مروری تحلیل ترافیک شبکه‌ گمنام‌ساز پارس با استفاده از یادگیری ماشین

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دانشگاه امام حسین علیه السلام

2 عضو هیئت علمی

3 استادیار دانشگاه جامع امام حسین(ع)

چکیده

گمنامی یکی از ارکان حریم خصوصی در محیط اینترنت به شمار می‌‌‌‌آید که رعایت آن توسط دولت‌‌‌‌ها و سرویس‌‌‌‌های خدماترسانی امری ضروری است. تشخیص ترافیک عبوری از یک شبکه، به منزله تشخیص ماهیت آن ترافیک است و اگر این ترافیک، ترافیک یک گمنام‌ساز باشد به این معنی است که در شبکه اطلاعات محرمانه در حال رد و بدل شدن است و این به معنی خدشه وارد شدن به گمنامی است. رده‌بندی ترافیک، یک روش بسیار قوی در داده‌کاوی است که کاربردهای فراوانی دارد. از جمله این کاربردها می‌‌‌‌توان به مدیریت ترافیک با استفاده از شناسایی ترافیک عبوری از شبکه اشاره نمود. در این تحقیق با استفاده از روش‌های داده‌کاوی، در گام اول، میزان تفکیک‌پذیری گمنام‌ساز پارس (که یک گمنام‌ساز بومی است) با ترافیک گمنام‌سازهای مسیریاب پیازی، پروژه اینترنت نامرئی، جاندو و ترافیک HTTPS، و در گام دوم و در یک بررسی عمیق‌تر، میزان تفکیک‌پذیری ترافیک چهار سرویس متفاوت درون گمنام‌ساز پارس مورد بررسی قرار گرفت. نتایج این آزمایش‌ها در گام اول، رده‌بندی با دقت 100% و در گام دوم، دقت بالای 95% را (با استفاده از الگوریتم جنگل تصادفی) نشان می‌دهد. علاوه بر آن، با رتبه‌بندی ویژگی‌های استفاده شده در هر آزمایش، میزان تاثیرگذاری این ویژگی‌ها بر دقت کل و زمان ساخت مدل بررسی شده است.

کلیدواژه‌ها


عنوان مقاله [English]

Pars Anonymity Network Traffic Flow Analysis Using Machine Learning

نویسندگان [English]

  • Mehdi Dehghani 2
  • H. Akbari 3
2 Teacher
3 Assistant Professor of Imam Hossein University
چکیده [English]

Anonymity is one of the fundamentals of privacy in the internet that should be strictly considered by governments and ISPs. Network traffic flow detection, is considered as detecting the nature of this traffic; Thus, if the traffic of an anonymizer is detected, it means that classified data is being transmitting throw the network, which in return is a great flaw in the anonymity system. Traffic classification - which has various applications - is one of the most powerful methods in datamining. Traffic management via detecting network traffic flow, is viewed as one of these applications. In this research, by using datamining techniques, in the first step the detection rate of Pars Anonymizer (as a domestic anonymizer) is assessed in compare with The Onion Router, Invisible Internet Project, JonDo and HTTPS Traffic, and at the next step, in a more detailed way, the classification rate of four different services in the desired anonymizer was studied. Results suggest that the classification accuracy rate of these experiments at the first step is 100% and at the next step -with the use of Random Forest algorithm- is 95%. In addition, by evaluating the used specifications in every experiment, the effectiveness of these specifications on the overall accuracy and the model build time was assessed.

کلیدواژه‌ها [English]

  • Anonymity
  • Anonymity Network
  • Data Mining
  • Classification
  • Machine Learning
  • Traffic Analysis
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