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Register a new userSYSML: StYlometry with Structure and Multitask Learning: Implications for Darknet Forum Migrant Analysis
Sysml: Stylometry مع الهيكل والتعلم المتعدد التعلم: الآثار المترتبة على تحليل منتدى Darknet
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تستخدم منتديات سوق Darknet في كثير من الأحيان لتبادل السلع والخدمات غير القانونية بين الأطراف التي تستخدم التشفير لإخفاء هوياتها.يتم استخدام شبكة Tor لاستضافة هذه الأسواق، والتي تضمن إخفاء هويتها الإضافي من IP وتتبع الموقع، مما يجعل من الصعب الارتباط عبر المستخدمين الخبيثة باستخدام حسابات متعددة (Sybils).بالإضافة إلى ذلك، يهاجر المستخدمون إلى منتديات جديدة عندما يتم إغلاق المرء زيادة زيادة صعوبة ربط المستخدمين عبر منتديات متعددة.نقوم بتطوير نهج تعليم متعدد الأطباق المستند إلى المصممة على أساس التصديق على اللغة الطبيعية والتفاعلات النموذجية باستخدام Asceddings الرسم البياني لإنشاء تمثيلات منخفضة الأبعاد من حلقات قصيرة لنشاط المستخدم لإسناد التأليف.نحن نقدم تقييم شامل لأساليبنا في أربع منتديات Darknet المختلفة التي توضح فعالتها على أحدث من الفن، مع رفع ما يصل إلى 2.5x في مرتبة الاسترجاع المتوسط و 2x على استدعاء @ 10.
Darknet market forums are frequently used to exchange illegal goods and services between parties who use encryption to conceal their identities. The Tor network is used to host these markets, which guarantees additional anonymization from IP and location tracking, making it challenging to link across malicious users using multiple accounts (sybils). Additionally, users migrate to new forums when one is closed further increasing the difficulty of linking users across multiple forums. We develop a novel stylometry-based multitask learning approach for natural language and model interactions using graph embeddings to construct low-dimensional representations of short episodes of user activity for authorship attribution. We provide a comprehensive evaluation of our methods across four different darknet forums demonstrating its efficacy over the state-of-the-art, with a lift of up to 2.5X on Mean Retrieval Rank and 2X on Recall@10.
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