The 5th International Conference on Future Networks & Distributed Systems最新文献

Modeling and Computing Available Rights for Algorithmic Licensing of Movies based on Blockchain 基于区块链的电影算法授权的可用权利建模与计算

The 5th International Conference on Future Networks & Distributed Systems Pub Date : 2021-12-15 DOI: 10.1145/3508072.3508224

A. Beckmann, João Santos, Indira Ananth

引用次数: 1

An Efficient and Secure Consensus Mechanism for Peer-to-Peer Networks 一种高效、安全的对等网络共识机制

The 5th International Conference on Future Networks & Distributed Systems Pub Date : 2021-12-15 DOI: 10.1145/3508072.3508226

Avinash Kshirsagar, V. Pachghare

引用次数: 1

Integration Of Consortium Blockchain Model In The Nigerian Banking Sector 尼日利亚银行业整合联盟区块链模式

The 5th International Conference on Future Networks & Distributed Systems Pub Date : 2021-12-15 DOI: 10.1145/3508072.3508110

O. A. Ojerinde, Jonathan Salawu, O. A. Abisoye, Andrew A. Uduimoh

引用次数: 0

Detection and Classification System for Cashew Plant Diseases using Convolutional Neural Network 基于卷积神经网络的腰果病害检测与分类系统

The 5th International Conference on Future Networks & Distributed Systems Pub Date : 2021-12-15 DOI: 10.1145/3508072.3508107

Mathew Timothy, Ojo John, A. Aibinu, B. Adebisi

{"title":"Detection and Classification System for Cashew Plant Diseases using Convolutional Neural Network","authors":"Mathew Timothy, Ojo John, A. Aibinu, B. Adebisi","doi":"10.1145/3508072.3508107","DOIUrl":"https://doi.org/10.1145/3508072.3508107","url":null,"abstract":"Cashew (Anacardium occidentale L.) is an important cash crop which serves as a major source of food, income and industrial raw materials to many African countries. However, cashew crops are commonly infected with various diseases that cause significant loss in yield. Existing automatic disease identification techniques are foliar identification-based while other infections appear on the nut and stem. In this work Convolutional Neural Network (CNN) was deployed for the detection and classification of diseases in leaves, stem and nut of cashew plant. Local dataset was sourced from LAUTECH teaching and research farm cashew plantation using Sony alpha A-330 digital camera with resolution of 10.20 Megapixels. The dataset comprises of 1050 sample images from three different parts of the various cashew plants that includes leaf, nut and stem. The database was divided into three sets for training, validation and testing in the ratio 50%, 30% and 20% respectively. Each of these sample images were preprocessed using histogram equalization and de-noised, using median filter. Feature extraction with classification and detection was done using transfer learning with CNN (ResNet-50) pre-trained deep learning network. The developed system was implemented in MATLAB R (2018a). It was evaluated using specificity, sensitivity, accuracy and error rate on the validation and test sets. The average performance was also computed. The system gave specificity, sensitivity, accuracy and error rate of 97.22, 98.19, 97.22, 2.78% on validations set and 97.56, 98.82, 98.29, 1.71% on test set, respectively. The average performance obtained for specificity, sensitivity, accuracy and error rate, were 97.39, 97.55, 97.76 and 2.25%, respectively. The system detected and classified four categories of cashew plant diseases that commonly occur on the leave, nut and stem.","PeriodicalId":315315,"journal":{"name":"The 5th International Conference on Future Networks & Distributed Systems","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125135594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improving the Analysis of Business Processes in Digital Era 改进数字化时代的业务流程分析

The 5th International Conference on Future Networks & Distributed Systems Pub Date : 2021-12-15 DOI: 10.1145/3508072.3508222

Shayzak Kholmuminov, B. Tursunov, M. Saidova, Laylo Abduhalilova, Nigora Sadriddinova

引用次数: 4

Optimization of Information Processes of Multilevel Intelligent Systems 多层次智能系统的信息处理优化

The 5th International Conference on Future Networks & Distributed Systems Pub Date : 2021-12-15 DOI: 10.1145/3508072.3508212

G. Ishankhodjayev, M. Sultanov, Dilmurod Mirzaahmedov, D. Azimov

引用次数: 5

Important Aspects Related to Foreign Exchange Operations of Commercial Banks 商业银行外汇业务的重要方面

The 5th International Conference on Future Networks & Distributed Systems Pub Date : 2021-12-15 DOI: 10.1145/3508072.3508208

Akram Tolliboevich Absalamov, Oybek Odilovich Xudoyorov, Sardor Umarovich Kholdorov

引用次数: 0

A Hybrid Deep Learning Approach for Advanced Persistent Threat Attack Detection 高级持续威胁攻击检测的混合深度学习方法

The 5th International Conference on Future Networks & Distributed Systems Pub Date : 2021-12-15 DOI: 10.1145/3508072.3508085

Meaad Alrehaili, Adel Alshamrani, A. Eshmawi

{"title":"A Hybrid Deep Learning Approach for Advanced Persistent Threat Attack Detection","authors":"Meaad Alrehaili, Adel Alshamrani, A. Eshmawi","doi":"10.1145/3508072.3508085","DOIUrl":"https://doi.org/10.1145/3508072.3508085","url":null,"abstract":"Advanced Persistent Threat (APT) attack is one of the most common and costly destructive attacks on the target system. This attack has become a challenge for companies, governments, and organizations’ information security systems. In recent years, methods for detecting and preventing APT attacks that use machine learning or deep learning algorithms to analyze indications and anomalous behaviors in network traffic have become popular. However, due to a lack of typical data from attack campaigns, the APT attack detection approach that uses behavior analysis and evaluation approaches encounter many issues. Network traffic analysis to detect a common APT attack is one of the solutions for dealing with this situation. This paper develops efficient and flexible deep learning models. To analyze huge network traffic, a hybrid deep learning approach that builds two models is used: Stacked Autoencoder with Long Short-Term Memory (SAE-LSTM) and Convolutional Neural Networks with Long Short-Term Memory Network (CNN-LSTM) to detect indications of APT attacks. A reliable dataset ’DAPT2020’ that covers all APT stages is used to evaluate the proposed approach. The experimental results demonstrate that the hybrid deep learning approach proved to give higher performance than the individual deep learning model in detecting malicious behavior in each APT stage.","PeriodicalId":315315,"journal":{"name":"The 5th International Conference on Future Networks & Distributed Systems","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131809288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Effect of Variability of Orders in an Auction-based Local Energy Market 基于拍卖的局部能源市场中订单可变性的影响

The 5th International Conference on Future Networks & Distributed Systems Pub Date : 2021-12-15 DOI: 10.1145/3508072.3508104

Olamide Jogunola, A. S. Ajagun, B. Adebisi, Craig Morley

引用次数: 0

Security in Internet of Things: RPL Attacks Taxonomy 物联网安全:RPL攻击分类

The 5th International Conference on Future Networks & Distributed Systems Pub Date : 2021-12-15 DOI: 10.1145/3508072.3512286

Farag Azzedin, Hussah Albinali

引用次数: 2