Pub Date : 2022-01-01DOI: 10.32604/jcs.2023.042486
Abdulrahman M. Abdulghani, Mokhles M. Abdulghani, Wilbur L. Walters, Khalid H. Abed
{"title":"Improving Intrusion Detection in UAV Communication Using an LSTM-SMOTE Classification Method","authors":"Abdulrahman M. Abdulghani, Mokhles M. Abdulghani, Wilbur L. Walters, Khalid H. Abed","doi":"10.32604/jcs.2023.042486","DOIUrl":"https://doi.org/10.32604/jcs.2023.042486","url":null,"abstract":"","PeriodicalId":37820,"journal":{"name":"Journal of Cyber Security and Mobility","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79866746","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}
Pub Date : 2022-01-01DOI: 10.32604/jcs.2022.028065
Jian-quan Ouyang, Jinling Wang, Yaowu Wang, Tianming Liu
{"title":"CenterPicker: An Automated Cryo-EM Single-Particle Picking Method Based on Center Point Detection","authors":"Jian-quan Ouyang, Jinling Wang, Yaowu Wang, Tianming Liu","doi":"10.32604/jcs.2022.028065","DOIUrl":"https://doi.org/10.32604/jcs.2022.028065","url":null,"abstract":"","PeriodicalId":37820,"journal":{"name":"Journal of Cyber Security and Mobility","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87608479","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}
Pub Date : 2022-01-01DOI: 10.32604/jcs.2022.032915
Liang Xiu-jian, Sun He
{"title":"Deep Learning Based Image Forgery Detection Methods","authors":"Liang Xiu-jian, Sun He","doi":"10.32604/jcs.2022.032915","DOIUrl":"https://doi.org/10.32604/jcs.2022.032915","url":null,"abstract":"","PeriodicalId":37820,"journal":{"name":"Journal of Cyber Security and Mobility","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85016871","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}
Pub Date : 2022-01-01DOI: 10.32604/jcs.2022.028792
Yuzhen Liu, Xiaoliang Wang, Zhoulei Cao, Frank Jiang
{"title":"Security Analysis for a VANET Privacy Protection Scheme","authors":"Yuzhen Liu, Xiaoliang Wang, Zhoulei Cao, Frank Jiang","doi":"10.32604/jcs.2022.028792","DOIUrl":"https://doi.org/10.32604/jcs.2022.028792","url":null,"abstract":"","PeriodicalId":37820,"journal":{"name":"Journal of Cyber Security and Mobility","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76166791","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}
Pub Date : 2022-01-01DOI: 10.32604/jcs.2022.030498
Ahmed AL-Hthlool, M. Frikha
{"title":"Analysis of Security Aspects in LoRaWAN","authors":"Ahmed AL-Hthlool, M. Frikha","doi":"10.32604/jcs.2022.030498","DOIUrl":"https://doi.org/10.32604/jcs.2022.030498","url":null,"abstract":"","PeriodicalId":37820,"journal":{"name":"Journal of Cyber Security and Mobility","volume":"126 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78643886","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}
Pub Date : 2022-01-01DOI: 10.32604/jcs.2022.038401
Yanyu Chen, Zhangjie Fu
{"title":"Phishing Scam Detection on Ethereum via Mining Trading Information","authors":"Yanyu Chen, Zhangjie Fu","doi":"10.32604/jcs.2022.038401","DOIUrl":"https://doi.org/10.32604/jcs.2022.038401","url":null,"abstract":"","PeriodicalId":37820,"journal":{"name":"Journal of Cyber Security and Mobility","volume":"28 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83669202","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}
Pub Date : 2022-01-01DOI: 10.32604/jcs.2022.029020
Maryam Abdulaziz Saad Bubukayr, M. Frikha
{"title":"Web Tracking Domain and Possible Privacy Defending Tools: A燣iterature燫eview","authors":"Maryam Abdulaziz Saad Bubukayr, M. Frikha","doi":"10.32604/jcs.2022.029020","DOIUrl":"https://doi.org/10.32604/jcs.2022.029020","url":null,"abstract":"","PeriodicalId":37820,"journal":{"name":"Journal of Cyber Security and Mobility","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75587308","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}
Pub Date : 2022-01-01DOI: 10.32604/jcs.2022.033537
A. Moawad, Ahmed Ismail Ebada, Aya M. Al-Zoghby
{"title":"A Survey on Visualization-Based Malware Detection","authors":"A. Moawad, Ahmed Ismail Ebada, Aya M. Al-Zoghby","doi":"10.32604/jcs.2022.033537","DOIUrl":"https://doi.org/10.32604/jcs.2022.033537","url":null,"abstract":"","PeriodicalId":37820,"journal":{"name":"Journal of Cyber Security and Mobility","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86499761","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}
Pub Date : 2021-12-28DOI: 10.13052/jcsm2245-1439.1121
Shishir K. Shandilya
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.
{"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}
Pub Date : 2021-11-20DOI: 10.13052/jcsm2245-1439.1114
E. S. Vishva, D. Aju
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.
{"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}
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