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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41-50 订阅

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The Role of Unsupervised learning in Defending Against Adversarial Attacks

The Role of Unsupervised Learning in Defending Against Adver...

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2024 OPJU International Technology conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Anjimoon, Shaik Choudhary, Sapna Jain Anandhi, R.J. Singh, Navdeep Parmar, Ashish Abood, Baydaa Sh. Z. Institute of Aeronautical Engineering Hyderabad Dundigal India Shri Ram Group of Institutions M.P jabalpur India New Horizon College of Engineering Department of Information Science Engineering Bangalore India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India National University of Science and Technology College of Health and Medical Technology Dhi Qar64001 Iraq

ISBN: (纸本)9798350373783

The fast-changing realm of machine learning and artificial intelligence makes model susceptibility to adversarial assaults a serious concern. Adversarial assaults manipulate input data to influence machine learning model estimations. This threatens self-driving vehicles, healthcare, and cybersecurity. To solve this challenge, we investigate how unsupervised learning might prevent adversarial assaults. Our paper introduces the AutoEncoder-Based Adversarial Detector (AED), Variational Autoencoders for Adversarial Feature Extraction (VAE-AFE), and Clustering and Density-Based Hybrid defense. These strategies increase machine learning security via uncontrolled learning. Because they recreate raw data, extract strong traits, and apply clustering and density-based techniques, these methods discover and reduce adversarial hazards well. We demonstrate that these techniques outperform adversarial defensive tactics in prolonged trials. Accuracy, precision, recall, F1 Score, and ROC AUC reveal that the recommended techniques increase over time. The offered solutions offer a clear defense against dynamic threats, surpassing previous ways and securing AI applications. As artificial intelligence becomes increasingly widespread, machine learning model security and integrity are crucial. These approaches offer promise for this objective and valuable insights into adversarial defense, a burgeoning field. © 2024 IEEE.

关键词： Adversarial machine learning

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learning without Gradients: Multi-Agent Reinforcement learning approach to optimization 4

Learning without Gradients: Multi-Agent Reinforcement Learni...

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conference on artificial intelligence and machine learning in defense applications iv

作者： Morcos, Amir Man, Hong West, Aaron Maguire, Brian US Army DEVCOM AC Picatinny Arsenal NJ 07885 USA

ISBN: (纸本)9781510655560

The field of Reinforcement learning continues to show promise in solving old problems in new innovative ways. Thanks to the algorithms' ability to learn without an explicit set of labeled training data, the action, environment, reward approach has lured many researches into framing old problems in this manner. Recent publications have demonstrated how utilizing a multi-agent reinforcement learning approach can lead to a superior policy for optimization algorithm over the current standards. The challenge with the aforementioned approaches is the inclusion of the gradient in the state-space. This forces a costly calculation that is often the bottle neck in most machine learning problems, often limiting or preventing training at the edge or on the front lines. While previous works dating back decades have demonstrated the ability to train simple machine learning models without the use of gradients, none have done so using a policy which leverages previous experiences to solve the problem more quickly. This work will show how a Multi-Agent Reinforcement learning approach can be used to optimize models in training without the need for the gradient of the loss function, effectively eliminating the need for backpropagation and significantly reducing the computational power required to train a model. Furthermore, the work will examine conditions under which the agents failed to find an optimal solution. As well as how this approach can be beneficial in complex defense applications.

关键词： machine learning Data modeling Neural networks Performance modeling defense and security Nomenclature Optimization (mathematics) Statistical modeling Video compression Visualization

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Task-Oriented Synthetic-to-Real Image Translation for Data-Efficient learning 2

Task-Oriented Synthetic-to-Real Image Translation for Data-E...

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conference on Synthetic Data for artificial intelligence and machine learning - Tools, Techniques, and applications II

作者： Bernal, Edgar A. Sharma, Rohan Yenneti, Shanmukha Mackey, Ian Malave, Javier Walvoord, Derek J. Brower, Bernard FLX AI 44 Elton StSuite A Rochester NY 14607 USA Univ Buffalo 113X Davis Hall Buffalo NY 14260 USA L3Harris 332 Initiat Dr Rochester NY 14606 USA

ISBN: (纸本)9781510673892;9781510673885

Training state-of-the-art image classifiers and object detectors remains an extremely data-intensive process to this day. This is because inherently data-hungry, deep supervised networks are the traditional framework of choice. The significant data needs in turn impose strict requirements on the data acquisition, curation, and labelling stages that typically precede the learning process. This poses a particularly significant challenge for military and defense applications where the availability of high-quality labeled data is often limited. What is needed are methods that can effectively learn from sparse amounts of labeled, real-world data. In this paper, we propose a novel framework that incorporates a synthetic data generator into a supervised learning pipeline in order to enable end-to-end co-optimization of the discriminability and realism of the synthetic data, as well as the performance of the supervised engine. We demonstrate, via extensive empirical validation on image classification and object detection tasks, that the proposed framework is capable of learning from a small fraction of the real-world data required to train traditional, standalone supervised engines, while matching or even outperforming its off-the-shelf counterparts.

关键词： End-to-end supervised and unsupervised learning generative learning cross-domain translation synthetic imagery data scarcity image classification object detection learning from synthetic data

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Adversarial Attack and defense for Transductive Support Vector machine

Adversarial Attack and Defense for Transductive Support Vect...

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International Joint conference on Neural Networks (IJCNN)

作者： Liu, Li Chen, Haiyan Yin, Changchun Fang, Liming Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing Peoples R China Sci & Technol Parallel & Distributed Proc Lab PDL Nanjing Peoples R China

ISBN: (纸本)9798350359329;9798350359312

As a classic semi-supervised approach, the Transductive Support Vector machine (TSVM) has exhibited remarkable accuracy by utilizing unlabeled data. However, the robustness of TSVM against adversarial attacks remains a subject of investigation, prompting concerns about its reliability in security-critical applications. To unveil the vulnerability of TSVM, we introduce a finite-attack model specifically tailored to its characteristics, effectively manipulating its outputs. Additionally, we present Adversarial defense-based TSVM (AD-TSVM), the first dedicated defense scheme designed for TSVM. AD-TSVM incorporates adversarial information into the optimization process, enhancing robustness by rebuilding a customized loss function and decision margin to counteract attacks. Rigorous experiments conducted on benchmark datasets demonstrate the effectiveness of AD-TSVM in significantly improving both the accuracy and stability of TSVM when confronted with adversarial attacks. This pioneering research assesses the weaknesses of TSVM and, more importantly, offers valuable insights and solutions for developing secure and trustworthy TSVM systems in the face of emerging threats.

关键词： semi-supervised learning transductive support vector machine adversarial learning

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DrFuse: learning Disentangled Representation for Clinical Multi-Modal Fusion with Missing Modality and Modal Inconsistency 38

DrFuse: Learning Disentangled Representation for Clinical Mu...

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38th AAAI conference on artificial intelligence (AAAI) / 36th conference on Innovative applications of artificial intelligence / 14th Symposium on Educational Advances in artificial intelligence

作者： Yao, Wenfang Yin, Kejing Cheung, William K. Liu, Jia Qin, Jing Hong Kong Polytech Univ Sch Nursing Hong Kong Peoples R China Hong Kong Baptist Univ Dept Comp Sci Hong Kong Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China

ISBN: (纸本)1577358872

The combination of electronic health records (EHR) and medical images is crucial for clinicians in making diagnoses and forecasting prognoses. Strategically fusing these two data modalities has great potential to improve the accuracy of machine learning models in clinical prediction tasks. However, the asynchronous and complementary nature of EHR and medical images presents unique challenges. Missing modalities due to clinical and administrative factors are inevitable in practice, and the significance of each data modality varies depending on the patient and the prediction target, resulting in inconsistent predictions and suboptimal model performance. To address these challenges, we propose DrFuse to achieve effective clinical multi-modal fusion. It tackles the missing modality issue by disentangling the features shared across modalities and those unique within each modality. Furthermore, we address the modal inconsistency issue via a diseasewise attention layer that produces the patient- and diseasewise weighting for each modality to make the final prediction. We validate the proposed method using real-world large-scale datasets, MIMIC-iv and MIMIC-CXR. Experimental results show that the proposed method significantly outperforms the state-of-the-art models.

关键词： Forecasting

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Simulation Fidelity Analysis using Deep Neural Networks 2

Simulation Fidelity Analysis using Deep Neural Networks

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conference on Synthetic Data for artificial intelligence and machine learning - Tools, Techniques, and applications II

作者： Dabbiru, Lalitha Goodin, Chris Carruth, Daniel W. Aspin, Zachary Carrillo, Justin Kaniarz, John Mississippi State Univ Ctr Adv Vehicular Syst Mississippi State MS 39762 USA US Army Corps Engineers Vicksburg MS 00555 USA Ground Vehicle Syst Ctr Detroit MI USA

ISBN: (纸本)9781510673892;9781510673885

machine learning algorithms require datasets that are both massive and varied to train and generalize effectively. However, preparing real-world semantically labeled datasets is a very time-consuming and cumbersome task. Also, training with low volume datasets can lead to compromised performance and poor generalization of such algorithms. This algorithm performance and generalization gap due to limited quantities of real-world data could be decreased with the help of synthetic datasets that are generated with the consideration of real-world features. In this work, a combination of synthetic and real-world datasets is used to demonstrate and assess the performance of simulated-to-real-world transfer learning algorithms where the training is done in synthetic and testing in real-world datasets. The performance is further evaluated with a mixture of real and synthetic datasets. Two simulators are used in this work to generate synthetic images. The first was the Mississippi State University Autonomous Vehicle Simulator (MAVS), a high-fidelity physics-based simulator for Autonomous Ground Vehicle (AGV) in off-road terrain. The MAVS has been used to study machine learning in a variety of applications using both camera and lidar data. In addition to MAVS, the Unreal Engine version 4 (UE4) was used to generate images. Finally, images with a variety of synthetic scene fidelities and real-world images were considered for training the neural network to evaluate the effectiveness of low-fidelity synthetic data and the network performed very well with excellent confidence scores for object detection.

关键词： machine learning neural networks simulated datasets object detection

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An Augmented MetiTarski Dataset for Real Quantifier Elimination Using machine learning 16th

An Augmented MetiTarski Dataset for Real Quantifier Eliminat...

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16th International conference on Intelligent Computer Mathematics (CICM)

作者： Hester, John Hitaj, Briland Passmore, Grant Owre, Sam Shankar, Natarajan Yeh, Eric Imandra Inc Austin TX 78704 USA SRI Int Menlo Pk CA 94025 USA

ISBN: (纸本)9783031427527;9783031427534

We contribute a new dataset composed of more than 41K MetiTarski challenges that can be used to investigate applications of machine learning (ML) in determining efficient variable orderings in Cylindrical Algebraic Decomposition. The proposed dataset aims to address inadvertent bias issues present in prior benchmarks, paving the way to development of robust, easy-to-generalize ML models.

关键词： MetiTarski Variable Ordering machine learning Datasets Generalizability Bias

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SmartGuard: Support Vector machine(SVM)-Powered defense Mechanism for Phishing Prevention

SmartGuard: Support Vector Machine(SVM)-Powered Defense Mech...

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

作者： Rambharat, Yadav Abhishek Shelke, Dev Ritesh Khaleel, Kundlik Adnan Arunkumar, Anande Aditya Ekhande, Deepa Pillai College of Engineering Dept. Computer Engineering New Panvel India Pillai College of Engineering Dept. Information Technology New Panvel India

ISBN: (纸本)9798331517953

SmartGuard serves as a strong defense system against data breaches and financial risks from hackers by utilizing a range of machine learning algorithms, including Support Vector machine (SVM). The proposed system involves a website that accepts URLs as input from users and employs various models to predict the legitimacy of the provided URL, distinguishing between legitimate and malicious websites. Additionally, users can input messages containing links for further analysis. Nowadays, many phishing links are received by people on WhatsApp and Emails. These links pretend to be genuine but in reality, are malicious. Clicking on this link can cause data or monetary loss to an individual. The proposed system also incorporates a client-side extension, and leverages many parameters to evaluate the authenticity of a website, protecting users from visiting potentially malicious sites. The integration of AI technologies and the user friendly interface make SmartGuard a robust tool against Phishing and other cyberattacks. © 2024 IEEE.

关键词： Phishing

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Collaborative Synthesis of Patient Records through Multi -Visit Health State Inference 38

Collaborative Synthesis of Patient Records through Multi -Vi...

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38th AAAI conference on artificial intelligence (AAAI) / 36th conference on Innovative applications of artificial intelligence / 14th Symposium on Educational Advances in artificial intelligence

作者： Sun, Hongda Lin, Hongzhan Yan, Rui Renmin Univ Gaoling Sch Artificial Intelligence Beijing Peoples R China Minist Educ Engn Res Ctr Next Generat Intelligent Search & Re Beijing Peoples R China

ISBN: (纸本)1577358872

Electronic health records (EHRs) have become the foundation of machine learning applications in healthcare, while the utility of real patient records is often limited by privacy and security concerns. Synthetic EHR generation provides an additional perspective to compensate for this limitation. Most existing methods synthesize new records based on real EHR data, without consideration of different types of events in EHR data, which cannot control the event combinations in line with medical common sense. In this paper, we propose MSIC, a Multi-visit health Status Inference model for Collaborative EHR synthesis to address these limitations. First, we formulate the synthetic EHR generation process as a probabilistic graphical model and tightly connect different types of events by modeling the latent health states. Then, we derive a health state inference method tailored for the multi-visit scenario to effectively utilize previous records to synthesize current and future records. Furthermore, we propose to generate medical reports to add textual descriptions for each medical event, providing broader applications for synthesized EHR data. For generating different paragraphs in each visit, we incorporate a multi-generator deliberation framework to collaborate the message passing of multiple generators and employ a two-phase decoding strategy to generate high -quality reports. Our extensive experiments on the widely used benchmarks, MIMIC -III and MIMIC -iv, demonstrate that MSIC advances state-of-the-art results on the quality of synthetic data while maintaining low privacy risks.

关键词： Message passing

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Block Switching: Defying Fast Gradient Sign Resistance

Block Switching: Defying Fast Gradient Sign Resistance

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Africa conference (IST)

作者： Mwangi, James Kihara Kuria, Jane Wandeto, John Dedan Kimathi Univ Technol Dept Informat Technol Nyeri Kenya

ISBN: (纸本)9781905824731;9798350356595

The study sought to enhance the resilience of deep learning models against adversarial attacks, particularly those leveraging the Fast Gradient Sign Method (FGSM), which presents a significant vulnerability in the security of machine learning systems. Amidst growing concerns over the effectiveness of conventional defense mechanisms, the specific objectives were to investigate existing models' robustness, implement a novel block-switching algorithm for defense, and evaluate its performance metrics in safeguarding against adversarial threats. Employing an empirical research methodology, the study utilized a combination of widely recognized deep learning architectures (ResNet, VGG, and Inception) pre-trained on the ImageNet dataset. This involved generating adversarial examples using FGSM to assess the models' resilience and conducting a sensitivity analysis on the impact of attack parameters on model performance. The study found that traditional defense strategies often fell short when subjected to sophisticated FGSM attacks, underlining the necessity for innovative solutions like the block-switching algorithm. Further, the findings established that the block-switching algorithm significantly improved model robustness by dynamically altering model components in response to detected threats, thereby enhancing defense capabilities against a variety of adversarial attacks. The study concludes that the block-switching algorithm represents a promising advancement in the field of adversarial defense, offering a more adaptable and effective approach to securing deep learning models. It recommends further research into adaptive defense mechanisms and the exploration of block-switching algorithm applications across different deep learning architectures and attack vectors. These findings will have a profound impact on the development of more secure artificial intelligence (AI) systems, influencing future cybersecurity strategies and contributing to the establishment of resilien

关键词： Block Switching Fast Gradient Sign Method machine learning Model ImageNet

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