Journal of Cyber Security and Mobility最新文献_第7页

A Privacy Preserving Framework to Protect Sensitive Data in Online Social Networks 一个保护在线社交网络中敏感数据的隐私保护框架

Journal of Cyber Security and Mobility Pub Date : 2022-11-07 DOI: 10.13052/jcsm2245-1439.1144

Nisha P. Shetty, Balachandra, Niraj Yagnik, Tulika Banerjee, Angad Singh

{"title":"A Privacy Preserving Framework to Protect Sensitive Data in Online Social Networks","authors":"Nisha P. Shetty, Balachandra, Niraj Yagnik, Tulika Banerjee, Angad Singh","doi":"10.13052/jcsm2245-1439.1144","DOIUrl":"https://doi.org/10.13052/jcsm2245-1439.1144","url":null,"abstract":"In this day and age, Internet has become an innate part of our existence. This virtual platform brings people together, facilitating information exchange, sharing photos, posts, etc. As interaction happens without any physical presence in the medium, trust is often compromised in all these platforms operating via the Internet. Although many of these sites provide their ingrained privacy settings, they are limited and do not cater to all users’ needs. The proposed work highlights the privacy risk associated with various personally identifiable information posted in online social networks (OSN). The work is three-facet, i.e. it first identifies the type of private information which is unwittingly revealed in social media tweets. To prevent unauthorized users from accessing private data, an anonymous mechanism is put forth that securely encodes the data. The information loss incurred due to anonymization is analyzed to check how much of privacy-utility trade-off is attained. The private data is then outsourced to a more secure server that only authorized people can access. Finally, to provide effective retrieval at the server-side, the traditional searchable encryption technique is modified, considering the typo errors observed in user searching behaviours. With all its constituents mentioned above, the purported approach aims to give more fine-grained control to the user to decide who can access their data and is the correct progression towards amputating privacy violation.","PeriodicalId":37820,"journal":{"name":"Journal of Cyber Security and Mobility","volume":"32 1","pages":"575-600"},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87126520","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

Can We Detect Malicious Behaviours in Encrypted DNS Tunnels Using Network Flow Entropy? 我们可以使用网络流熵检测加密DNS隧道中的恶意行为吗？

Journal of Cyber Security and Mobility Pub Date : 2022-08-14 DOI: 10.13052/jcsm2245-1439.1135

Yulduz Khodjaeva, Nur Zincir-Heywood, Ibrahim Zincir

引用次数: 0

Dynamic List Based Data Integrity Verification in Cloud Environment 云环境下基于动态列表的数据完整性验证

Journal of Cyber Security and Mobility Pub Date : 2022-07-22 DOI: 10.13052/jcsm2245-1439.1134

Akshay Kc, Balachandra Muniyal

引用次数: 0

A Systematic Literature Review of Routine Activity Theory’s Applicability in Cybercrimes 常规活动理论在网络犯罪中的应用研究综述

Journal of Cyber Security and Mobility Pub Date : 2022-06-17 DOI: 10.13052/jcsm2245-1439.1133

R. Ahmad, R. Thurasamy

{"title":"A Systematic Literature Review of Routine Activity Theory’s Applicability in Cybercrimes","authors":"R. Ahmad, R. Thurasamy","doi":"10.13052/jcsm2245-1439.1133","DOIUrl":"https://doi.org/10.13052/jcsm2245-1439.1133","url":null,"abstract":"Cybercrimes are increasing at an alarming rate and cause detrimental effects to the victims. Routine Activity Theory (RAT) is commonly used to understand the factors influencing cybercrime victimization. However, there have been inconsistent findings on the applicability of RAT theory. This study performs a Systematic Literature Review analysis to consolidate and provide a coherent analysis of the related studies employing RAT theory for cybercrime victimization. The articles were also differentiated based on the cybercrimes topologies being investigated; (a) cybercrime dependent (hacking and malware) and (b) cybercrime enabled (phishing, fraud and identity theft). The findings suggest that a refined specification and operationalization of RAT’S construct tailoring to the types of cybercrimes can arguably yield more accurate application and interpretation of RAT Theory in cybercrimes. Consequently, this will address the inaccurate measurement issues of some of the RATS’s constructs, leading to inconclusive effects on cybercrime victimization. In addition, there is a need for more longitudinal studies to disentangle the effect of RAT’s construct during pre and post cybercrimes. Security advocates can apply the findings of this research to formulate relevant cybercrime awareness programs. The findings also shed some insights into which groups should be targeted for different cybercrime educational and awareness programs. This study can increase the awareness among citizens in terms of their online activities, their attributes and the types of protection from becoming cybercrime victims.","PeriodicalId":37820,"journal":{"name":"Journal of Cyber Security and Mobility","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47077558","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: 4

On the Controllability of Artificial Intelligence: An Analysis of Limitations 论人工智能的可控性:局限性分析

Journal of Cyber Security and Mobility Pub Date : 2022-05-25 DOI: 10.13052/jcsm2245-1439.1132

Roman V. Yampolskiy

引用次数: 6

Classification of Phishing Email Using Word Embedding and Machine Learning Techniques 基于词嵌入和机器学习技术的网络钓鱼邮件分类

Journal of Cyber Security and Mobility Pub Date : 2022-05-07 DOI: 10.13052/jcsm2245-1439.1131

M. Somesha, A. R. Pais

{"title":"Classification of Phishing Email Using Word Embedding and Machine Learning Techniques","authors":"M. Somesha, A. R. Pais","doi":"10.13052/jcsm2245-1439.1131","DOIUrl":"https://doi.org/10.13052/jcsm2245-1439.1131","url":null,"abstract":"Email phishing is a cyber-attack, bringing substantial financial damage to corporate and commercial organizations. A phishing email is a special type of spamming, used to trick the user to disclose personal information to access his digital assets. Phishing attack is generally triggered by emailing links to spoofed websites that collect sensitive information. The APWG survey suggests that the existing countermeasures remain ineffective and insufficient for detecting phishing attacks. Hence there is a need for an efficient mechanism to detect phishing emails to provide better security against such attacks to the common user. The existing open-source data sets are limited in diversity, hence they do not capture the real picture of the attack. Hence there is a need for real-time input data set to design accurate email anti-phishing solutions. In the current work, it has been created a real-time in-house corpus of phishing and legitimate emails and proposed efficient techniques to detect phishing emails using a word embedding and machine learning algorithms. The proposed system uses only four email header-based heuristics for the classification of emails. The proposed word embedding cum machine learning framework comprises six word embedding techniques with five machine learning classifiers to evaluate the best performing combination. Among all six combinations, Random Forest consistently performed the best with FastText (CBOW) by achieving an accuracy of 99.50% with a false positive rate of 0.053%, TF-IDF achieved an accuracy of 99.39% with a false positive rate of 0.4% and Count Vectorizer achieved an accuracy of 99.18% with a false positive rate of 0.98% respectively for three datasets used.","PeriodicalId":37820,"journal":{"name":"Journal of Cyber Security and Mobility","volume":"39 1","pages":"279-320"},"PeriodicalIF":0.0,"publicationDate":"2022-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75873911","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: 6

Journal of Cyber Security and Mobility Pub Date : 2022-03-22 DOI: 10.13052/jcsm2245-1439.1124

Florian Platzer, Fabian Brenner, M. Steinebach

引用次数: 3

An Introduction to the exFAT File System and How to Hide Data Within exFAT文件系统介绍及如何在其中隐藏数据

Journal of Cyber Security and Mobility Pub Date : 2022-03-22 DOI: 10.13052/jcsm2245-1439.1125

J. Heeger, York Yannikos, M. Steinebach

引用次数: 0

Majority Vote-Based Ensemble Approach for Distributed Denial of Service Attack Detection in Cloud Computing 基于多数投票的云计算分布式拒绝服务攻击检测集成方法

Journal of Cyber Security and Mobility Pub Date : 2022-03-22 DOI: 10.13052/jcsm2245-1439.1126

A. Alqarni

{"title":"Majority Vote-Based Ensemble Approach for Distributed Denial of Service Attack Detection in Cloud Computing","authors":"A. Alqarni","doi":"10.13052/jcsm2245-1439.1126","DOIUrl":"https://doi.org/10.13052/jcsm2245-1439.1126","url":null,"abstract":"Cloud computing is considered as technical advancement in information technology. Many organizations have been motivated by this advancement to outsource their data and computational needs. Such platforms are required to fulfil basic security principles such as confidentiality, availability, and integrity. Cloud computing offers scalable and virtualized services with a high flexibility level and decreased maintenance costs to end-users. The infrastructure and protocols that are behind cloud computing may contain bugs and vulnerabilities. These vulnerabilities are being exploited by attackers, leading to attacks. Among the most reported attacks in cloud computing are distributed denial-of-service (DDOS) attacks. DDOS attacks are conducted by sending many data packets to the targeted infrastructure. This leads to most network bandwidth and server time being consumed, thus causing a denial of the service problem. Several methods have been proposed and experimented with for early DDOS attack detection. Employing a single machine learning classification model may give an adequate level of attack detection accuracy but needs an enhancement. In this study, we propose an approach based on an ensemble of machine learning classifiers. The proposed approach uses a majority vote-based ensemble of classifiers to detect attacks more accurately. A subset of the CICDDOS2019 dataset consisting of 32,000 instances, including 8450 benign and 23,550 DDOS attack instances was used in this study for results and evaluation. The experimental results showed that 98.02% accuracy was achieved with 97.45% sensitivity and 98.65% specificity.","PeriodicalId":37820,"journal":{"name":"Journal of Cyber Security and Mobility","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49330630","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: 9

Evaluating Dynamic Tor Onion Services for Privacy Preserving Distributed Digital Identity Systems 保护隐私分布式数字身份系统的动态Tor洋葱服务评估

Journal of Cyber Security and Mobility Pub Date : 2022-03-22 DOI: 10.13052/jcsm2245-1439.1122

Tobias Höller, Michael Roland, R. Mayrhofer

引用次数: 2