A Sub-Optimal Low Complex Multiple Malicious Users' Detection Mechanism in a Cooperative Spectrum Sensing System

نویسنده

  • Shivanshu Shrivastava
چکیده

In cognitive radio based cooperative sensing systems, some secondary users are likely to act malicious. Such secondary users are called malicious users. An optimal application of Dixon's outlier detection scheme for detecting malicious users in a cognitive radio cooperative spectrum sensing system has recently been termed as the Sliding Window Dixon's test. The Sliding window Dixon's test applies the Dixon's test on the smaller data sets with maximum overlaps. However, it is found that this scheme is highly complex. In this paper, we propose a novel scheme to exploit the robustness of the Dixon's test without compromising on its complexity. Simulations demonstrate that the proposed methods are efficient in suppressing multiple malicious users. Keywords— Sub-optimal low complex scheme, Cognitive Radio, Cooperative Spectrum Sensing, Secondary User, Malicious User, Dixon's test, Sliding Window Dixon's test

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

An Effective and Optimal Fusion Rule in the Presence of Probabilistic Spectrum Sensing Data Falsification Attack

Cognitive radio (CR) network is an excellent solution to the spectrum scarcity problem. Cooperative spectrum sensing (CSS) has been widely used to precisely detect of primary user (PU) signals. The trustworthiness of the CSS is vulnerable to spectrum sensing data falsification (SSDF) attack. In an SSDF attack, some malicious users intentionally report wrong sensing results to cheat the fusion c...

متن کامل

Spectrum Sensing Data Falsification Attack in Cognitive Radio Networks: An Analytical Model for Evaluation and Mitigation of Performance Degradation

Cognitive Radio (CR) networks enable dynamic spectrum access and can significantly improve spectral efficiency. Cooperative Spectrum Sensing (CSS) exploits the spatial diversity between CR users to increase sensing accuracy. However, in a realistic scenario, the trustworthy of CSS is vulnerable to Spectrum Sensing Data Falsification (SSDF) attack. In an SSDF attack, some malicious CR users deli...

متن کامل

Investigation of Always Present and Spectrum Sensing based Incumbent Emulators

Cognitive radio (CR) technology has been suggested for effective use of spectral resources. Spectrum sensing is one of the main operations of CR users to identify the vacant frequency bands. Cooperative spectrum sensing (CSS) is used to increase the performance of CR networks by providing spatial diversity. The accuracy of spectrum sensing is the most important challenge in the CSS process sinc...

متن کامل

Secure Collaborative Spectrum Sensing in the Presence of Primary User Emulation Attack in Cognitive Radio Networks

Collaborative Spectrum Sensing (CSS) is an effective approach to improve the detection performance in Cognitive Radio (CR) networks. Inherent characteristics of the CR have imposed some additional security threats to the networks. One of the common threats is Primary User Emulation Attack (PUEA). In PUEA, some malicious users try to imitate primary signal characteristics and defraud the CR user...

متن کامل

A Fast Malicious User Detection Scheme Based on POMDP for Cooperative Spectrum Sensing in Cognitive Radio networks

Cooperative spectrum sensing (CSS) can improve spectrum sensing accuracy, but it can be injured due to potential attacks from malicious cognitive radio user who reports false sensing results to the fusion center (FC). Many researchers focus on reducing the effect of malicious users on the accuracy of spectrum sensing. A promising method to detect malicious users is to determine their abnormal s...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2017