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صفحه اصلی
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سی امین کنفرانس بین المللی مهندسی برق
Defense Against Spectrum Sensing Data Falsification Attack in Cognitive Radio Networks Using Machine Learning
نویسندگان :
Nazanin Parhizgar
1
Ali Jamshidi
2
Peyman Setoodeh
3
1- دانشکده مهندسی برق و کامپیوتر/ دانشگاه شیراز
2- دانشکده مهندسی برق و کامپیوتر/ دانشگاه شیراز
3- دانشکده مهندسی برق و کامپیوتر/ دانشگاه شیراز
کلمات کلیدی :
cognitive radio (CR) networks, spectrum sensing data falsification (SSDF) attack, artificial neural network (ANN), classification
چکیده :
Cognitive radio (CR) networks are an emerging and promising technology to improve the utilization of vacant bands. In CR networks, security is a very noteworthy domain. Two threatening attacks are primary user emulation (PUE) and spectrum sensing data falsification (SSDF) attacks. A PUE attacker mimics the primary user signals to deceive the legitimate secondary users. The SSDF attacker falsifies its observations to misguide the fusion center to make a wrong decision about the status of the primary user. In this paper, we propose a scheme based on clustering the secondary users to counter SSDF attacks. Our focus is on detecting and classifying each cluster into reliable and unreliable. We introduce two different methods using an artificial neural network (ANN) for both methods and five more classifiers such as support vector machine (SVM), random forest (RF), K-nearest neighbors, logistic regression (LR), and decision tree (DR) for the second one to accomplish this target. we also consider deterministic and white Gaussian noise (WGN) scenarios for attack strategy. The results demonstrate that our method outperforms a recently suggested scheme.
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بیشتر
ثمین همایش، سامانه مدیریت کنفرانس ها و جشنواره ها - نگارش 43.6.0
