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Towards an in-dept. Evaluation of the Performance, Suitability and Plausibility of Few-Shot Meta Transfer Learning on An Unknown Out-of-Distribution Cyber-attack Detection

Towards an in-Depth Evaluation of the Performance, Suitabili...

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2024 International Symposium on networks, computers and Communications, ISNCC 2024

作者： Ige, Tosin Kiekintveld, Christopher Piplai, Aritran Wagler, Amy Kolade, Olukunle Hafiz Matti, Bolanle The University of Texas Dept. of Computer Science El PasoTXUSA United States The University of Texas Dept. of Public Health Science El PasoTX United States United State Navy Office of Naval Research Pentagon United States Palo Alto Networks Inc Office of Network Security TX United States

ISBN: (纸本)9798350364910

The emergence of few-shot learning as a potential approach to address the problem of data scarcity by learning underlying pattern from a few training sample had so far given a mix-result especially on the suitability of model-agnostic meta learning, transfer learning, and optimization strategy to rapidly learn valid information from few sample. In this research, we did an in-dept. evaluation of meta- learning to determine their plausibility and suitability for previously unknown cyberattack detection by first retrieving the original research artifacts of current state of the art meta learning to repeat the experiment with original dataset before replicating the experiment with two different malware dataset which had not been previously done with meta-transfer learning. On each of the experiments, meta-transfer learning gave good results on digital character recognition dataset but abysmal result on Malimg and Malevis malware images datasets thereby indicating its unreliability for detecting cyberattacks and the need for an improvement to the state-of-the-art meta transfer learning towards a better attack detection. Transfer learning performance is independent on imbalance and hence does not influence its performance since both malware dataset used for this experiment result in high validation loss and balancing the dataset doesn't result in reduced validation loss,the successful learning transfer seen on digital character recognition dataset is not unconnected to the fact that several languages have similar characters and digits thereby enhancing the successful learning transfer unlike malware datasets, and more importantly the finding that current meta-learning transfer approach doesn't generalize well on malware dataset and hence not suitable for detecting previously unseen out-of-distribution attack. © 2024 IEEE.

关键词： Zero-shot learning

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An in-dept. Investigation Into the Performance of State-of-the-Art Zero-Shot, Single-Shot, and Few-Shot Learning Approaches on an Out-of-Distribution Zero-Day Malware Attack Detection

An in-Depth Investigation Into the Performance of State-of-t...

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2024 International Symposium on networks, computers and Communications, ISNCC 2024

作者： Ige, Tosin Kiekintveld, Christopher Piplai, Aritran Wagler, Amy Kolade, Olukunle Hafiz Matti, Bolanle The University of Texas Dept. of Computer Science El PasoTX United States The University of Texas Dept. of Public Health Science El PasoTX United States Office of Naval Research United State Navy Pentagon United States Office of Network Security Palo Alto Networks Inc TX United States

ISBN: (纸本)9798350364910

N-shot learning has emerge in recent year as poten-tial learning approach to solve the problem of data scarcity by learning underlying pattern from a few training sample. Despite recent state-of-the-art research on model-agnostic metal learning, transfer learning, and optimization strategy to rapidly learn valid information from few sample, there remains a big challenge on an actual out-of-distribution zero-day without any similarity to previously known malware family or new variant of an existing malware family. This ultimately questions the effectiveness of cur-rent state-of-the-art few-shot learning approach. In this research, we did an in-dept. investigation into the performance of state-of-the-art Zero-shot, Single-shot, and few-shot learning approaches on zero-day out-of-distribution malware attack detection based on their static properties using Malimg and Malevis mal ware dataset. We ensure our model was aware of an out-of-distribution class during training while varying the number of samples in the out-of-distribution class accordingly zero-shot(no sample), single-shot (1 sample), few-shot(S samples) while using confusion matrix to get the actual number of correct prediction on out-of-distribution mal ware validation samples. we assert that the model should be smart enough to detect and classify previously unseen data into an empty family as an out-of-distribution considering that the model was made to be aware of the existence of such distribution during training. Result shows 0, 0, and 3 correct out-of-distribution predictions on Zero-shot, single-shot, and few-shot experiments respectively, thereby showing limitation of the current state-of-the-art N-shot approaches on out-of-distribution attack. © 2024 IEEE.

关键词： Zero-shot learning

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Utilizing low-cost optical sensor for the measurement of particulate matter and calculating Pearson's correlation coefficient 16

Utilizing low-cost optical sensor for the measurement of par...

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16th International Conference on Correlation Optics, COR 2023

作者： Kirešová, Simona Rusyn, Volodymyr Guzan, Milan Vorobets, George Sobota, Branislav Vorobets, Oleksandr Dept. of Theoretical and Industrial Electrical Engineering Technical University of Košice Letná 9 Košice042 00 Slovakia Dept. of Radio Engineering and Information Security Yuriy Fedkovych Chernivtsi National University 2 Kotsjubynskyi Str. Chernivtsi58000 Ukraine Dept. of Computer Systems and Networks Yuriy Fedkovych Chernivtsi National University 2 Kotsjubynskyi Str. Chernivtsi58000 Ukraine Dept. of Computers and Informatics Technical University of Košice Letná 9 Košice042 00 Slovakia

ISBN: (纸本)9781510671829

This article discusses the possibilities of measuring Particulate Matter using optical low-cost sensors. Depending on the sensor used, not only can there be problems with the positioning of the sensor with respect to the incoming sunlight, but it may also be possible to measure ultrafine particles. While PM2.5 and PM10 are commonly used to characterize air quality, in this paper we point out the need to pay more attention to smaller particles up to 0.1 μm in size (also referred to as ultrafine particulates) due to their highly negative effects on the human health. Attention is also paid to the correlation between particulate matter and meteorological factors like pressure, temperature, and humidity. However, the sudden change in Pearson's correlation coefficient unveiled a need to look into the effect of the wind on particulate matter. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Optical sensors

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An Innovative Hashgraph-based Federated Learning Approach for Multi Domain 5G Network Protection

An Innovative Hashgraph-based Federated Learning Approach fo...

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2022 IEEE Future networks World Forum (FNWF)

作者： Hisham A. Kholidy Riaad Kamaludeen Dept. of Networks and Computer Security College of Engineering SUNY Polytechnic Institute Utica NY USA

Federated Learning (FL) is a decentralized learning approach, meaning it learns from data housed locally on devices such as tablets, cellular phones, and more, and does not collect nor transfer user-sensitive data but merely learns from the data utilizing a shared model and sending periodical updates. Using federated learning throws out the problems associated with user privacy and the high bandwidth needed to transmit resource-intensive files to a central server for training. However, FL systems may be compromised to make a wrong decision or disclose private data once the attacker modifies the FL model and/or its paraments. The main contribution of this paper includes (1) introducing a comprehensive study that explores the FL and how it applies to different domains like healthcare and medicine, Insurance and Finance, Robotics and Autonomous Systems, Virtual Reality, and 5G. (2) Develop a Hashgraph-based federated learning Approach (HFLA) to protect the 5G network against poisoning and membership inherence attacks. The HFLA was evaluated using our Federated 5G testbed and proved its superiority compared to other existing FL approaches.

关键词： Training Solid modeling Privacy 5G mobile communication Federated learning Autonomous systems Medical services

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Machine Learning for Climate Change Impact Assessment and Adaptation Planning 2

Machine Learning for Climate Change Impact Assessment and Ad...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Balakrishnan, T. Suresh Krishnan, Prabhakar Ebenezar, U. Samson Mohammed Nizarudeen, M. Kamal, N. Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Amrita Vishwa Vidyapeetham Center for Cyber Security Systems and Networks Amritapuri-Campus India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Care College of Engineering Department of Computer Science and Engineering Tamil Nadu Trichy India Grt Institute of Engineering and Technology Dept. of Computer Science and Engineering Tamil Nadu Tiruttani India

ISBN: (纸本)9798350384277

The presented research introduces a new advanced data analytics methodology for climate change impact assessment and adaptive planning that utilizes machine learning and deep-learning techniques. Quantitative analysis was performed over several databases such as GHCN, ERA5, National Centers for Environmental Prediction (NCEP) and JRA-55 in addition to CRU for a thorough assessment of the proposed methodology. In climate modeling, the Convolutional Neural networks (CNN) showed better accuracy than ERA5 and JRA-55 which outperformed Global Historical Climatology Network (GHCN) with lower Mean Absolute Error (MAE), Root Mean Squared Error values. The Early Warning System, which was built using the LSTM network algorithm, had a higher accuracy of 0.90 and recall rate of 0.88 especially with JRA-55 proving its effectiveness in early detection for extreme weather events before they occur. The RL-based adaptive planning component performed well across all datasets, with ERA5 reaching the best feedback loop performance score of 94%. The precision agriculture optimization, which was enabled by Random Forest revealed different accuracy and AUC-ROC values across various datasets showing flexibility. Against international climate agreements, the overall alignment score was 0.78 with ERA5 being slightly better at 0.80 using Support Vector Machines (SVM). These quantitative assessments accentuate the practicality, efficacy, and applicability of this methodology in various datasets making it an apt tool for evidence-based decisions on climate resilience strategies at the global level. © 2024 IEEE.

关键词： Adversarial machine learning

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Toward An Experimental Federated 6G Testbed: A Federated Leaning Approach

Toward An Experimental Federated 6G Testbed: A Federated Lea...

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ACS/IEEE International Conference on computer Systems and Applications

作者： Hisham A. Kholidy Salim Hariri Dept. of Networks and Computer Security College of Engineering SUNY Polytechnic Institute Utica NY USA Dept. of Electrical and Computer Engineering Univ. of Arizona Tucson AZ

ISBN: (纸本)9798350310092

With the development of the smart systems such as smart city, smart buildings, smart industries, the need for a highly reliable, scalable, and secure communications using high-data-rate and low latency networks such as 6G networks is increased. The artificial intelligence and machine learning (AI/ML) will be pervasive and of key relevance across the security technology stack and architecture in the 6G networks. Despite the advantages of the 6G networks, sophisticated cyberattacks can disrupt the operation of 6G critical infrastructures and associated services. In this paper, we present a novel methodology to create a federated cyber testbed as a service (FCTaaS) that can be offered as a ubiquitous cloud service. Due to the widespread usage of Machine Learning (ML) in critical decision processes of 5G/6G resource management and their applications, there is an exponential growth in cyberattacks to maliciously manipulate the ML algorithms and consequently influence their decision process in favor of the attackers. Currently, there are many isolated cyber testbeds; however, little research has focused on methods to automatically build a federated cyber testbed in general and especially in 6G testbeds. In this paper, we show how to use the FCTaaS services can be used to seamlessly compose a federated cyber testbed that allows researchers to experiment with and evaluate different algorithms to implement different algorithms to conduct data analytics, cybersecurity, and resilient algorithms. In particular, we will show how the FCTaaS can be used to develop highly efficient and accurate federated learning algorithms that can tolerate a wide range of attacks against ML algorithms such as data poisoning and ML model attacks.

关键词： 6G mobile communication Cloud computing Smart buildings Federated learning Smart cities Reliability engineering Data models

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Towards an in-dept. Evaluation of the Performance, Suitability and Plausibility of Few-Shot Meta Transfer Learning on An Unknown Out-of-Distribution Cyber-attack Detection

Towards an in-Depth Evaluation of the Performance, Suitabili...

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International Symposium on networks, computers and Communications (ISNCC)

作者： Tosin Ige Christopher Kiekintveld Aritran Piplai Amy Wagler Olukunle Kolade Bolanle Hafiz Matti Dept. of Computer Science The University of Texas at El Paso Texas Dept. of Public Health Science The University of Texas at El Paso Texas USA Office of Naval Research United State Navy Pentagon USA Office of Network Security Palo Alto Networks Inc Texas USA

ISBN: (数字)9798350364910

ISBN: (纸本)9798350364927

关键词： Metalearning Training Transfer learning Malware Data models Character recognition Reliability Few shot learning Cyberattack Optimization

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An in-dept. Investigation Into the Performance of State-of-the-Art Zero-Shot, Single-Shot, and Few-Shot Learning Approaches on an Out-of-Distribution Zero-Day Malware Attack Detection

An in-Depth Investigation Into the Performance of State-of-t...

引用

International Symposium on networks, computers and Communications (ISNCC)

作者： Tosin Ige Christopher Kiekintveld Aritran Piplai Amy Wagler Olukunle Kolade Bolanle Hafiz Matti Dept. of Computer Science The University of Texas EI Paso Texas USA Dept. of Public Health Science The University of Texas EI Paso Texas USA Office of Naval Research United State Navy Pentagon USA Office of Network Security Palo Alto Networks Inc Texas USA

ISBN: (数字)9798350364910

ISBN: (纸本)9798350364927

关键词： Training computers Heuristic algorithms Transfer learning Metals Predictive models Malware Data models Few shot learning Optimization

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Secure the 5G and Beyond networks with Zero Trust and Access Control Systems for Cloud Native Architectures

Secure the 5G and Beyond Networks with Zero Trust and Access...

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ACS/IEEE International Conference on computer Systems and Applications

作者： Hisham A. Kholidy Keven Disen Andrew Karam Elhadj Benkhelifa Mohammad A. Rahman Atta-Ur Rahman Ibrahim Almazyad Ahmed F. Sayed Rakia Jaziri Dept. of Networks and Computer Security College of Engineering SUNY Polytechnic Institute Utica NY USA Dept. of Networks and Comp. Security SUNY Polytechnic Institute Utica NY USA The Air Force Research Laboratory (AFRL RIGB) Rome NY USA Smart Systems AI and Cybersecurity Research Centre Staffordshire Univ. UK Dept of Electrical and Computer Engineering Florida International University USA Dept. of Computer Science Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia Dept. of Electrical and Computer Engineering Univ. of Arizona Tucson AZ USA Transmission Dept. Telecom Egypt Fayoum Egypt Paragraphe Research Lab. Univ. of Paris VIII France

5G networks are highly distributed, built on an open service-based architecture that requires multi-vendor hardware and software development environments, all of which create a high attack surface in the 5G networks than other proprietary fixed-function networks. Besides that, cloud-native architectures also present new security challenges. Cloud-native separates monolithic virtual machines into microservice pods, resulting in higher volumes of signaling and communication flowing through and between microservices. In addition, secure connections in monolithic applications have been replaced by untrusted communication between microservice pods, requiring additional cybersecurity capabilities. Access control systems were created to provide reliability and limit access to an organization’s assets. However, due to technology's constant evolution and dynamicity, these conventional security systems lack the security to protect an organization’s information because they were created to address access control for known users. For 5G based cloud native technology, these access controls need to be taken further by implementing a Zero Trust model to secure one’s essential assets for all users within the system. Zero Trust is implemented in an access control system under the concept "Never Trust, Always Verify". In this paper, we implement zero trust as a factor within access control systems by combining the principles of access control systems and zero-trust security by factoring in the user’s historical behavior and recommendations into the mix.

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Improved YOLOv5 Object Detection Method for Cervical Fluid Cell Pathological Image Detection

Improved YOLOv5 Object Detection Method for Cervical Fluid C...

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International Joint Conference on Neural networks (IJCNN)

作者： Qingyan Ding Xuecheng Dong Na Li Yu Pan Wan Zheng Dept. Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Cervical liquid-based cell pathology image detection and recognition is a key task in gynecological pathology aimed at early detection of cervical precancerous lesions. In this study, a deep learning based method is proposed for the detection and recognition of cervical liquid-based cell images. This paper is based on the YOLOv5 network model for improvement. Firstly, in the data preprocessing stage: for the characteristics of the dataset, this paper proposes two data enhancement methods to augment the training dataset; secondly, the Transformer module for local information enhancement is improved by introducing a local attention mechanism, which restricts each position to only pay attention to positions within a certain range around it; then, the spatial pyramid pooling module (DSPPCSPC) is improved, and the spatial Pyramid Pooling Module (DSPPC-SPC), introducing deformable mask of deformable convolution (ODConv); then, this paper proposes a new Attention Module (XCAttention), improving S2Attention to focus on the local information of the image, and introducing its Channel Attention Module into the third branch, which performs the Dot with the first and the second branches that have gone through Spatial-shift to obtain the final output. Product operation to get the final output. Through experiments, this paper’s algorithm improves in Precision, Recall, mAP50, and mAP50-95 metrics by 1.9%, 1.9%, 2%, and 1.2%, respectively. The experimental results show that this paper’s algorithm can effectively improve the detection performance of the model.

关键词： YOLO Training Deep learning Pathology Technological innovation Image recognition Convolution

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