Journal of Cyber Security and Mobility最新文献_第9页

Improving Intrusion Detection in UAV Communication Using an LSTM-SMOTE Classification Method 利用LSTM-SMOTE分类方法改进无人机通信入侵检测

Journal of Cyber Security and Mobility Pub Date : 2022-01-01 DOI: 10.32604/jcs.2023.042486

Abdulrahman M. Abdulghani, Mokhles M. Abdulghani, Wilbur L. Walters, Khalid H. Abed

引用次数: 0

CenterPicker: An Automated Cryo-EM Single-Particle Picking Method Based on Center Point Detection 中心拾取器:一种基于中心点检测的冷冻电镜单粒子自动拾取方法

Journal of Cyber Security and Mobility Pub Date : 2022-01-01 DOI: 10.32604/jcs.2022.028065

Jian-quan Ouyang, Jinling Wang, Yaowu Wang, Tianming Liu

引用次数: 0

Deep Learning Based Image Forgery Detection Methods 基于深度学习的图像伪造检测方法

Journal of Cyber Security and Mobility Pub Date : 2022-01-01 DOI: 10.32604/jcs.2022.032915

Liang Xiu-jian, Sun He

引用次数: 0

Security Analysis for a VANET Privacy Protection Scheme VANET隐私保护方案的安全性分析

Journal of Cyber Security and Mobility Pub Date : 2022-01-01 DOI: 10.32604/jcs.2022.028792

Yuzhen Liu, Xiaoliang Wang, Zhoulei Cao, Frank Jiang

引用次数: 0

Analysis of Security Aspects in LoRaWAN LoRaWAN的安全问题分析

Journal of Cyber Security and Mobility Pub Date : 2022-01-01 DOI: 10.32604/jcs.2022.030498

Ahmed AL-Hthlool, M. Frikha

引用次数: 1

Phishing Scam Detection on Ethereum via Mining Trading Information 基于挖掘交易信息的以太坊网络钓鱼诈骗检测

Journal of Cyber Security and Mobility Pub Date : 2022-01-01 DOI: 10.32604/jcs.2022.038401

Yanyu Chen, Zhangjie Fu

引用次数: 0

Web Tracking Domain and Possible Privacy Defending Tools: A燣iterature燫eview 网络跟踪域和可能的隐私保护工具:燣文献燫综述

Journal of Cyber Security and Mobility Pub Date : 2022-01-01 DOI: 10.32604/jcs.2022.029020

Maryam Abdulaziz Saad Bubukayr, M. Frikha

引用次数: 2

A Survey on Visualization-Based Malware Detection 基于可视化的恶意软件检测研究进展

Journal of Cyber Security and Mobility Pub Date : 2022-01-01 DOI: 10.32604/jcs.2022.033537

A. Moawad, Ahmed Ismail Ebada, Aya M. Al-Zoghby

引用次数: 0

Design and Deployment of Network Testbed for Web Data Security Web数据安全网络试验台的设计与部署

Journal of Cyber Security and Mobility Pub Date : 2021-12-28 DOI: 10.13052/jcsm2245-1439.1121

Shishir K. Shandilya

{"title":"Design and Deployment of Network Testbed for Web Data Security","authors":"Shishir K. Shandilya","doi":"10.13052/jcsm2245-1439.1121","DOIUrl":"https://doi.org/10.13052/jcsm2245-1439.1121","url":null,"abstract":"In recent years, the cyber security scenario has transformed predominantly from conventional response-based security mechanisms to proactive security strategies. And this transformation is still continuing which is shifting it from proactive security strategies to cyber immunity which eliminates the cyber threats by introducing stringent and adaptive security measures. In the process of developing new security algorithms/procedures, accurate modelling and effective simulation play a vital role for the robustness and effectiveness of proposed system. It is also necessary to analyze the behaviour of proposed system against multiple types of known cyber attacks. This paper focuses on the existing network testbeds for an effective analysis and monitoring while proposing a new network testbed for examining new security concepts like cyber immunity. The proposed network testbed is designed to incorporate the methods and procedures of Nature-inspired Cyber Security to accommodate the adaptive responses against the sophisticated and ever-advancing cyber attacks. The proposed testbed provides customizable analytical tool to design, test and examine the new security algorithms through a rich set of attack scenarios. It also allows developers to design, implement, and evaluate their defensive techniques with library support.","PeriodicalId":37820,"journal":{"name":"Journal of Cyber Security and Mobility","volume":"51 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76914180","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: 1

Phisher Fighter: Website Phishing Detection System Based on URL and Term Frequency-Inverse Document Frequency Values Phisher Fighter：基于URL和词频逆文档频率值的网站钓鱼检测系统

Journal of Cyber Security and Mobility Pub Date : 2021-11-20 DOI: 10.13052/jcsm2245-1439.1114

E. S. Vishva, D. Aju

{"title":"Phisher Fighter: Website Phishing Detection System Based on URL and Term Frequency-Inverse Document Frequency Values","authors":"E. S. Vishva, D. Aju","doi":"10.13052/jcsm2245-1439.1114","DOIUrl":"https://doi.org/10.13052/jcsm2245-1439.1114","url":null,"abstract":"Fundamentally, phishing is a common cybercrime that is indulged by the intruders or hackers on naive and credible individuals and make them to reveal their unique and sensitive information through fictitious websites. The primary intension of this kind of cybercrime is to gain access to the ad hominem or classified information from the recipients. The obtained data comprises of information that can very well utilized to recognize an individual. The purloined personal or sensitive information is commonly marketed in the online dark market and subsequently these information will be bought by the personal identity brigands. Depending upon the sensitivity and the importance of the stolen information, the price of a single piece of purloined information would vary from few dollars to thousands of dollars. Machine learning (ML) as well as Deep Learning (DL) are powerful methods to analyse and endeavour against these phishing attacks. A machine learning based phishing detection system is proposed to protect the website and users from such attacks. In order to optimize the results in a better way, the TF-IDF (Term Frequency-Inverse Document Frequency) value of webpages is employed within the system. ML methods such as LR (Logistic Regression), RF (Random Forest), SVM (Support Vector Machine), NB (Naive Bayes) and SGD (Stochastic Gradient Descent) are applied for training and testing the obtained dataset. Henceforth, a robust phishing website detection system is developed with 90.68% accuracy.","PeriodicalId":37820,"journal":{"name":"Journal of Cyber Security and Mobility","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42488057","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: 3