Investigating Use of Kinds of Deep Learning Methods in Internet of Things Networks Security
The development of smart devices in many aspects of our daily lives is accompanied by the increasing use of appropriate mechanisms to counter them against various attacks and applications in the Internet of Things environment. In this context, it is emerging as one of the most successful and suitable techniques for use in various aspects of IoT security. The aim of this is to systematically review and analyze research studies on research eyes conducted in different Internet of Things security scenarios. The reviewed researches are classified according to different perspectives in a coherent and structured classification to identify the gap in this research area. This research has been published on articles related to the keywords "concept learning", "security" and "Internet of Things" in the four main databases IEEEXplore, ScienceDirect, SpringerLink, and ACM Digital Library. In the end, 90 articles have been selected and reviewed. These studies are conducted according to three main research questions, i.e. the security aspects involved, the network architectures used, and the datasets used in IoT security. The final discussion explores the research gaps and acknowledges the outstanding flaws and vulnerabilities in the IoT security scenario.
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Dynamic Load Balancing Improvement in Software-Defined Networks Using Fuzzy Multi-Objective Programming Algorithms
Mohammadreza Forghani, Mohammadreza Soltanaghaei*, Farsad Zamani Boroujeni
Journal of Information and Communication Technology, -
بهبود توزیع بار هوشمند در مهندسی ترافیک شبکه های تعریف شده نرم افزاری
محمدرضا فرقانی*، ، فرساد زمانی بروجنی
نشریه فناوریهای نوین در مهندسی برق و کامپیوتر، تابستان 1403 -
A stochastic mathematical programming approach to resilient supplier selection and order allocation problem: A case study of Iran Khodro supply chain
A. Bakhtiari Tavana, M. Rabieh *, M. S. Pishvaee, M. Esmaeili
Scientia Iranica, Sep-Oct 2023 -
Classification and Allocation of Suppliers to Customers in Resilince Supply Chains Using Machine Learning
, Laya Olfat *, Maghsoud Amiri, Iman Raeesi Vanani
Journal of Industrial Management Perspective,