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  1. Book ; Online: Evolving Code with A Large Language Model

    Hemberg, Erik / Moskal, Stephen / O'Reilly, Una-May

    2024  

    Abstract: Algorithms that use Large Language Models (LLMs) to evolve code arrived on the Genetic Programming (GP) scene very recently. We present LLM GP, a formalized LLM-based evolutionary algorithm designed to evolve code. Like GP, it uses evolutionary operators, ...

    Abstract Algorithms that use Large Language Models (LLMs) to evolve code arrived on the Genetic Programming (GP) scene very recently. We present LLM GP, a formalized LLM-based evolutionary algorithm designed to evolve code. Like GP, it uses evolutionary operators, but its designs and implementations of those operators radically differ from GP's because they enlist an LLM, using prompting and the LLM's pre-trained pattern matching and sequence completion capability. We also present a demonstration-level variant of LLM GP and share its code. By addressing algorithms that range from the formal to hands-on, we cover design and LLM-usage considerations as well as the scientific challenges that arise when using an LLM for genetic programming.

    Comment: 34 pages, 9 figures, 6 Tables
    Keywords Computer Science - Neural and Evolutionary Computing ; Computer Science - Artificial Intelligence ; I.2.8
    Publishing date 2024-01-13
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Evaluating efficacy of indoor non-pharmaceutical interventions against COVID-19 outbreaks with a coupled spatial-SIR agent-based simulation framework.

    Gunaratne, Chathika / Reyes, Rene / Hemberg, Erik / O'Reilly, Una-May

    Scientific reports

    2022  Volume 12, Issue 1, Page(s) 6202

    Abstract: Contagious respiratory diseases, such as COVID-19, depend on sufficiently prolonged exposures for the successful transmission of the underlying pathogen. It is important that organizations evaluate the efficacy of non-pharmaceutical interventions aimed ... ...

    Abstract Contagious respiratory diseases, such as COVID-19, depend on sufficiently prolonged exposures for the successful transmission of the underlying pathogen. It is important that organizations evaluate the efficacy of non-pharmaceutical interventions aimed at mitigating viral transmission among their personnel. We have developed a operational risk assessment simulation framework that couples a spatial agent-based model of movement with an agent-based SIR model to assess the relative risks of different intervention strategies. By applying our model on MIT's Stata center, we assess the impacts of three possible dimensions of intervention: one-way vs unrestricted movement, population size allowed onsite, and frequency of leaving designated work location for breaks. We find that there is no significant impact made by one-way movement restrictions over unrestricted movement. Instead, we find that reducing the frequency at which individuals leave their workstations combined with lowering the number of individuals admitted below the current recommendations lowers the likelihood of highly connected individuals within the contact networks that emerge, which in turn lowers the overall risk of infection. We discover three classes of possible interventions based on their epidemiological effects. By assuming a direct relationship between data on secondary attack rates and transmissibility in the agent-based SIR model, we compare relative infection risk of four respiratory illnesses, MERS, SARS, COVID-19, and Measles, within the simulated area, and recommend appropriate intervention guidelines.
    MeSH term(s) COVID-19/epidemiology ; COVID-19/prevention & control ; Computer Simulation ; Disease Outbreaks/prevention & control ; Humans ; Incidence ; Measles
    Language English
    Publishing date 2022-04-13
    Publishing country England
    Document type Journal Article ; Research Support, U.S. Gov't, Non-P.H.S. ; Research Support, Non-U.S. Gov't
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-022-09942-y
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Book ; Online: LLMs Killed the Script Kiddie

    Moskal, Stephen / Laney, Sam / Hemberg, Erik / O'Reilly, Una-May

    How Agents Supported by Large Language Models Change the Landscape of Network Threat Testing

    2023  

    Abstract: In this paper, we explore the potential of Large Language Models (LLMs) to reason about threats, generate information about tools, and automate cyber campaigns. We begin with a manual exploration of LLMs in supporting specific threat-related actions and ... ...

    Abstract In this paper, we explore the potential of Large Language Models (LLMs) to reason about threats, generate information about tools, and automate cyber campaigns. We begin with a manual exploration of LLMs in supporting specific threat-related actions and decisions. We proceed by automating the decision process in a cyber campaign. We present prompt engineering approaches for a plan-act-report loop for one action of a threat campaign and and a prompt chaining design that directs the sequential decision process of a multi-action campaign. We assess the extent of LLM's cyber-specific knowledge w.r.t the short campaign we demonstrate and provide insights into prompt design for eliciting actionable responses. We discuss the potential impact of LLMs on the threat landscape and the ethical considerations of using LLMs for accelerating threat actor capabilities. We report a promising, yet concerning, application of generative AI to cyber threats. However, the LLM's capabilities to deal with more complex networks, sophisticated vulnerabilities, and the sensitivity of prompts are open questions. This research should spur deliberations over the inevitable advancements in LLM-supported cyber adversarial landscape.
    Keywords Computer Science - Cryptography and Security ; Computer Science - Machine Learning
    Subject code 006
    Publishing date 2023-10-10
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Book ; Online: Using a Collated Cybersecurity Dataset for Machine Learning and Artificial Intelligence

    Hemberg, Erik / O'Reilly, Una-May

    2021  

    Abstract: Artificial Intelligence (AI) and Machine Learning (ML) algorithms can support the span of indicator-level, e.g. anomaly detection, to behavioral level cyber security modeling and inference. This contribution is based on a dataset named BRON which is ... ...

    Abstract Artificial Intelligence (AI) and Machine Learning (ML) algorithms can support the span of indicator-level, e.g. anomaly detection, to behavioral level cyber security modeling and inference. This contribution is based on a dataset named BRON which is amalgamated from public threat and vulnerability behavioral sources. We demonstrate how BRON can support prediction of related threat techniques and attack patterns. We also discuss other AI and ML uses of BRON to exploit its behavioral knowledge.

    Comment: 5 pages, 2 Figures, 2 Tables, ACM KDD AI4Cyber: The 1st Workshop on Artificial Intelligence- enabled Cybersecurity Analytics at KDD'21
    Keywords Computer Science - Cryptography and Security ; Computer Science - Artificial Intelligence
    Publishing date 2021-08-05
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Book ; Online: Analyzing the Components of Distributed Coevolutionary GAN Training

    Toutouh, Jamal / Hemberg, Erik / O'Reilly, Una-May

    2020  

    Abstract: Distributed coevolutionary Generative Adversarial Network (GAN) training has empirically shown success in overcoming GAN training pathologies. This is mainly due to diversity maintenance in the populations of generators and discriminators during the ... ...

    Abstract Distributed coevolutionary Generative Adversarial Network (GAN) training has empirically shown success in overcoming GAN training pathologies. This is mainly due to diversity maintenance in the populations of generators and discriminators during the training process. The method studied here coevolves sub-populations on each cell of a spatial grid organized into overlapping Moore neighborhoods. We investigate the impact on the performance of two algorithm components that influence the diversity during coevolution: the performance-based selection/replacement inside each sub-population and the communication through migration of solutions (networks) among overlapping neighborhoods. In experiments on MNIST dataset, we find that the combination of these two components provides the best generative models. In addition, migrating solutions without applying selection in the sub-populations achieves competitive results, while selection without communication between cells reduces performance.

    Comment: Accepted as a full paper in Sixteenth International Conference on Parallel Problem Solving from Nature (PPSN XVI)
    Keywords Computer Science - Neural and Evolutionary Computing ; Computer Science - Distributed ; Parallel ; and Cluster Computing ; Computer Science - Machine Learning
    Subject code 006
    Publishing date 2020-08-03
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Book ; Online: Re-purposing Heterogeneous Generative Ensembles with Evolutionary Computation

    Toutouh, Jamal / Hemberg, Erik / O'Reilly, Una-May

    2020  

    Abstract: Generative Adversarial Networks (GANs) are popular tools for generative modeling. The dynamics of their adversarial learning give rise to convergence pathologies during training such as mode and discriminator collapse. In machine learning, ensembles of ... ...

    Abstract Generative Adversarial Networks (GANs) are popular tools for generative modeling. The dynamics of their adversarial learning give rise to convergence pathologies during training such as mode and discriminator collapse. In machine learning, ensembles of predictors demonstrate better results than a single predictor for many tasks. In this study, we apply two evolutionary algorithms (EAs) to create ensembles to re-purpose generative models, i.e., given a set of heterogeneous generators that were optimized for one objective (e.g., minimize Frechet Inception Distance), create ensembles of them for optimizing a different objective (e.g., maximize the diversity of the generated samples). The first method is restricted by the exact size of the ensemble and the second method only restricts the upper bound of the ensemble size. Experimental analysis on the MNIST image benchmark demonstrates that both EA ensembles creation methods can re-purpose the models, without reducing their original functionality. The EA-based demonstrate significantly better performance compared to other heuristic-based methods. When comparing both evolutionary, the one with only an upper size bound on the ensemble size is the best.

    Comment: Accepted as a full paper for the Genetic and Evolutionary Computation Conference - GECCO'20
    Keywords Computer Science - Artificial Intelligence ; Computer Science - Machine Learning ; Computer Science - Neural and Evolutionary Computing
    Subject code 006
    Publishing date 2020-03-30
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Book ; Online: Data Dieting in GAN Training

    Toutouh, Jamal / O'Reilly, Una-May / Hemberg, Erik

    2020  

    Abstract: We investigate training Generative Adversarial Networks, GANs, with less data. Subsets of the training dataset can express empirical sample diversity while reducing training resource requirements, e.g. time and memory. We ask how much data reduction ... ...

    Abstract We investigate training Generative Adversarial Networks, GANs, with less data. Subsets of the training dataset can express empirical sample diversity while reducing training resource requirements, e.g. time and memory. We ask how much data reduction impacts generator performance and gauge the additive value of generator ensembles. In addition to considering stand-alone GAN training and ensembles of generator models, we also consider reduced data training on an evolutionary GAN training framework named Redux-Lipizzaner. Redux-Lipizzaner makes GAN training more robust and accurate by exploiting overlapping neighborhood-based training on a spatial 2D grid. We conduct empirical experiments on Redux-Lipizzaner using the MNIST and CelebA data sets.

    Comment: Chapter 14 of the Book "Deep Neural Evolution - Deep Learning with Evolutionary Computation"
    Keywords Computer Science - Machine Learning ; Computer Science - Neural and Evolutionary Computing
    Publishing date 2020-04-06
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Book ; Online: Automating Cyber Threat Hunting Using NLP, Automated Query Generation, and Genetic Perturbation

    Karuna, Prakruthi / Hemberg, Erik / O'Reilly, Una-May / Rutar, Nick

    2021  

    Abstract: Scaling the cyber hunt problem poses several key technical challenges. Detecting and characterizing cyber threats at scale in large enterprise networks is hard because of the vast quantity and complexity of the data that must be analyzed as adversaries ... ...

    Abstract Scaling the cyber hunt problem poses several key technical challenges. Detecting and characterizing cyber threats at scale in large enterprise networks is hard because of the vast quantity and complexity of the data that must be analyzed as adversaries deploy varied and evolving tactics to accomplish their goals. There is a great need to automate all aspects, and, indeed, the workflow of cyber hunting. AI offers many ways to support this. We have developed the WILEE system that automates cyber threat hunting by translating high-level threat descriptions into many possible concrete implementations. Both the (high-level) abstract and (low-level) concrete implementations are represented using a custom domain specific language (DSL). WILEE uses the implementations along with other logic, also written in the DSL, to automatically generate queries to confirm (or refute) any hypotheses tied to the potential adversarial workflows represented at various layers of abstraction.

    Comment: 5 pages 8 figures
    Keywords Computer Science - Cryptography and Security ; Computer Science - Artificial Intelligence
    Subject code 006
    Publishing date 2021-04-23
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Book ; Online: Evaluating Efficacy of Indoor Non-Pharmaceutical Interventions against COVID-19 Outbreaks with a Coupled Spatial-SIR Agent-Based Simulation Framework

    Gunaratne, Chathika / Reyes, Rene / Hemberg, Erik / O'Reilly, Una-May

    2021  

    Abstract: Contagious respiratory diseases, such as COVID-19, depend on sufficiently prolonged exposures for the successful transmission of the underlying pathogen. It is important for organizations to evaluate the efficacy of interventions aiming at mitigating ... ...

    Abstract Contagious respiratory diseases, such as COVID-19, depend on sufficiently prolonged exposures for the successful transmission of the underlying pathogen. It is important for organizations to evaluate the efficacy of interventions aiming at mitigating viral transmission among their personnel. We have developed a operational risk assessment simulation framework that couples a spatial agent-based model of movement with a SIR epidemiological model to assess the relative risks of different intervention strategies. By applying our model on MIT's STATA building, we assess the impacts of three possible dimensions of intervention: one-way vs unrestricted movement, population size allowed onsite, and frequency of leaving designated work location for breaks. We find that there is no significant impact made by one-way movement restrictions over unrestricted movement. Instead, we find that a combination of lowering the number of individuals admitted below the current recommendations and advising individuals to reduce the frequency at which they leave their workstations lowers the likelihood of highly connected individuals within the contact networks that emerge, which in turn lowers the overall risk of infection. We discover three classes of possible interventions based on their epidemiological effects. By assuming a direct relationship between data on secondary attack rates and transmissibility in the SIR model, we compare relative infection risk of four respiratory diseases, MERS, SARS, COVID-19, and Measles, within the simulated area, and recommend appropriate intervention guidelines.
    Keywords Quantitative Biology - Populations and Evolution ; Computer Science - Social and Information Networks ; Quantitative Biology - Quantitative Methods
    Publishing date 2021-08-24
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Article ; Online: Stratified locality-sensitive hashing for accelerated physiological time series retrieval.

    Kim, Yongwook Bryce / Hemberg, Erik / O'Reilly, Una-May

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

    2017  Volume 2016, Page(s) 2479–2483

    Abstract: We introduce stratified locality-sensitive hashing (SLSH) for retrieving similar physiological waveform time series. SLSH further accelerates the sublinear retrieval time obtained by the standard locality-sensitive hashing (LSH) method. The standard ... ...

    Abstract We introduce stratified locality-sensitive hashing (SLSH) for retrieving similar physiological waveform time series. SLSH further accelerates the sublinear retrieval time obtained by the standard locality-sensitive hashing (LSH) method. The standard family of locality-sensitive hash functions is limited to provide only a single perspective on the data due to its one-to-one relationship to a distinct distance function for measuring similarity. SLSH incorporates multiple locality-sensitive hash families with various distance functions enabling it to examine the data with more diverse and refined perspectives. We provide the procedures of SLSH with locality-sensitive hash families for the l1 and the cosine distances, and compare its performance to the standard LSH on an arterial blood pressure time series data extracted from the physiological waveform repository of the MIMIC2 database. The time to retrieve five most similar waveforms by SLSH is 14 times faster than the linear search and 1.7 times faster than the standard LSH when we allow 5% decrease in accuracy as a trade-off.
    MeSH term(s) Algorithms ; Databases, Factual ; Humans ; Monitoring, Physiologic
    Language English
    Publishing date 2017-03-08
    Publishing country United States
    Document type Journal Article
    ISSN 2694-0604
    ISSN (online) 2694-0604
    DOI 10.1109/EMBC.2016.7591233
    Database MEDical Literature Analysis and Retrieval System OnLINE

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