| مشخصات مقاله | |
| ترجمه عنوان مقاله | تشخیص سریع اسپمر ها از طریق اشتراک اطلاعات مشارکتی در میان فراهم کنندگان خدمات چندگانه |
| عنوان انگلیسی مقاله | Rapid detection of spammers through collaborative information sharing across multiple service providers |
| انتشار | مقاله سال ۲۰۱۹ |
| تعداد صفحات مقاله انگلیسی | ۱۴ صفحه |
| هزینه | دانلود مقاله انگلیسی رایگان میباشد. |
| پایگاه داده | نشریه الزویر |
| نوع نگارش مقاله |
مقاله پژوهشی (Research article) |
| مقاله بیس | این مقاله بیس نمیباشد |
| نمایه (index) | scopus – master journals – JCR |
| نوع مقاله | ISI |
| فرمت مقاله انگلیسی | |
| ایمپکت فاکتور(IF) |
۵٫۳۴۱ در سال ۲۰۱۷ |
| شاخص H_index | ۸۵ در سال ۲۰۱۹ |
| شاخص SJR | ۰٫۸۴۴ در سال ۲۰۱۷ |
| شناسه ISSN | ۰۱۶۷-۷۳۹X |
| شاخص Quartile (چارک) | Q1 در سال ۲۰۱۷ |
| رشته های مرتبط | مهندسی کامپیوتر – مهندسی فناوری اطلاعات – مهندسی فناوری اطلاعات و ارتباطات |
| گرایش های مرتبط | الگوریتم و محاسبات – اینترنت و شبکه های گسترده – شبکه های کامپیوتری |
| نوع ارائه مقاله |
ژورنال |
| مجله / کنفرانس | سیستم های کامپیوتری نسل آینده – Future Generation Computer Systems |
| دانشگاه | School of Computing Science, Newcastle University, United Kingdom |
| کلمات کلیدی | SPIT، مشارکتی، VoIP، عملیات شبکه، اعتبار |
| کلمات کلیدی انگلیسی | SPIT, Collaboration, VoIP, Network operations, Reputation |
| شناسه دیجیتال – doi |
https://doi.org/10.1016/j.future.2017.12.026 |
| کد محصول | E12034 |
| وضعیت ترجمه مقاله | ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید. |
| دانلود رایگان مقاله | دانلود رایگان مقاله انگلیسی |
| سفارش ترجمه این مقاله | سفارش ترجمه این مقاله |
| فهرست مطالب مقاله: |
| Outline Highlights Abstract Keywords ۱٫ Introduction ۲٫ VoIP spam ۳٫ Related work and motivation ۴٫ Collaborative SPIT detection system: the design ۵٫ Discussion on the privacy protection ۶٫ Experimental methodology ۷٫ Performance evaluation ۸٫ Conclusions Acknowledgments References |
| بخشی از متن مقاله: |
| Abstract Spammers and telemarketers target a very large number of recipients usually dispersed across many Service Providers (SPs). Collaboration and Information sharing between SPs would increase the detection accuracy but detection effectiveness depends on the amount of information shared between SPs. Having service provider’s exchange call detail records would arguably attain the best detection accuracy but would require significant network resources. Moreover, SPs are likely to feel uncomfortable in sharing their call records because call records contain user’s private information as well as operational details of their networks. The challenge towards the design of collaborative Spam over Internet Telephony (SPIT) detection system is two-fold: it should attain high detection accuracy with a small false positive, and should fully protect the privacy of users and their service providers. In this paper, we propose a COllaborative Spit Detection System (COSDS)—a collaborative SPIT detection system for the Voice over IP (VoIP) network where service providers collaborate for the effective and early detection of SPIT callers without raising privacy concerns. To this extent, COSDS relies on a trusted Centralized Repository (CR) and exchange of non-sensitive reputation scores. The CR computes global reputation of users by aggregating the reputation scores provided by the respective collaborating SPs. The data exchanged to the CR is not sensitive regarding users privacy, and cannot be used to infer the relationship network of users. We evaluate the performance of our system using synthetic data that we have generated by simulating the realistic social behavior of spammers and non-spammers in a network. The results show that the COSDS approach has better detection accuracy as compared to the traditional stand-alone detection systems. For instances, in a setup where spammers are making calls to recipients of many SPs, COSDS successfully identifies spammers with the True Positive (TP) rate of around 80% and false positive (FP) rate of around 2% on a first day, which further increases to 100% TP rate and zero FP rate in three days. COSDS approach is fast, requires a small communication overhead, ensures privacy of users and collaborating SP, and requires only few iterations for the reputation convergence within the SP. |