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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131-140 订阅

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Certified Robustness of Nearest Neighbors against Data Poisoning and Backdoor Attacks 36

Certified Robustness of Nearest Neighbors against Data Poiso...

引用

36th AAAI conference on artificial intelligence / 34th conference on Innovative applications of artificial intelligence / 12th Symposium on Educational Advances in artificial intelligence

作者： Jia, Jinyuan Liu, Yupei Cao, Xiaoyu Gong, Neil Zhenqiang Duke Univ Durham NC 27706 USA

ISBN: (纸本)9781577358763

Data poisoning attacks and backdoor attacks aim to corrupt a machine learning classifier via modifying, adding, and/or removing some carefully selected training examples, such that the corrupted classifier makes incorrect predictions as the attacker desires. The key idea of state-of-the-art certified defenses against data poisoning attacks and backdoor attacks is to create a majority vote mechanism to predict the label of a testing example. Moreover, each voter is a base classifier trained on a subset of the training dataset. Classical simple learning algorithms such as k nearest neighbors (kNN) and radius nearest neighbors (rNN) have intrinsic majority vote mechanisms. In this work, we show that the intrinsic majority vote mechanisms in kNN and rNN already provide certified robustness guarantees against data poisoning attacks and backdoor attacks. Moreover, our evaluation results on MNIST and CIFAR-10 show that the intrinsic certified robustness guarantees of kNN and rNN outperform those provided by state-of-the-art certified defenses. Our results serve as standard baselines for future certified defenses against data poisoning attacks and backdoor attacks.

关键词： learning algorithms

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C2 Design Considerations for Federated AI/ML Systems 4

C2 Design Considerations for Federated AI/ML Systems

引用

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

作者： Michaelis, James Morelli, Alessandro Ali, Muddasar US Army DEVCOM Res Lab 2800 Powder Mill Rd Adelphi MD 20783 USA Florida Inst Human & Machine Cognit 40 S Alcaniz St Pensacola FL 32502 USA

ISBN: (纸本)9781510651036;9781510651029

Building on growth in the commercial IoT space, the Internet of Battlefield Things (IoBT) paradigm envisions mixed application of commercial and military IoT technologies to support Multi-Domain Operations via next-generation artificial intelligence and machine learning (AI/ML) applications. Operational environments for IoBT are expected to feature significant data volume and variety to support mission needs at varying echelons, while simultaneously requiring coordinated asset management and AI/ML usage across distributed coalitions. These conditions motivate the design and implementation of next-generation infrastructures intended to facilitate: (1) Data exchange between systems according to broader mission needs;(2) Coordinated AI/ML processing across systems;(3) Intelligent prioritization of cross-infrastructure processing and dissemination. Towards achieving these aims, this work looks into the design and prototyping of an AI/ML pipeline, aimed at: (1) Ingest and processing of video feeds;(2) Prioritization of video feed object detections based on Value of Information (VoI) assessment;(3) Support for information exchange with external systems. This submission will cover implementation details of the AI/ML pipeline, a reference usage scenario for IoT data processing and information exchange, and planned future work.

关键词： artificial intelligence/machine learning (AI/ML) Value of Information (VoI) Federated Infrastructures

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SpreadGNN: Decentralized Multi-Task Federated learning for Graph Neural Networks on Molecular Data 36

SpreadGNN: Decentralized Multi-Task Federated Learning for G...

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36th AAAI conference on artificial intelligence / 34th conference on Innovative applications of artificial intelligence / 12th Symposium on Educational Advances in artificial intelligence

作者： He, Chaoyang Ceyani, Emir Balasubramanian, Keshav Annavaram, Murali Avestimehr, Salman Univ Southern Calif Los Angeles CA 90089 USA

ISBN: (纸本)9781577358763

Graph Neural Networks (GNNs) are the first choice methods for graph machine learning problems thanks to their ability to learn state-of-the-art level representations from graph-structured data. However, centralizing a massive amount of real-world graph data for GNN training is prohibitive due to user-side privacy concerns, regulation restrictions, and commercial competition. Federated learning is the de-facto standard for collaborative training of machine learning models over many distributed edge devices without the need for centralization. Nevertheless, training graph neural networks in a federated setting is vaguely defined and brings statistical and systems challenges. This work proposes SpreadGNN, a novel multi-task federated training framework capable of operating in the presence of partial labels and the absence of a central server for GNNs over molecular graphs. We provide convergence guarantees and empirically demonstrate the efficacy of our framework on a variety of non-I.I.D. distributed graph-level molecular property prediction datasets with partial labels. Our results show that SpreadGNN outperforms GNN models trained over a central server-dependent federated learning system, even in constrained topologies.

关键词： Graph neural networks

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Adversarial Attacks and defenses in Deep learning

Adversarial Attacks and Defenses in Deep Learning

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2024 International conference on Emerging Innovations and Advanced Computing, INNOCOMP 2024

作者： Kashyap, Swati Sharma, Akshay Gautam, Savit Sharma, Rishabh Chauhan, Sneha Simran Chandigarh University Department of Computer Science Mohali India

ISBN: (纸本)9798350376470

Deep learning, a cornerstone of artificial intelligence (AI), has revolutionized a number of fields, including self-driving cars, image recognition, and intelligent medical applications. These developments have aided in the development of smart cities. That being said, new findings have shown that deep neural networks are susceptible to hostile attacks on security-critical tasks. This research paper investigates the related defensive strategies and explores the crucial problem of adversarial intrusions in deep learning. It draws attention to the adversarial attacks that deep neural networks are susceptible to, especially in high-security applications like network intrusion detection systems and radiofrequency identification. This paper presents the idea of adversarial intrusions, looks at their theoretical underpinnings, and evaluates how they affect different kinds of data, such as text, graphs, and images. The field of cybersecurity is given special attention, with attacks being categorized using adversarial examples and defensive measures being discussed. Additionally, the study introduces FIAT, a novel adversarial defense strategy based on feature-level interpretability. This paper intends to stimulate future research endeavors to guarantee the security and resilience of deep learning algorithms in real-world scenarios by offering a thorough overview of adversarial intrusions and defenses in the field of deep learning for Edge AI. © 2024 IEEE.

关键词： Adversarial machine learning

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MoReco: AI Model Recommendation and Optimization for B5G Networks 4th

MoReco: AI Model Recommendation and Optimization for B5G Net...

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4th International conference on Advanced Network Technologies and Intelligent Computing-ANTIC-Annual

作者： Jaisawal, Sandeep Kumar Chawla, Ditya Mittal, Prerna Roopalakshmi, R. Paul, Ankita Singh, Sukhdeep Samsung R&D Inst India Bangalore Bangalore Karnataka India Manipal Acad Higher Educ Dept CSE Manipal Inst Technol Manipal 576104 Karnataka India

ISBN: (纸本)9783031837951;9783031837968

The deployment of artificial intelligence and machine learning (AIML) models in 5G networks has become increasingly critical for optimizing network performance, particularly in applications such as Self-Organizing Networks (SON) and Open Radio Access Networks (O-RAN). However, the dynamic nature of cloud-based network infrastructures presents significant challenges, as changes in underlying resources can adversely affect the inference time and prediction accuracy of these models. This paper introduces MoReco, a novel framework designed to predict and optimize the performance of AIML models in continuously changing deployment environments. MoReco features a "trade-off analyzer" that selects the most suitable ML algorithm and hyperparameters, ensuring that the trained model meets predefined thresholds for both inference time and accuracy. By maintaining a comprehensive record of previous iterations and dynamically tuning models in response to network changes, MoReco eliminates the need for repeated retraining and redeployment. The system also includes a predictive mechanism that estimates model performance without actual deployment, significantly improving the efficiency of the model deployment process. The proposed framework is evaluated within the context of 5G networks, demonstrating its potential to enhance reliability and operational efficiency in intelligent network management. The paper concludes by discussing future work aimed at expanding MoReco's capabilities and exploring its application in other domains such as IoT and smart cities.

关键词： 5G Networks artificial intelligence (AI) machine learning (ML) Self-Organizing Networks (SON) Open Radio Access Network (O-RAN) AIML Model Optimization

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Design of an Improved Model for Data Poisoning Detection Using AEAD-TL, GARNN, and FL-DPD

Design of an Improved Model for Data Poisoning Detection Usi...

引用

2024 International conference on artificial intelligence and Quantum Computation-Based Sensor applications, ICAIQSA 2024

作者： Hatwar, Nitesh L. Sharma, V.K. Manjre, Bhushan M. Bhagwant University Computer Science and Engineering Rajasthan Ajmer India G H Raisoni College of Engineering and Management Information Technology Maharashtra Nagpur India

ISBN: (纸本)9798331517953

With the increased vulnerabilities of adversaries with respect to training data concerning machine learning models, the detection and prevention of data poisoning attacks in AI systems are now critical with strong mechanisms. Detect ion methods that have remained behind so far include fixed models for detection purposes but are not adaptable. This work addresses these limitations by providing novel and efficient detection and mitigation techniques against sophisticated poisoning attacks on various AI systems, with guarantees of accuracy and resilience under attack. We introduce five advanced models tailored to effectively counteract a wide range of poisoning attack types. First, the Adaptive Ensemble Anomaly Detection with Transfer learning (AEAD-TL) enhances the detection process's accuracy through transfer learning of the ensemble of models for adapting evolving attack patterns. Those include Graph-Attack-Resistant Neural Networks (GARNN), which uses graph neural networks to detect attacks on the relationships of data in order to provide resistance to graph-based perturbations, followed by Autoencoder-Based Poisoning Gradient Monitoring (APGM), which monitors deviations in gradients during training in order to identify poisoned data points that would highly improve the detection accuracy with minimal overhead in terms of computations, and lastly, Federated learning with Distributed Poisoning defense (FL-DPD), which strengthens federated learning models by defending poisoned client updates using a mix of local and global anomaly detection mechanisms. The final model is the Resilient Hybrid Model with Transferable Outlier Detection, RHMT-OD, which integrates supervised learning and unsupervised learning to detect known as well as novel attacks, without requiring full retraining, while transferring learned patterns across datasets. Overall, these methods enhance the detection precision and reject false positives, while expediting the training procedure, thus ultimate

关键词： Federated learning

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Network security threat detection algorithm based on machine learning

Network security threat detection algorithm based on machine...

引用

2024 International conference on Signal Processing and Communication Security, ICSPCS 2024

作者： Ren, Yong School of Artificial Intelligence Wuhan Polytechnic University Wuhan430074 China

ISBN: (纸本)9781510681699

Network security threats are increasingly becoming the focus of information system security, and an efficient and intelligent detection mechanism is urgently needed to deal with the evolving attack methods in time. This paper is devoted to exploring and evaluating network security threat detection algorithms based on machine learning. Through the empirical analysis of typical data sets such as KDD99, the performance of different algorithms in real network environment is compared. We have focused on algorithms such as Support Vector machine (SVM) and Long Short Term Memory (LSTM) in deep learning, and attempted to explore their hybrid applications. Through comparative experiments, it was found that the LSTM+SVM hybrid model can effectively control the false alarm rate while improving accuracy. The experimental results on KDD99 data set show that our model has significantly improved the accuracy of many attack types, especially in the scene where the "U2R" attack type is "normal", the false alarm rate is about 9%. Through this study, I hope to provide valuable reference for the research and practical application in the field of network security threat detection, and contribute to building a smarter and more robust network security defense system. © 2024 SPIE.

关键词： Support vector machines

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Evaluating Explainable artificial intelligence (XAI): Algorithmic Explanations for Transparency and Trustworthiness of ML Algorithms and AI Systems 4

Evaluating Explainable Artificial Intelligence (XAI): Algori...

引用

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

作者： Khakurel, Utsab Rawat, Danda B. Howard Univ Dept Elect Engn & Comp Sci Washington DC 20059 USA

ISBN: (纸本)9781510651036;9781510651029

Explainable artificial intelligence (XAI) is the capability of explaining the reasoning behind the choices made by the machine learning (ML) algorithm which can help understand and maintain the transparency of the decision-making capability of the ML algorithm. Humans make thousands of decisions every day in their lives. Every decision an individual makes, they can explain the reasons behind why they made the choices that they made. Nonetheless, it is not the same in the case of ML and AI systems. Furthermore, XAI was not wideley researched until suddenly the topic was brought forward and has been one of the most relevant topics in AI for trustworthy and transparent outcomes. XAI tries to provide maximum transparency to a ML algorithm by answering questions about how models effectively came up with the output. ML models with XAI will have the ability to explain the rationale behind the results, understand the weaknesses and strengths the learning models, and be able to see how the models will behave in the future. In this paper, we investigate XAI for algorithmic trustworthiness and transparency. We evaluate XAI using some example use cases and by using SHAP (SHapley Additive exPlanations) library and visualizing the effect of features individually and cumulatively in the prediction process.

关键词： Explainable artificial intelligence XAI Transparency Trustworthy AI Equitable AI AI Ethics

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Enhancing Adversarial Robustness in Automatic Modulation Recognition with Dynamical Systems-Inspired Deep learning Frameworks 18th

Enhancing Adversarial Robustness in Automatic Modulation Rec...

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18th International conference on Wireless artificial Intelligent Computing Systems and applications, WASA 2024

作者： Li, Xiaohu Zhou, Yajian Yan, Hongchao Beijing University of Posts and Telecommunications Beijing100876 China The 54th Research Institute of China Electronics Technology Group Corporation HeBei050050 China

ISBN: (纸本)9783031714634

This study introduces a novel deep learning framework aimed at enhancing the defensive capabilities of Automatic Modulation Recognition (AMR) systems against adversarial attacks through the application of dynamical systems theory and an adaptive weight learning mechanism. Utilizing dynamical systems theory, we analyze and simulate the propagation process of adversarial perturbations, revealing the mechanisms behind various types of attacks, thereby identifying and reinforcing the model's intrinsic resistance to disturbances. At the same time, we introduce an adaptive weight learning mechanism capable of assessing and adjusting the weights of different features, optimizing the feature fusion process to improve the model's accuracy and robustness when facing unknown adversarial samples. Through testing on standard AMR datasets, we have validated that our proposed framework significantly enhances the model's performance under both normal and adversarial conditions. The contributions of this paper lie not only in improving the adversarial defense capabilities of AMR systems but also in providing a new methodology for designing deep learning models using dynamical systems theory and adaptive learning mechanism. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Assured AI reference architecture

Assured AI reference architecture

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conference on Assurance and Security for AI-Enabled Systems

作者： Tyler, Marcus McCeney, James Mitre Corp Mclean VA 22102 USA

ISBN: (纸本)9781510674271;9781510674264

The Cross-Industry Standard Process for the development of machine learning applications with Quality assurance (CRISP-ML(Q)) framework describes the full AI model lifecycle from data sourcing to deployment, along with risk mitigation procedures for each phase of development. In this work we implement an example model pipeline that utilizes CRISP-ML(Q), along with mitigations for risks documented in MITRE ATLAS and the National Institute of Standards and Technology artificial intelligence Risk Management Framework (NIST AI RMF) and its associated playbook, with the goal of creating a generalizable reference framework for model developers and decisionmakers. This work builds on our previous effort in cross-referencing CRISP-ML(Q) with published ML risk frameworks by developing a working and reproducible pipeline for image classification that validates our risk mitigation approach. In this pipeline, we implement multiple risk mitigations and demonstrate their effectiveness in the areas of drift detection, spam mitigation, and defense against adversarial attacks, along with demonstrations of model explainability to improve model trustworthiness. We present results from a variety of A/B mitigation tests that show the effectiveness of our mitigations, and we demonstrate full pipeline runs for Convolutional Neural Network (CNN) and Resnet18 model architectures that utilize our risk mitigation process.

关键词： machine learning AI Security Model Lifecycle Risk Mitigation AI Assurance MLSecOps

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