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An Explainable Machine Learning-Based Phishing Website Detection using Gradient Boosting

An Explainable Machine Learning-Based Phishing Website Detec...

引用

2024 International conference on Information Technology Research and Innovation, ICITRI 2024

作者： Prayogo, Rizal Dwi Alfisyahrin, Alvin Rizqi Gambetta, Windy Karimah, Siti Amatullah Nambo, Hidetaka Kanazawa University Division of Electrical Engineering and Computer Science Graduate School of Natural Science and Technology Kanazawa Japan Telkom University School of Computing Bandung Indonesia Kanazawa University Faculty of Transdisciplinary Sciences Institute of Philosophy in Interdisciplinary Sciences Kanazawa Japan Institut Teknologi Bandung School of Electrical Engineering and Informatics Bandung Indonesia

ISBN: (纸本)9798350376210

As the need for achieving optimal performance in prediction models, several recent and complex models have been developed. However, many of these models operate as black boxes, providing little insight into their predictive results. In recent years, three gradient-boosting methods based on Decision Trees have been recommended, i.e., XGBoost, CatBoost, and LightGBM. These methods have proven to deliver competitive performance with fast training times. Nevertheless, in critical domains like security, there is a growing need for increased transparency among stakeholders. One pressing concern in the security field is the proliferation of phishing websites. To address this issue, we propose an explainable machine learning-based approach using gradient-boosting methods with hyperparameter optimization on three phishing website datasets. Our best methods surpass the state-of-the-art phishing website detection methods, achieving accuracy rates of 97.45%, 99.16%, and 97.85% for the UCI (2015), Mendeley (2018), and Mendeley (2020) datasets, respectively. Subsequently, we implement posthoc explainability using SHAP and LIME for the selected dataset. The experimental results indicate that three features, i.e., length_url, directory_length, and time_domain_activation, are consistently identified as the most influential features in the dataset. Moreover, our proposed approach demonstrates promising results for detecting phishing websites with both high accuracy and explainability. © 2024 IEEE.

关键词： Phishing

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Redis automatic performance tuning based on eBPF 14

Redis automatic performance tuning based on eBPF

引用

14th International conference on Measuring Technology and Mechatronics Automation, ICMTMA 2022

作者： Yang, Junqing Chen, Lijun Bai, Jiaqing School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an710121 China

ISBN: (纸本)9781665499781

In Internet applications, cacliing lias become a key component of a high-concurrency architecture. As more and more companies use Redis as a caching database, the performance of Redis has become a particularly important part of the production environment. performance tuning has always been a challenging field. Most engineers use the experience to determine the bottleneck of the system and then achieve performance tuning. This type of method is not only extremely time-consuming but also does not have the reusability of system parameters. In response to these problems, this article establishes an automatic tuning model that uses the eBPF program to identify different Redis working scenarios and uses the random forest method to sort the memory parameters in advance in different working scenarios to filter out the high-efficiency parameters. And then realize Redis automatic performance tuning by changing the parameter values. Different from the optimization at the application level, the operating system directly manages the resources of the hardware, so that not only the performance of the server can be maximized, but also the service scenario can be more suitable. © 2022 IEEE

关键词： Reusability

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A Semantic Analysis-Based Method for Smart Contract Vulnerability 8

A Semantic Analysis-Based Method for Smart Contract Vulnerab...

引用

8th IEEE International conference on Big Data Security on Cloud, 8th IEEE International conference on high performance and Smart Computing, and 8th IEEE International conference on Intelligent Data and Security, BigDataSecurity/HPSC/IDS 2022

作者： Yan, Xingrun Wang, Shuo Gai, Keke Beijing Institute of Technology School of Computer Science and Technology Beijing100081 China Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100081 China

ISBN: (纸本)9781665480697

Smart contracts are distributed, self-executing programs on blockchain network and they have the potential to impact many industries. A large number of developers are optimistic about the development prospects of smart contracts. However, due to different levels of familiarity of developers with technology, the existence of serious vulnerabilities in code can result in huge property losses. The serious consequences of reentrancy vulnerabilities have attracted widespread attention from researchers, and researchers have proposed many approaches to detect them. Many experiments show that these approaches can only effectively detect certain types of vulnerabilities. Similar vulnerabilities still exist in some smart contracts. In this paper, we propose a semantic analysis-based detection method for reentrancy vulnerabilities. The method utilizes machine learning techniques to efficiently detect the vulnerabilities of the given code. And then, our method provides semantic analysis-based effective correction feedback of the code. Compared with the existing vulnerability detection tools Slither and Mythril, our method improves the work efficiency. Moreover, compared with the current Mythril method with the best vulnerability detection accuracy, our method improves the accuracy by 1.01%. © 2022 IEEE.

关键词： Smart contract

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Interpretable deep learning quantifies the impact of genomic variations on CD8+ T cell infiltration

Interpretable deep learning quantifies the impact of genomic...

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2024 IEEE International conference on Bioinformatics and Biomedicine, BIBM 2024

作者： He, Yi Xu, Liwen Wang, Xiang Peng, Shaoliang Hunan University College of Computer Science and Electronic Engineering Changsha China Central South University 2nd Xiangya Hospital Department of Thoracic Surgery Changsha China

ISBN: (纸本)9798350386226

Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment, but challenges remain in identifying molecular determinants of clinical response. To address this, we developed a Transformer-based model, ImmRegVar, to predict CD8+ T cell infiltration using somatic genomic variations from 7972 solid tumor samples. ImmRegVar outperforms traditional regression methods in predictive accuracy and model performance. We introduce an innovative explanatory factor that combines Transformer attention weights with feature ablation techniques, allowing precise quantification of each gene mutation's contribution. This factor offers an interpretable metric for understanding how specific mutations affect CD8+ T cell infiltration, shedding light on immune regulatory mechanisms. Validation through random permutation tests confirms its robustness and stability in high-dimensional sparse data. Our analysis also aligns with known immune regulatory genes, supporting the biological credibility of ImmRegVar. This research advances the understanding of key factors in immunotherapy and opens new pathways for exploring immune regulation and clinical applications. ImmRegVar and the explanatory factor are available at //***/Liwen-Liberty/ImmRegVar. © 2024 IEEE.

关键词： Ablation

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Evaluation of OSS-Enabled OpenRAN Compliant 5G StandAlone Campus Networks

Evaluation of OSS-Enabled OpenRAN Compliant 5G StandAlone Ca...

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2023 IEEE International conference on Electrical, computer and Energy Technologies, ICECET 2023

作者： Chepkoech, Maurine Modroiu, Elena-Ramona Mwangama, Joyce Corici, Marius Magedanz, Thomas University of Cape Town Electrical Engineering Cape Town South Africa Technische Universität Berlin Electrical Engineering and Computer Sciences Berlin Germany Fraunhofer Fokus Institute Technische Universität Berlin Electrical Engineering and Computer Sciences Berlin Germany

ISBN: (纸本)9798350327816

Open source software (OSS) implementations of 5G campus networks are essential for facilitating researchers to evaluate and validate use cases and applications using customized 5G network configurations. These configurations provide specific performance, such as guaranteed bandwidth, low latency, high reliability, and increased flexibility. To realize a campus network that is both adaptable and cost-effective, it is imperative to overcome the limitations imposed by vendor lock-ins. This objective can be accomplished through the adoption of open network architectures and software-defined networking concepts, which enable the realization of 5G networks using non-proprietary and programmable hardware. Furthermore, the utilization of accessible OSS helps reduce licensing costs while expanding the possibilities for customization. Thus, this research paper presents a modular, cost-efficient, future-proof, and flexible 5G standalone campus network deployed at the Technische Universität Berlin, designed for research and validation of candidate use cases in the drive towards Open Campus Networks. The paper presents a qualitative evaluation of the different OSS RAN implementations, as well as a comparative analysis of performance achieved by different combinations and interworking of radio access and core network software frameworks. © 2023 IEEE.

关键词： 5G 6G Campus network testbed Open-source OpenRAN

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ACPNet: Enhancing Small-Scale Dieases Detection in Panoramic X-rays

ACPNet: Enhancing Small-Scale Dieases Detection in Panoramic...

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2024 IEEE International conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Gao, Nan Zhu, Junchao Chen, Peng Tang, Jijun Jiang, Feng Liang, Ronghua Zhejiang University of Technology College of Computer Science and Technology Hangzhou China Chinese Academy of Sciences Shenzhen Institute of Advanced Technology Shenzhen China Zhejiang Cancer Hospital Department of Radiotherapy Hangzhou China

ISBN: (纸本)9798350386226

Deep learning-based disease detection can automatically identify dental diseases in panoramic X-rays and improve the accuracy and efficiency of doctors' diagnoses. However, due to the complex data distribution of panoramic oral X-rays, significant scale differences among lesions, and the presence of many small-scale diseases, automated disease detection in panoramic oral X-rays faces considerable challenges. To alleviate the aforementioned issues, we propose ACPNet, which introduces a novel two-stage approach for detecting small-scale dental diseases in panoramic X-rays using the Contextual Attention Alignment Network (CAAN) and the Point-to-Patch Module (PTPM). To the best of our knowledge, we are the first to explore the detection of small-scale dental diseases in panoramic X-rays under limited sample conditions. Specifically, CAAN integrates deformable convolution with the global attention mechanism of transformer attention, enabling the model to more accurately extract small target foreground features in the complex background of panoramic X-rays. PTPM employs key point detection and cascade dynamic patches to adjust the bounding boxes of lesions, ensuring that small-scale diseases have sufficient high-quality proposals, thereby enhancing detector performance. Additionally, we collected a dataset containing 1157 instances of dental diseases to validate the effectiveness of our algorithm. Extensive experiments demonstrate that ACPNet achieves state-of-the-art performance, highlighting its superiority over baseline and other detection methods. © 2024 IEEE.

关键词： Deep learning

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Bridging the Gap: Generalising State-of-the-Art U-Net Models to Sub-Saharan African Populations 1

引用

Challenge on Brain Tumor Segmentation, BraTS 2023, International Challenge on Cross-Modality Domain Adaptation for Medical Image Segmentation, CrossMoDA 2023, held in conjunction with the Medical Image Computing for computer Assisted Intervention conference, MICCAI 2023

作者： Amod, Alyssa R. Smith, Alexandra Joubert, Pearly Raymond, Confidence Zhang, Dong Anazodo, Udunna C. Motchon, Dodzi Mutsvangwa, Tinashe E. M. Quetin, Sébastien Brain Behaviour Unit Neuroscience Institute University of Cape Town Cape Town South Africa ShockLab Department of Mathematics and Applied Mathematics University of Cape Town Cape Town South Africa Applied Mathematics Division Stellenbosch University Stellenbosch South Africa Division of Biomedical Engineering Neuroscience Institute University of Cape Town Cape Town South Africa Medical Artificial Intelligence Lab Lagos Nigeria Lawson Health Research Institute LondonON Canada Department of Electrical and Computer Engineering University of British Columbia Vancouver Canada Montreal Neurological Institute McGill University Montréal Canada Department of Medicine and Department of Clinical and Radiation Oncology University of Cape Town Cape Town South Africa Medical Physics Unit Department of Oncology McGill University Montréal Canada Montreal Institute for Learning Algorithms Montréal Canada

ISBN: (数字)9783031761638

ISBN: (纸本)9783031761621

A critical challenge for tumour segmentation models is the ability to adapt to diverse clinical settings, particularly when applied to poor quality neuroimaging data. The uncertainty surrounding this adaptation stems from the lack of representative datasets, leaving top-performing models without exposure to common artefacts found in MRI data throughout Sub-Saharan Africa (SSA). We replicated a framework that secured the 2nd position in the 2022 BraTS competition to investigate the impact of dataset composition on model performance and pursued four distinct approaches through training a model with various combinations of the BraTS-Africa and BraTS-Adult Glioma datasets. Notably, training on the smaller low-quality BraTS-Africa dataset alone yielded subpar results, and training on the larger high-quality BraTS-Adult Glioma dataset alone struggled to delineate oedematous tissue in the low-quality validation set. The most promising approach involved pre-training a model on high-quality neuroimages and then fine-tuning it on the smaller low-quality dataset. This approach took second place in the MICCAI BraTS Africa global challenge external testing phase. These findings underscore the significance of larger sample sizes and broad exposure to data in improving segmentation performance. Furthermore, we demonstrated there is potential for improving such models by fine-tuning them with a wider range of data locally. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Magnetic resonance imaging

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Speed up Object Detection on Gigapixel-level Images with Patch Arrangement

Speed up Object Detection on Gigapixel-level Images with Pat...

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IEEE/CVF conference on computer Vision and Pattern Recognition (CVPR)

作者： Fan, Jiahao Liu, Huabin Yang, Wenjie See, John Zhang, Aixin Lin, Weiyao Shanghai Jiao Tong Univ Shanghai Peoples R China Heriot Watt Univ Putrajaya Malaysia

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

With the appearance of super high-resolution (e.g., gigapixel-level) images, performing efficient object detection on such images becomes an important issue. Most existing works for efficient object detection on high-resolution images focus on generating local patches where objects may exist, and then every patch is detected independently. However, when the image resolution reaches gigapixel-level, they will suffer from a huge time cost for detecting numerous patches. Different from them, we devise a novel patch arrangement framework for fast object detection on gigapixel-level images. Under this framework, a Patch Arrangement Network (PAN) is proposed to accelerate the detection by determining which patches could be packed together into a compact canvas. Specifically, PAN consists of (1) a Patch Filter Module (PFM) (2) a Patch Packing Module (PPM). PFM filters patch candidates by learning to select patches between two granularities. Subsequently, from the remaining patches, PPM determines how to pack these patches together into a smaller number of canvases. Meanwhile, it generates an ideal layout of patches on canvas. These canvases are fed to the detector to get final results. Experiments show that our method could improve the inference speed on gigapixel-level images by 5x while maintaining great performance.

关键词： computer vision Image resolution Image recognition Costs Layout Object detection Reinforcement learning

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Sharing-Aware Data Mapping in Software Transactional Memory 21st

Sharing-Aware Data Mapping in Software Transactional Memory

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21st International conference on Embedded computer Systems - Architectures, Modeling, and Simulation (SAMOS)

作者： Pasqualin, Douglas Pereira Diener, Matthias Du Bois, Andre Rauber Pilla, Mauricio Lima Univ Fed Pelotas Comp Sci Grad Program PPGC Pelotas RS Brazil Univ Illinois Urbana IL 61801 USA

ISBN: (纸本)9783031045806;9783031045790

Software transactional memory (STM) is an abstraction used for thread synchronization that borrows the concept of transactions from databases. It is often easier to use than locks, proving a high-level abstraction for software developers. In current multicore architectures, data locality is an important aspect of STM performance. Sharing-aware mapping is a technique that aims to improve the performance of applications by mapping threads and data (in the form of memory pages) according to their memory access behavior. In prior work, we successfully used information gained from tracking STM variables and STM operations to perform an effective sharing-aware thread mapping. In this paper, we attempt to extend such a mechanism to perform data mapping. Although initial results using a synthetic application were encouraging, data mapping did not improve performance when using realistic workloads. Contrary to thread mapping, where only keeping track of STM operations is sufficient to perform an effective thread mapping, data mapping requires a global vision of memory page accesses of the application to be able to improve the performance, which STM runtimes can not provide.

关键词： Software transactional memory Data mapping Sharing-aware NUMA

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A Study on Consumer-Grade EEG Headsets in BCI Applications 22

A Study on Consumer-Grade EEG Headsets in BCI Applications

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22nd IEEE conference on Pervasive and Intelligent Computing

作者： Rizzo, Luigi Zicari, Paolo Cicirelli, Franco Guerrieri, Antonio Micieli, Massimo Vinci, Andrea Natl Res Council Italy ICAR CNR Inst High Performance Comp & Networking Arcavacata Di Rende CS Italy

ISBN: (纸本)9798331522759;9798331522742

This paper emphasizes the importance of wearable electroencephalogram (EEG) headsets with electrodes and their potential applications, particularly in the specialization of Braincomputer Interfaces (BCIs). We aim to contribute to the field of BCIs by providing a short overview of recent literature to identify some usage scenarios for consumer-grade EEG headsets (non-medical editions). The paper highlights how each scenario requires a specific set of appropriately positioned electrodes. Moreover, it will show different BCI settings that comprehend signal preprocessing of brain cerebral waves, feature extraction, control signals, and classification algorithms. The paper will also discuss several key insights into the practical use of consumer-grade low-cost devices. The proposed study aims to be a helpful starting point for researchers interested in working on consumer-grade EEG headsets for BCI by providing an understanding of the current state-of-the-art limits and challenges and giving hints about what setting are suitable for specific application scenarios.

关键词： Brain-computer Interface Consumer-Grade EEG Headsets Wearables EEG Electrode Placement Machine Learning Comparative Study

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