رمزگذار خودکار پشته‌ای کرونکر عمیق بهبود‌یافته با منطق فازی برای تشخیص مقاوم حملات در اینترنت اشیاء اجتماعی

حسن نتاج صلحدار, محمد

رمزگذار خودکار پشته‌ای کرونکر عمیق بهبود‌یافته با منطق فازی برای تشخیص مقاوم حملات در اینترنت اشیاء اجتماعی

نوع مقاله : مقاله پژوهشی

نویسنده

محمد حسن نتاج صلحدار

دانشجوی دکتری،پردیس صنعتی شهدای هویزه، دانشگاه شهید چمران اهواز، اهواز، ایران

چکیده

در این پژوهش، با توجه به پیچیدگی‌ و پویایی روزافزون حملات در محیط‌های اینترنت اشیاء اجتماعی (SIoT)، یک مدل ترکیبی نوین به نام خودرمزگذار پشته‌ای کرونکر عمیق فازی (Fuzzy DKSA) پیشنهاد شده است. ابتدا داده‌های خام شبکه با استفاده از نرمال‌سازی Z Score به مقیاس یکسان رسانده شدند و سپس از یک شبکه عصبی عمیق (DNN) تقویت‌شده با معیار شباهت گوور برای ادغام ویژگی‌ها بهره گرفته شد. در فاز تشخیص، ساختار شبکه کرونکر عمیق (DKN) با رمزگذار خودکار چندلایه (DSA) همراه با منطق فازی ترکیب شد تا به صورت پویا نسبت به الگوهای متفاوت حمله و شرایط متغیر شبکه واکنش نشان دهد. برای سنجش عملکرد، مجموعه‌داده‌ی N BaIoT با بیش از هفت میلیون نمونه و ده کلاس حمله متنوع مورد استفاده قرار گرفت. نتایج تجربی نشان داد که مدل پیشنهادی با بهبود همزمان دقت (۹۲٪)، معیار ) F1۹۱٪( و دقت پیش‌بینی (۹۱٪)، نسبت به روش‌های مرسوم مانند GAN، MH CNN AM، TM MLA و HAD برتری دارد. این دستاوردها تأکید می‌کنند که ادغام منطق فازی با معماری‌های عمیق می‌تواند عدم قطعیت‌‌های مرتبط با ترافیک شبکه و الگوهای حمله را به‌طور مؤثری مدیریت کند.

کلیدواژه‌ها

اینترنت اشیاء اجتماعی

منطق فازی

شبکه کرونکر عمیق

رمزگذار خودکار پشته‌ای

تشخیص نفوذ

موضوعات

آسیب پذیری ها و تهدیدات فضای سایبری

عنوان مقاله English

Fuzzy-Enhanced Deep Kronecker Stacked Autoencoder for Robust Attack Detection in Social Internet of Things

نویسنده English

Mohammad Hassan Nataj Solhdar

PhD student.Department of Shohadaye hoveyzeh Campus of technology, Shahid Chamran University of Ahvaz, Ahvaz,Iran

چکیده English

This paper proposes a novel hybrid model, the Fuzzy Deep Kronecker Stacked Autoencoder (FDKSAE), addressing the increasing complexity and dynamism of attacks in Social Internet of Things (SIoT) environments. Initially, raw network data were scaled using Z-score normalization. Subsequently, a deep neural network enhanced with the Gower similarity metric was employed for feature integration. In the detection phase, the deep Kronecker network architecture was combined with a multi-layer autoencoder and fuzzy logic to dynamically react to diverse attack patterns and varying network conditions. For performance evaluation, the N-BaIoT dataset, comprising over seven million samples and ten diverse attack classes, was utilized. Experimental results demonstrated that the proposed model achieved superior performance with simultaneous improvements in accuracy (92%), F1-score (91%), and prediction precision (91%), compared to conventional methods such as GAN, MH CNN AM, TM MLA, and HAD. These achievements underscore that the integration of fuzzy logic with deep architectures can effectively manage uncertainties associated with network traffic and attack patterns.

کلیدواژه‌ها English

Social Internet of Things – SIoT

Fuzzy Logic

Deep Kronecker Network

Stacked Autoencoder

Intrusion Detection

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