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检索条件"任意字段=Conference on Artificial Intelligence and Machine Learning in Defense Applications IV"

共 594 条记录，以下是111-120 订阅

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Distributed hierarchical reinforcement learning in multi-agent adversarial environments 4

Distributed hierarchical reinforcement learning in multi-age...

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conference on artificial intelligence and machine learning for Multi-Domain Operations applications iv

作者： Naderializadeh, Navid Soleyman, Sean Hung, Fan Khosla, Deepak Chen, Yang Fadaie, Joshua G. HRL Labs LLC 3011 Malibu Canyon Rd Malibu CA 90265 USA Univ Penn 200 South 33rd St Philadelphia PA 19104 USA

ISBN: (纸本)9781510651036;9781510651029

We develop a hierarchical approach for controlling a team of aircraft in multi-agent adversarial environments. Each individual aircraft is equipped with a high-level agent that is solely responsible for target assignment decisions, and a low-level agent that generates actions based only on the selected target. We use distributed deep reinforcement learning to train the high-level agents, and neuroevolution to train the low-level agents. This approach leverages centralized training for decentralized execution to enable individual autonomy when communication is limited. Simulation results confirm the superiority of our proposed approach as compared to non-hierarchical multi-agent reinforcement learning methods.

关键词： distributed decentralized neuroevolution reinforcement learning machine learning artificial intelligence multi-agent adversarial

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Recent Advances in Multi-modal Data Fusion: Status, Challenges and Perspectives 4

Recent Advances in Multi-modal Data Fusion: Status, Challeng...

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conference on artificial intelligence and machine learning for Multi-Domain Operations applications iv

作者： Rawal, Atul McCoy, James Raglin, Adrienne Rawat, Danda B. Sadler, Brian M. Howard Univ Ctr Excellence AI ML CoE AIML Dept Elect Engn & Comp Sci Washington DC 20059 USA US Army Res Lab Adelphi MD USA

ISBN: (纸本)9781510651036;9781510651029

Advancements in computer science, especially artificial intelligence (AI) and machine learning(ML) have brought about a scientific revolution in a plethora of military and commercial applications. One of such area has been data science, where the sheer astronomical amount of available data has spurred sub-fields of research involving its storage, analysis, and use. One such area of focus in recent years has been the fusion of data coming from multiple modalities, called multi-modal data fusion, and their use and analysis for practical and employable applications. Because of the differences within the data types, ranging from infrared/radio-frequency to audio/visual, it is extremely difficult, if not flat-out impossible, to analyze them via one singular method. The need to fuse multiple data types and sources properly and adequately for analysis, therefore arises an extra degree of freedom for data-science. This paper provides a survey for multi-modal data fusion. We provide an in-depth review of multi-modal data fusion themes, and describe the methods for designing and developing such data fusion techniques. We include an overview for the different methods and levels of data fusion techniques. An overview of security of data-fusion techniques, is also provided which highlights the present gaps within the field that need to be addressed.

关键词： Data fusion Data modeling Sensors Computer security Image encryption defense and security Analytical research Systems modeling Fuzzy logic Head

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Advancing Malware defense: A Hybrid Approach with Explainable Deep Neural Networks 2

Advancing Malware Defense: A Hybrid Approach with Explainabl...

引用

2nd DMIHER International conference on artificial intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Sharma, Deepika Devare, Manoj Department of Engineering and Technology Navi Mumbai India Amity University Amity Institute of Information Technology Mumbai India

ISBN: (纸本)9798331528713

Malware detection and classification remain critical challenges in cybersecurity, necessitating advanced techniques for combating increasingly sophisticated threats. This paper presents a novel hybrid approach that leverages the power of explainable deep neural networks to enhance malware detection and classification accuracy while providing interpretable results. The proposed method combines static and dynamic analysis techniques to extract a comprehensive set of features from both executable files and runtime behaviors. These features are then processed using a custom-designed deep neural network architecture that incorporates attention mechanisms and interpretability layers. The model not only achieves high accuracy in detecting and classifying various malware families, but also offers explanations for its decisions, addressing the "black box"problem often associated with deep learning models. The experimental results demonstrated the effectiveness of this hybrid approach, showing significant improvements over traditional machine learning methods and baseline deep learning models. The explainable component of the system provides valuable insights into the specific characteristics and behaviors that contribute to malware identification, potentially aiding the development of more robust defense mechanisms. This research contributes to the field by offering a powerful, interpretable tool for malware analysis that can adapt to evolving threats while providing transparency in its decision-making process, thus bridging the gap between advanced AI techniques and practical cybersecurity applications. © 2024 IEEE.

关键词： Deep neural networks

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Evaluating the Effectiveness of Attacks and defenses on machine learning Through Adversarial Samples 16

Evaluating the Effectiveness of Attacks and Defenses on Mach...

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16th IEEE International conference on Software Testing, Verification and Validation (ICST)

作者： Gala, Viraj R. Schneider, Martin A. Fraunhofer FOKUS Business Unit Qual Engn Berlin Germany

ISBN: (纸本)9798350333350

Adversarial attacks can compromise the robustness of machine learning models, including neural networks. Adversarial defenses can be employed to mitigate the impact of adversarial attacks. Due to adaptive attacks, however, the adversarial defenses are vulnerable as well. This makes it onerous to employ neural networks in safety and security-critical areas. However, a perception of the effectiveness of adversarial attacks and defenses can facilitate the development of more robust neural networks that are suitable for applications in these areas. The purpose of this paper is to evaluate the effectiveness of adversarial attacks and defenses and determine the dependency of effectiveness on the chosen values of the underlying parameters. To that end, we evaluate the (adaptive) Carlini & Wagner attack and KDE defense to measure their effectiveness for a range of parameter values. This paper investigates the aforementioned attacks and defenses for the optimal values of the parameters. We also prove that by changing the value of parameters, the effectiveness of adversarial attacks and defenses can be improved and state the necessary trade-offs involved. Furthermore, to substantiate the effect of adversarial attacks and defenses on the effectiveness of adaptive attacks, this paper investigates the effectiveness of the adaptive CW attack for the corresponding optimal values of the CW attack and KDE defense parameters.

关键词： adversarial machine learning adversarial attacks CW attack adversarial defense KDE defense adaptive CW attack artificial intelligence testing

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Toward ML-Based Application for Vehicles Operation Cost Management 1

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8th International Scientific-Technical conference (MANUFACTURING)

作者： Rojek, Izabela Mikolajewski, Dariusz Przybylinski, Slawomir Dostatni, Ewa Sapietova, Alzbeta Kazimierz Wielki Univ Bydgoszcz Poland Poznan Univ Tech Poznan Poland Univ Zilina Zilina Slovakia

ISBN: (数字)9783031564673

ISBN: (纸本)9783031564697;9783031564673

This paper presents the design and implementation of a web-based application for managing vehicle operating costs. The application is intended to help vehicle users control and optimize the money spent on the maintenance of both a single vehicle and a group of vehicles (fleet) for the company. Moreover this application aims to collect data that will then serve as sample files that will then be used in a machine learning application. At the beginning of the work, the methodology and requirements to be met by the web application were discussed. In the following parts of the work, the implementation of the web application and tests of the application are presented. At the end of the work, proposals for further development of the application and conclusions of the work are presented. In the era of electric vehicles, such applications will play an increasingly important role, including as journey calculators (charging plans, etc., taking into account journey times, distance covered, stopping times, minimizing energy consumption, etc.), hence the role of machine learning (ML) will grow, as, for example, as a route is covered, the software will recalculate data and alternative options in real time, which can be reflected in ERP logistics and enterprise fleet management systems for sustainability.

关键词： artificial intelligence machine learning Industry 4.0 Fleet Management Cost Optimization Sustainable Exploitation

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learning Disentangled Discrete Representations

Learning Disentangled Discrete Representations

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5th International Workshop on learning with Imbalanced Domains - Theory and applications / European conference on machine learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Friede, David Reimers, Christian Stuckenschmidt, Heiner Niepert, Mathias Univ Mannheim Mannheim Germany Max Planck Inst Biogeochem Jena Germany Univ Stuttgart Stuttgart Germany NEC Labs Europe Heidelberg Germany

ISBN: (纸本)9783031434204;9783031434211

Recent successes in image generation, model-based reinforcement learning, and text-to-image generation have demonstrated the empirical advantages of discrete latent representations, although the reasons behind their benefits remain unclear. We explore the relationship between discrete latent spaces and disentangled representations by replacing the standard Gaussian variational autoencoder (VAE) with a tailored categorical variational autoencoder. We show that the underlying grid structure of categorical distributions mitigates the problem of rotational invariance associated with multivariate Gaussian distributions, acting as an efficient inductive prior for disentangled representations. We provide both analytical and empirical findings that demonstrate the advantages of discrete VAEs for learning disentangled representations. Furthermore, we introduce the first unsupervised model selection strategy that favors disentangled representations.

关键词： Categorical VAE Disentanglement

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2nd International conference on Next-Generation Computing and Information Systems, ICNGCIS 2023

2nd International Conference on Next-Generation Computing an...

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2nd International conference on Next-Generation Computing and Information Systems, ICNGCIS 2023

ISBN: (纸本)9781032738659

The proceedings contain papers. The special focus in this conference is on . The topics include: Impact of artificial intelligence on high-penetration renewable physical infrastructure;collaborative attacks and defense;building a safe and secure metaverse;distributed-computing based versatile healthcare services framework for diagnostic markers;supervised context-aware Latent Dirichlet allocation-based drug recommendation model;combination kernel support vector machine based digital twin model for prediction of dyslexia in distributed environment;Time series analysis of vegetation change using remote sensing, GIS and FB prophet;advances in smart farming for precision agriculture: Green-IoT and machine learning as a solution;Towards a serverless computing and edge-intelligence architecture for the Personal-Internet-of-Things (PIoT);IOT and developed deep learning based road accident detection system and societal knowledge management;Healthcare CAD model for hierarchical processing of surgical navigation system;applicability of eye tracking technology in virtual keyboard for human-computer interactions;a digital twin framework for smart contract-based DeFi applications in the metaverse: Towards interoperability, service scaleup & resilience;a novel approach to glass identification using ensemble learning for forensics;deep learning-based cognitive digital twin system for wrist pulse diagnostic and classification;Multi-class instance segmentation for the detection of cervical cancer cells using modified mask RCNN;dilated convolution model for lightweight neural network;artificial intelligence based Monte Carlo model for epidemic forecasting for societal aspect;efficient data visualization through novel recurrent neural network-based dimension reduction;An optimized CNN-based model for pneumonia detection;EEG-based emotion recognition: Leveraging CNNs for precision;A seven layer DNN approach for social-media multilevel image-based text classification.

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Literature Review on the Current State-of-the-Art in Research and Technological Advancements in the Field of machine learning Applied to Predictive Maintenance 43rd

Literature Review on the Current State-of-the-Art in Researc...

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43rd IFIP WG 5.7 International conference on Advances in Production Management Systems (APMS)

作者： Resanovic, Daniel Balc, Nicolae Univ Appl Sci CAMPUS 02 Graz Austria Tech Univ Cluj Napoca Cluj Napoca Romania

ISBN: (纸本)9783031716355;9783031716331;9783031716324

This literature review examines the technologies of machine learning (ML) in Predictive Maintenance (PdM), highlighting the necessity for industries to boost production efficiency for competitive advantage amid growing global demands. It underscores the pivotal roles of technological advancements, especially the Internet of Things (IoT) and Big Data, in enabling smarter automation and intelligent production processes. By leveraging equipment sensor data, Predictive Maintenance serves as a crucial proactive maintenance strategy, enhancing reliability and accuracy through its diagnostic and prognostic stages. However, challenges in financial, organizational, data, and repair complexities hinder its full-scale implementation, necessitating a deeper understanding of ML techniques in PdM. The review further discusses AI/ML's role in enhancing predictive maintenance, detailing applications of AI, ML, and Deep learning (DL) in predictive analytics and identifying emerging trends like transformers and self-supervised learning, which promise to improve PdM outcomes. Through a structured analysis, this report underscores the evolving landscape of ML applications in PdM, highlighting both challenges and opportunities, and suggesting further exploration of ML algorithms in this field.

关键词： Predictive Maintenance machine learning artificial intelligence State of ML in Predictive Maintenance Data Management

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SeqSHAP: Subsequence Level Shapley Value Explanations for Sequential Predictions 29th

SeqSHAP: Subsequence Level Shapley Value Explanations for Se...

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29th International conference on Database Systems for Advanced applications (DASFAA)

作者： Jiang, Guanyu Zhuang, Fuzhen Song, Bowen Zhu, Yongchun Sun, Ying Wang, Weiqiang Wang, Deqing Beihang Univ Inst Artificial Intelligence Beijing Peoples R China Beihang Univ Sch Comp Sci SKLSDE Beijing Peoples R China Chinese Acad Sci Inst Comp Technol Beijing Peoples R China Hong Kong Univ Sci & Technol Guangzhou Thrust Artificial Intelligence Guangzhou Guangdong Peoples R China

ISBN: (纸本)9789819755615;9789819755622

With the rapid development of AI techniques, models built on deep learning techniques have been applied to an increasing number of real-world scenarios. As the predictive performance of deep learning models continues to improve, their structures are also becoming increasingly complex with a large number of internal parameters, making it difficult for humans to intuitively understand their prediction mechanisms. As a result, there is an increasing demand for interpretability in machine learning (ML) applications to provide explanations for those black-box models. In recent years, many explainable AI (XAI) methods have been proposed to explain the prediction results of ML models, targeting various prediction tasks and application scenarios. However, most existing methods overlook the interpretability of sequential scenarios, which have a wide range of applications in the real world like fraud detection and sequence-based recommendation. In this paper, we propose SeqSHAP to explain sequential model predictions from a unique perspective. Compared to existing element-wise XAI methods, SeqSHAP provides intuitive explanations at the subsequence level, which explicitly models the effect of contextual information among the relevant neighboring elements in sequences. We also provide a distribution-based segmentation method to obtain reasonable subsequences from the sequential data. Extensive experiments on online transaction datasets collected from AliPay show that the proposed method could provide both faithful and reliable explanations for sequential predictions.

关键词： Explainable AI SHAP Subsequence Sequential Prediction

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Leveraging Model Distillation as a defense Against Adversarial Attacks Based on Deep learning 2

Leveraging Model Distillation as a Defense Against Adversari...

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2nd IEEE International conference on Communication, Security and artificial intelligence, ICCSAI 2023

作者： Suresh, K. Radha, J. Thilagaraj, T. Subramani, R. Lakineni, Prasanna Kumar Taqui, Syed Noeman Department of Computer Science Bangalore India Nandha Engineering College Department of Computer Science and Engineering Tamilnadu Perundurai India Moodlakatte Institute of Technology Department of Computer Science and Engineering Moodalakatte Karnataka Kundapura India Department of Mathematics Bangalore India Gitam School of Technology Gitam University Department of Computer Science and Engineering Visakhapatnam India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Vlsi Microelectronics Tamil Nadu Chennai India

ISBN: (纸本)9798350369960

Adversarial attacks on deep learning models threaten machine learning system security and reliability. The above attacks use modest data alterations to produce erroneous model results while being undetected by humans. This work suggests model distillation to prevent adversarial perturbations. The student model is taught to emulate the teacher model in model distillation. This is done using teacher model soft outputs. Our idea is that this strategy organically strengthens the student model against adversarial assaults by keeping the teacher model's essential knowledge and generalization capabilities while reducing weaknesses. Distilled models are more resilient to adversarial assaults than non-distilled models, according to experiments. These models also perform similarly on undamaged, uncorrupted data. The results show that model distillation may be a powerful defense against machine learning adversaries. This method protects model resilience and performance. © 2023 IEEE.

关键词： Adversarial Attacks Deep learning Defensive Approach machine learning Model Distillation

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