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Offensive Security: Study on Penetration Testing Attacks, Methods, and their Types

Offensive Security: Study on Penetration Testing Attacks, Me...

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2022 IEEE International conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2022

作者： Aibekova, Altynai Selvarajah, Vinesha Asia Pacific University of Technology and Innovation Technology Park Malaysia

ISBN: (纸本)9781665483162

In the era of increasing growth of digitalization all information has become easily available through mobile and computer devices. This improvement has brought many useful and efficient technologies and services to peoples' lives which are Web Application, Cloud computing, Online Communication platforms, E-Commerce, and far more. While some users access this information with only good intentions, some use it to identify ways to destroy or steal valuable data, documents of a website, or in a physical building. As it is well known the term penetration testing is also named ethical hacking. Penetration testing is a set of procedures that emulates the actions of potential hackers but instead of taking advantage of the breaches found, penetration testers document them and present practical advice on how to fix identified weaknesses in the system. This study aims to discuss the types, the strategies of penetration testing, the code of conduct of penetration testers, the advantages, and the methodology in performing penetration testing. The method of penetration testing includes six stages that will be further discussed in detail. This paper illustrates a practical exercise in the example of the Five86-1 machine in a CTF format and that is built with the purpose of learning and gaining experience in conducting ethical hacking. All the attacks demonstrated in the paper are done in Kali Linux operating system. Further, the impact and critical analysis of the attacks is covered in the paper. © 2022 IEEE.

关键词： Personal computing

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17th International conference on Future Networks and Communications / 19th International conference on Mobile Systems and Pervasive computing / 12th International conference on Sustainable Energy Information Technology, FNC/MobiSPC/SEIT 2022

17th International Conference on Future Networks and Communi...

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17th International conference on Future Networks and Communications / 19th International conference on Mobile Systems and Pervasive computing / 12th International conference on Sustainable Energy Information Technology, FNC/MobiSPC/SEIT 2022

The proceedings contain 123 papers. The topics discussed include: automated machine learning based elderly fall detection classification;security concerns for 5G/6G mobile network technology and quantum communication;enhancement of the TSCH-Sim simulator to support manual scheduling and routing;multidimensional computer interconnection networks with the virtual cut–through routing;cocoa companion: deep learning-based smartphone application for cocoa disease detection;a blockchain-based platform for data management and sharing;new and efficient lightweight cryptography algorithm for mobile and web applications;contextual information aware optimal communication in radio networks in considering the pervasive computing -a literature review;blockchain-based real-time information management in Remicon manufacturing process;shedding strategies for optimizing join processing in spatial data streams;live sentiment analysis using multiple machine learning and text processing algorithms;and designing a tool to address the depression of children during online education.

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International conference on computing, Intelligence and Data Analytics, ICCIDA 2022

International Conference on Computing, Intelligence and Data...

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International conference on computing, Intelligence and Data Analytics, ICCIDA 2022

ISBN: (纸本)9783031270987

The proceedings contain 44 papers. The special focus in this conference is on computing, Intelligence and Data Analytics. The topics include: Preface;Priority-Based Pollution Management ITS;security Issues for Banking Systems;smart Autonomous Bike Hardware Safety Metrics;demand Side Management Based Cost-Centric Solution for a Grid Connected Microgrid System with Pico-Hydro Storage System;impact of Multimodal Model Complexity on Classification of Diabetic Retinopathy Level;secure Image Data Encryption Scheme for 5G Internet of Things Applications;Symptom Based Health Status Prediction via Decision Tree, KNN, XGBoost, LDA, SVM, and Random Forest;Multiband Microstrip Elliptical Monopole Antenna Design with Mushroom-Like Loadings for DCS, 5G and Ku-Band Applications;an IoT-Based Modular Avionics and Electrical System for Nanosatellite Systems;reinforcement learning for Intrusion Detection;comparison of Small-Sized Deep Neural Network Models for Hyperspectral Image Classification;autonomous Mobile Robot Mapping and Exploration of an Indoor Environment;time-Sensitive Embedding for Understanding Customer Navigational Behavior in Mobile Banking;do Lunar Cycles Affect Bitcoin Prices?;Stock Price Prediction in Response to US Dollar Exchange Rate Using machine learning Techniques;A Service of RSU Communication in Internet of Vehicles (IoV) in Urban Environment;a Comparative Study on Subdural Brain Hemorrhage Segmentation;deep learning Based Spectrum Sensing Method for Cognitive Radio System;Colour Image Encryption Based on HACS and Knuth-Durstenfeld Algorithm;Turkish Sign Language Recognition by Using Wearable MYO Armband;effects of Different Parameters on Sleep Quality;predictive Maintenance on Industrial Data Using Soft Voted Ensemble Classifiers;Medical Image Transmission Using a Secure Cryptographic Approach for IoMT Applications;volume Forecasting in Supply Chain: A Mixed Study of Boosting and Prophet algorithms;blockchain-Based System for Traceability of Counterfeit Med

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RECURSIVE DISENTANGLEMENT NETWORK 10

RECURSIVE DISENTANGLEMENT NETWORK

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10th International conference on learning Representations, ICLR 2022

作者： Chen, Yixuan Shi, Yubin Li, Dongsheng Wang, Yujiang Dong, Mingzhi Zhao, Yingying Dick, Robert Lv, Qin Yang, Fan Shang, Li China and Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai China Microsoft Research Asia Shanghai China Department of Computing Imperial College London London United Kingdom Department of Electrical Engineering and Computer Science University of Michigan Michigan United States Department of Computer Science University of Colorado Boulder Boulder United States School of Microelectronics Fudan University Shanghai China

Disentangled feature representation is essential for data-efficient learning. The feature space of deep models is inherently compositional. Existing β-VAE-based methods, which only apply disentanglement regularization to the resulting embedding space of deep models, cannot effectively regularize such compositional feature space, resulting in unsatisfactory disentangled results. In this paper, we formulate the compositional disentanglement learning problem from an information-theoretic perspective and propose a recursive disentanglement network (RecurD) that propagates regulatory inductive bias recursively across the compositional feature space during disentangled representation learning. Experimental studies demonstrate that RecurD outperforms β-VAE and several of its state-of-the-art variants on disentangled representation learning and enables more data-efficient downstream machine learning tasks. © 2022 ICLR 2022 - 10th International conference on learning Representationss. All rights reserved.

关键词： Information theory

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International conference on Information Security, Privacy, and Digital Forensics, 2022

International Conference on Information Security, Privacy, a...

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International conference on Information Security, Privacy, and Digital Forensics, 2022

ISBN: (纸本)9789819950904

The proceedings contain 29 papers. The special focus in this conference is on Information Security, Privacy, and Digital Forensics. The topics include: Lightweight Malicious Packet Classifier for IoT Networks;cyber Security Issues and Challenges on Non-fungible Tokens;the Rise of Public Wi-Fi and Threats;digital Forensic Investigation on Ponzi Schemes;holistic Cyber Threat Hunting Using Network Traffic Intrusion Detection Analysis for Ransomware Attacks;Cyber Security Attack Detection Framework for DODAG Control Message Flooding in an IoT Network;Application of Digital Forensic Evidence in Hit and Run: A Comparative Study with Special Reference to § 304 Part II of IPC;a Forensic Video Upscaling Colorizing and Denoising Framework for Crime Scene Investigation;a Review of Face Detection Anti Spoofing Techniques on Varied Data Sets;re-examining Laws Pertaining to Admissibility of Digital Evidence in Investigations;ethereum Blockchain-Based Medicine Supply Chain;machine learning algorithms for Attack and Anomaly Detection in IoT;a Mini Review on—Physically Unclonable Functions: The Hardware Security Primitives;an Intelligent Analysis of Mobile Evidence Using Sentimental Analysis;Forensics Analysis of TOR Browser;phishing Classification Based on Text Content of an Email Body Using Transformers;vehicle Theft Detection and Tracking Using Surveillance Video for the Modern Traffic Security Management System;Resilient Risk-Based Adaptive Authentication and Authorization (RAD-AA) Framework;survey on Blockchain Scalability Addressing Techniques;anti-money Laundering Analytics on the Bitcoin Transactions;fog Forensics: A Comprehensive Review of Forensic Models for Fog computing Environment;memory Forensics for Artefacts Recovery from Ether Transactions;a Lightweight Intrusion Detection and Electricity Theft Detection System for Smart Grid.

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learning to Limit Data Collection via Scaling Laws: A Computational Interpretation for the Legal Principle of Data Minimization 22

Learning to Limit Data Collection via Scaling Laws: A Comput...

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5th ACM conference on Fairness, Accountability, and Transparency, FAccT 2022

作者： Shanmugam, Divya Diaz, Fernando Shabanian, Samira Finck, Michele Biega, asia Massachusetts Institute of Technology CambridgeMA United States Microsoft Research Montreal Canada University of Tuebigen Germany Max Planck Institute for Security and Privacy Bochum Germany

ISBN: (纸本)9781450393522

Modern machine learning systems are increasingly characterized by extensive personal data collection, despite the diminishing returns and increasing societal costs of such practices. Yet, data minimisation is one of the core data protection principles enshrined in the European Union's General Data Protection Regulation ('GDPR') and requires that only personal data that is adequate, relevant and limited to what is necessary is processed. However, the principle has seen limited adoption due to the lack of technical interpretation. In this work, we build on literature in machine learning and law to propose FIDO, a Framework for Inhibiting Data Overcollection. FIDO learns to limit data collection based on an interpretation of data minimization tied to system performance. Concretely, FIDO provides a data collection stopping criterion by iteratively updating an estimate of the performance curve, or the relationship between dataset size and performance, as data is acquired. FIDO estimates the performance curve via a piecewise power law technique that models distinct phases of an algorithm's performance throughout data collection separately. Empirical experiments show that the framework produces accurate performance curves and data collection stopping criteria across datasets and feature acquisition algorithms. We further demonstrate that many other families of curves systematically overestimate the return on additional data. Results and analysis from our investigation offer deeper insights into the relevant considerations when designing a data minimization framework, including the impacts of active feature acquisition on individual users and the feasability of user-specific data minimization. We conclude with practical recommendations for the implementation of data minimization. © 2022 Owner/Author.

关键词： Data acquisition

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2022 International conference on Data Analytics for Business and Industry, ICDABI 2022

2022 International Conference on Data Analytics for Business...

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2022 International conference on Data Analytics for Business and Industry, ICDABI 2022

ISBN: (纸本)9781665490580

The proceedings contain 145 papers. The topics discussed include: literature review of blockchain-based cloud computing: data security issues and challenges;a review on methods for managing the risk of android ransomware;blockchain vulnerabilities and recent security challenges: a review paper;the internet of things security issues and countermeasures in network layer: a systematic literature review;supply chain management in green construction: a systematic review;digital marketing competencies as a factor in the success of e-commerce small businesses in international markets;predicting the impact of university majors on the length of post-graduation unemployment periods in Bahrain;comparing the performance of machine learning algorithms for predicting employees’ turnover;signal synchronization of traffic lights using reinforcement learning;and assessing the adequacy of the legal framework in facilitating e-commerce in Nigeria.

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Smoky Mountains Computational Sciences and Engineering conference, SMC 2022

Smoky Mountains Computational Sciences and Engineering Confe...

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Smoky Mountains Computational Sciences and Engineering conference, SMC 2022

ISBN: (纸本)9783031236051

The proceedings contain 24 papers. The special focus in this conference is on Smoky Mountains Computational Sciences and Engineering. The topics include: The INTERSECT Open Federated Architecture for the Laboratory of the Future;real-Time Edge Processing During Data Acquisition;towards a Software Development Framework for Interconnected Science Ecosystems;Adrastea: An Efficient FPGA Design Environment for Heterogeneous Scientific computing and machine learning;toward an Autonomous Workflow for Single Crystal Neutron Diffraction;industrial Experience Deploying Heterogeneous Platforms for Use in Multi-modal Power Systems Design Workflows;self-describing Digital Assets and Their Applications in an Integrated Science and Engineering Ecosystem;Simulation Workflows in Minutes, at Scale for Next-Generation HPC;machine learning Approaches to High Throughput Phenotyping;Self-learning Data Foundation for Scientific AI;SMC 2022 Data Challenge: Summit Spelunkers Solution for Challenge 2;usage Pattern Analysis for the Summit Login Nodes;finding Hidden Patterns in High Resolution Wind Flow Model Simulations;investigating Relationships in Environmental and Community Health: Correlations of Environment, Urban Morphology, and Socio-economic Factors in the Los Angeles Metropolitan Statistical Area;patterns and Predictions: Generative Adversarial Networks for Neighborhood Generation;Preconditioners for Batched Iterative Linear Solvers on GPUs;mobility Aware Computation Offloading Model for Edge computing;machine learning for First Principles Calculations of Material Properties for Ferromagnetic Materials;A Vision for Coupling Operation of US Fusion Facilities with HPC Systems and the Implications for Workflows and Data Management;at-the-Edge Data Processing for Low Latency High Throughput machine learning algorithms;Implementation of a Framework for Deploying AI Inference Engines in FPGAs;calvera: A Platform for the Interpretation and Analysis of Neutron Scattering Data.

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2022 International conference on Emerging Trends in computing and Engineering Applications, ETCEA 2022 - Proceedings

2022 International Conference on Emerging Trends in Computin...

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2022 International conference on Emerging Trends in computing and Engineering Applications, ETCEA 2022

ISBN: (纸本)9781665477093

The proceedings contain 44 papers. The topics discussed include: recognizing cells in histological images of fish gonads;human-cyber-physical disassembly workstation 5.0 for sustainable manufacturing;classification of fundus images captured using D-eye smartphone retinal imaging system;off-shelf deep learning architectures as feature extractor in CT COVID-19 classification;a method for measuring the weight of human body segments based on 3D-printed body segments;risk assessment and biomechanical analysis of sewing machine operators using CATIA software;hyperparameters optimization of convolutional neural networks using evolutionary algorithms;a hybrid approach for auto-correcting grammatical errors generated by non-native Arabic speakers;a comparison of the application of artificial intelligence in the educational system;and autonomous vehicle and its adoption: challenges, opportunities, and future implications.

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Development of Loan Default Prediction Model for Finance Companies in Sri Lanka - A Case Study

Development of Loan Default Prediction Model for Finance Com...

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International conference on Data Science and Its Applications (ICoDSA)

作者： Chitty, Rajiv Gunawikrama, Keerthi Fernando, Harinda Asia Pacific Inst Informat Technol Sch Comp Colombo Sri Lanka Univ Ruhuna Fac Engn Dept Elect & Informat Engn Matara Sri Lanka

ISBN: (数字)9781665486651

ISBN: (纸本)9781665486651

Finance Companies (FC's), play a pivotal role in the economy of Sri Lanka, by serving the under banked and non-banked segments of the society. The business model entails lending to the bottom of the pyramid, that leads to the acceptance of higher credit risk at a higher yield that inevitably leads to lower asset quality. The focus on this customer segment has lead to an increase in non performing loans among FCs in the recent past. Due to several challenges facing the industry, including intense competition and lack of experienced credit officers, the FC's have been seeking options to automate evaluation of credit worthiness at the point of loan origination. This work is an attempt to develop a machine learning based loan default prediction system to improve credit decisions. Several traditional machine learning algorithms are chosen, trained and validated by using real world data set related to vehicle leasing, obtained from one of the leading FCs in Sri Lanka. The data set consists of 100,000 cases having 29 attributes each. Models are compared for accuracy, sensitivity, specificity and robustness. The model using Support Vector machine and Random Forest produces comparatively promising results. Further work is recommended to generalize the model for economic cycles and shocks using micro and macro economic variables.

关键词： loan default predication machine learning finance companies

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