRL), conducted its first ever tests, Ruth and Ray. Although neither test was successful, these failures, along with another a year later at Operation Castle, provided the new laboratory with design data leading to future successful tests. The Federal Civil Defense Administration (FCDA) participated in a test series with the general goal of preparing for societal recovery from a nuclear attack. Known as Operation Doorstep, the FCDA’s experiments evaluated the effects of a nuclear attack on food supplies, housing and urban structures, and even a copse of trees transplanted from nearby Mt. Charleston. A contingent of the press witnessed test Annie from an outcropping of rocks, subsequently named News Nob. The presence of the news media, along with the FCDA’s participation, marked a fundamental change in the Nevada test program - a change that that now included the general populace, whose survival was, of course, the rationale for testing. The Department of Defense continued and expanded its Desert Rock exercises of troop exercises, helicopter operations, and evaluation of blast damage to military aircraft, tanks, and guns. Annie, Nancy, Badger, Simon, Encore, and Grable involved larger numbers of personnel than the other five shots. Below is an edited and condensed account of Desert Rock activities compiled by the Defense Threat Reduction Agency.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2013

Decision Analysis Methods For the Analysis of Nuclear Terrorism Threats with Imperfect Information

Protection of commercial nuclear fuel cycles against adversary groups who may attempt to steal weapons-usable material is an issue of interest to the U.S. Department of Energy, Office of Nuclear Energy. The wide array of potential nuclear fuel cycle configurations and the security measures that could be deployed to protect them pose a daunting system design problem. In general, there are billions of potential design configurations. Moreover, the design choices are not independent – the effectiveness of one design feature or security measure can affect the desirability of other design choices. To explore this issue, we develop a mixed-integer optimization problem that solves a complex decision tree to allocate security resources across a commercial nuclear fuel cycle configuration. A nominal UREX+ reprocessing facility is used to demonstrate how the model allocates resources across a particular fuel cycle facility and shows how the problem can be scaled up to include other facilities and design features in the fuel cycle. In our model, the defender first chooses a set of safeguards to implement in order to mitigate the risk of a terrorist theft from various unit operations in the reprocessing facility. The attacker observes the defender’s strategy choice and executes an attack to acquire material, including the possibility of declining to attack at all. The model considers the terrorist as an intelligent adversary with a set of attack strategies known to the defender. The strategic choices made by both the defender and attacker affect the probability of a successful attack by the terrorist and in turn affect the distribution of consequences to the defender. We use a decision tree to illustrate the interactions among costs, decisions, correlated uncertainties, and outcomes, and then show how to formulate the problem as a mixed-integer program with an equivalent solution. The novel features of our formulation include the following. First, as discussed above, we use a sequential game-theoretic formulation (a Stackelberg game). This formulation assumes the attacker can observe the defender’s actions before the attack is executed. Many previous game-theoretic studies have assumed attacker and defender act simultaneously without knowledge of the other’s strategy choice. We believe the sequential game formulation is more applicable for knowledgeable insider adversaries, the focus of this study. Second, we build on our previous work that has developed a methodology to reflect the correlation among probability distributions represented as discrete chance nodes in a decision tree. We illustrate how this methodology can be applied to two chance nodes in a decision tree that relate the quality of the materials that might be stolen or diverted by an adversary with the yield of a weapon that might be constructed from that material. In a decision tree, the dependence of the yield on the quality of the material could be modeled with conditional probabilities of the weapon yield given the quality of the materials obtained. For three outcome discretizations, this would require the assessment of nine conditional probabilities from domain experts as part of the model building effort. In contrast, our methodology will allow this same representation of the tree based on the assessment of the correlation between these two chance nodes by the experts, which we believe is a much easier and more reliable assessment task. The advantage of this approach increases as the number of dependent chance nodes grows in a decision tree representation of this problem. Third, we extend this approach to the approximation of the dependence between discrete binary events such as success or failure represented by binary chance nodes in a decision tree or in the special case of a probabilistic risk analysis (PRA) based on an event tree. This extension is a novel application in this domain, and uses Monte Carlo simulation to determine the appropriate conditional probabilities on the binary chance nodes when the number of correlated uncertainties is more than two. Fourth, we show how these decision trees or probability trees can be formulated and solved as equivalent integer programming (IP) problems. The advantages of the IP representation include the opportunity to use the computing power associated with powerful optimization solver algorithms available on high speed computers to solve very large versions of these trees, and the ability to include several such models of different facilities into a much larger IP with connecting constraints that represent budget restrictions, number of available inspection opportunities in a given time window, or other dependencies among these facilities. Policy makers can use the resulting model to inform their decisions about how to assess terrorism risk and to safeguard the commercial nuclear fuel cycle.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2017

Fires Following Bark Beetles: Factors Controlling Severity and Disturbance Interactions in Ponderosa Pine

Previous studies have suggested that bark beetles and fires can be interacting disturbances, whereby bark beetle–caused tree mortality can alter the risk and severity of subsequent wildland fires. However, there remains considerable uncertainty around the type and magnitude of the interaction between fires following bark beetle attacks, especially in drier forest types such as those dominated by ponderosa pine (Pinus ponderosa Lawson & C. Lawson). We used a full factorial design across a range of factors thought to control bark beetle-fire interactions, including the temporal phase of the outbreak, level of mortality, and wind speed. We used a three-dimensional physics-based model, HIGRAD/FIRETEC, to simulate fire behavior in fuel beds representative of 60 field plots across five national forests in northern Arizona, USA. The plots were dominated by ponderosa pine, and encompassed a gradient of bark beetle–caused mortality due to a mixture of both Ips and Dendroctonus species. Non-host species included two sprouting species, Gambel oak (Quercus gambelii Nutt.) and alligator juniper (Juniperus deppeana Steud.), as well as other junipers and pinyon pine (Pinus edulis Engelm.). The simulations explicitly accounted for the modifications of fuel mass and moisture distribution caused by bark beetle–caused mortality. We first analyzed the influence of the outbreak phase, level of mortality, and wind speed on the severity of a subsequent fire, expressed as a function of live and dead canopy fuel consumption. We then computed a metric based on canopy fuel loss to characterize whether bark beetles and fire are linked disturbances and, if they are, if the linkage is antagonistic (net bark beetle and fire severity being less than if the two disturbances occurred independently) or synergistic (greater combined effects than independent disturbances). Both the severity of a subsequent fire and whether bark beetles and fire are linked disturbances depended on the outbreak phase of the bark beetle mortality and attack severity, as well as the fire weather (here, wind). Greater fire severity and synergistic interactions were generally associated with the “red phase” (when dead needles remain on trees). In contrast, during the “gray phase” (when dead needles had fallen to the ground), fire severity was either similar to, or less than, green-phase fires and interactions were generally antagonistic, but included both synergistic and neutral interactions. The simulations also revealed that the magnitude of the linkage between these two disturbances was smaller for fires occurring during high wind conditions, especially in the red phase. This complexity might be a reason for the contrasted or controversial perception of bark beetle-fire interactions reported in the literature, since both fire severity and the type and magnitude of the linkage can vary strongly among studies. These results suggest that, for fires burning in the gray phase following moderate levels of mortality, bark beetle–caused mortality may buffer rather than exacerbate fire severity. However, for fires burning under high wind speeds, regardless of the outbreak phase or level of mortality, the near complete loss of canopy fuels may push this ecosystem into an alternative state dominated by sprouting species.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Dynamic probabilistic risk assessment and game theory for cyber security risk analysis in nuclear power plants

Nuclear Power Plants and energy systems have become more prone to cyber-attacks with their digitalization and the increased use of smart equipment. Hence, it is important to quantify the risk associated with cyber-attacks in such systems. Dynamic Probabilistic Risk Assessment which involves studying the evolution of a system due to random events and operator and attacker actions during a cyber-attack by employing a physics-based model of the system is a suitable framework to quantify cybersecurity risk in nuclear power plants. In addition to the plant dynamics, it is also important to model the strategies of the attackers and plant operators for an effective cybersecurity risk assessment. Game theory provides a set of necessary tools to model such strategic interactions. In this research, a framework that integrates dynamic probabilistic risk assessment with game theory for cybersecurity risk analysis in nuclear power plants is presented. The mathematical formulation is derived based on the theory of continuous event trees. We propose a game theory based action model, that utilizes physics-based rewards to define the strategies of attackers and operators at every decision epoch. As a case study, the risk associated with cyber-attacks on the digital components in the secondary side of a pressurized water reactor is studied using a reduced order model. A set of attacker actions and a set of operator actions are defined for the system. The operator and attacker interactions were modelled using simultaneous game, their action policies were computed using the concept of mixed strategy Nash equilibrium and the evolution of the system was studied.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Risk informed cyber security for nuclear power plants - 342

Nuclear power plants are increasingly adding digital components for plant operation, safety, and security. These digital components fill a gap with legacy equipment where replacement components no longer exist. They also benefit operation of the plant by increasing efficiency in power generation, monitoring of equipment and plant parameters, as well as aiding operator control. However, the addition of digital components and systems also adds cyber risks with previously unanalyzed failure modes and attack vectors are introduced with these new systems. These risks are difficult to identify, analyze, and mitigate due to the increasingly complex nature of the digital components and the integration of these components with additional plant processes and communication networks. The research presented in this paper develops a new method that addresses the cyber risk to inform appropriate levels of protection. EPRI and Sandia are working under a Cooperative Research and Development Agreement to develop an effective method of evaluating the cyber risk in production nuclear power facilities. The Cyber Hazards Analysis Risk Methodology (CHARM) focuses on ensuring adequate controls are in place for appropriate cyber protection of the plant from radiological release or generation risk. Existing plant hazards analyses (e.g., PRA, FTA) do not account for software deficiencies or adversarial intent. This method leverages existing plant analyses and MIT's Systems Theoretic Process Analysis (STPA) to create cyber informed fault trees. These new fault trees will provide the basis for comprehensive cyber risk analysis and help ensure potential gaps in cyber security controls are identified and corrected. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

IViz-OT (Intrusion Detection Visualizer for Operational Technology Network) [SWR-22-63]

The Visualizer dashboard provides grid operator highly-trusted alarming environment for an ongoing or potential cyber-attack based on system anomalies and network-based verification. Once anomalies are detected by the IDS tool (HIDES, NREL SWR-19-65), this platform stores the signatures or alert logs that are generated by the intrusion detector, lays out the detailed summary of the possible alerts, and maps these attacks with high-level scenarios. These scenarios are later combined to define a final event using a decision tree approach and a final report is generated out of this tool for further forensic analysis. It also supports authentication and authorization to support roles-based access control (RBAC) for users and a group of people.

Singh, Vivek Kumar↗
DOE OSTI2020

Detection and Diagnosis of Data Integrity Attacks in Solar Farms Based on Multilayer Long Short-Term Memory Network

Photovoltaic (PV) systems are becoming more vulnerable to cyber threats. In response to this emerging concern, developing cyber-secure power electronics converters has received increased attention from the IEEE Power Electronics Society that recently launched a cyber-physical-security initiative. Here this letter proposes a deep sequence learning based diagnosis solution for data integrity attacks on PV systems in smart grids, including dc–dc and dc–ac converters. The multilayer long short-term memory networks are used to leverage time-series electric waveform data from current and voltage sensors in PV systems. The proposed method has been evaluated in a PV smart grid benchmark model with extensive quantitative analysis. As a comparison, we have evaluated classic data-driven methods, including K-nearest neighbor, decision tree, support vector machine, artificial neural network, and convolutional neural network. Comparison results verify performances of the proposed method for detection and diagnosis of various data integrity attacks on PV systems.

42 ENGINEERING↗
DOE OSTI2026

Validating a Dynamic PWR Safety and Security Model?

Nuclear power plants (NPPs) are assessed for safety and security using separate models that cannot capture how an attacker's decisions and a plant's response unfold together in real time, leaving regulators and operators without a complete picture of true plant vulnerability. Traditional probabilistic risk assessment (PRA) methods treat adversarial events as fixed initiators with predetermined outcomes, and are structurally incapable of representing the time-dependent interplay between physical security events, safety system response, and operator mitigative actions. At Idaho National Laboratory (INL), I contributed to the development and validation of Modeling and Analysis for Safety and Security using the Dynamic EMRALD Framework (MASS-DEF). Where static PRA relies on event-tree logic that cannot evolve mid-scenario, MASS-DEF couples a time-dependent dynamic PRA tool EMRALD (Event Modeling Risk Assessment using Linked Diagrams) with attack simulation software, allowing attacker behavior, plant system states, and operator actions to interact across time. My work focused on validating a general Pressurized Water Reactor (PWR) model. I traced model logic against PWR plant to identified errors in logic and confirm accuracy. I then built and tested attack scenarios against a general PWR model to verify that the model produced expected outcomes across all logical pathways. I also contributed a section to a related technical paper applying the same EMRALD platform to radiation dose modeling. Results show that MASS-DEF can quantitatively demonstrate that many plants exceed their regulatory security thresholds. This demonstrated margin provides a technically defensible basis for reducing the number of guards without compromising regulatory compliance. Physical security costs represent roughly 10% of annual operating budgets, making such reductions directly meaningful to INL's mission of sustaining existing commercial NPPs. This internship strengthened my understanding of nuclear systems, probabilistic modeling, and technical writing, and has solidified my pursuit of a career at a national laboratory.

98 - NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL↗
DOE OSTI2015

A Selectivity based approach to Continuous Pattern Detection in Streaming Graphs

Cyber security is one of the most significant technical challenges in current times. Detecting adversarial activities, prevention of theft of intellectual properties and customer data is a high priority for corporations and government agencies around the world. Cyber defenders need to analyze massive-scale, high-resolution network flows to identify, categorize, and mitigate attacks involving networks spanning institutional and national boundaries. Many of the cyber attacks can be described as subgraph patterns, with prominent examples being insider infiltrations (path queries), denial of service (parallel paths) and malicious spreads (tree queries). This motivates us to explore subgraph matching on streaming graphs in a continuous setting. The novelty of our work lies in using the subgraph distributional statistics collected from the streaming graph to determine the query processing strategy. We introduce a ``Lazy Search" algorithm where the search strategy is decided on a vertex-to-vertex basis depending on the likelihood of a match in the vertex neighborhood. We also propose a metric named ``Relative Selectivity" that is used to select between different query processing strategies. Our experiments performed on real online news, network traffic stream and a synthetic social network benchmark demonstrate 10-100x speedups over non-incremental, selectivity agnostic approaches.

dynamic graphs↗
DOE OSTI2015

A Selectivity based approach to Continuous Pattern Detection in Streaming Graphs

Cyber security is one of the most significant technical challenges in current times. Detecting adversarial activities, prevention of theft of intellectual properties and customer data is a high priority for corporations and government agencies around the world. Cyber defenders need to analyze massive-scale, high-resolution network flows to identify, categorize, and mitigate attacks involving net- works spanning institutional and national boundaries. Many of the cyber attacks can be described as subgraph patterns, with promi- nent examples being insider infiltrations (path queries), denial of service (parallel paths) and malicious spreads (tree queries). This motivates us to explore subgraph matching on streaming graphs in a continuous setting. The novelty of our work lies in using the subgraph distributional statistics collected from the streaming graph to determine the query processing strategy. We introduce a “Lazy Search" algorithm where the search strategy is decided on a vertex-to-vertex basis depending on the likelihood of a match in the vertex neighborhood. We also propose a metric named “Relative Selectivity" that is used to se- lect between different query processing strategies. Our experiments performed on real online news, network traffic stream and a syn- thetic social network benchmark demonstrate 10-100x speedups over selectivity agnostic approaches.

graphs, cybersecurity↗