Cognitive Radio Jamming Attack Detection Using an Autoencoder for CRIoT Network

2021 ◽  
Author(s):  
V. Nallarasan ◽  
Kottilingam Kottursamy
Keyword(s):  
Cognitive Radio ◽  
Attack Detection ◽  
Jamming Attack

scholarly journals Cognitive Radio Jamming Attack Detection Using an Autoencoder for CRIoT Network 

2021 ◽  
Author(s):  
Nallarasan v ◽  
Kottilingam Kottursamy
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sharing ◽  
Time Instant ◽  
Physical Layer ◽  
Attack Detection ◽  
Spectrum Utilization ◽  
Jamming Attacks ◽  
Dynamic Spectrum Sharing ◽  
Jamming Attack ◽  
Jamming Detection

Abstract IoT network-connected devices will be kept on increasing and will cross million, but it is impossible to allocate spectrum for those million and million of the devices. This spectrum scarcity can be handled by incorporating cognitive radio-based dynamic spectrum sharing, which is referred to as Cognitive Radio Internet of Things (CRIoT). But CRIoT sufferers from the Physical layer attack in cognitive radio, which affects the spectrum sensing accuracy and reduces the spectrum utilization. There are various attacks at the physical layer of cognitive radio among jamming attacks resulting in a denial of cognitive radio services and make spectrum underutilization. Continuous jamming can be detected quickly based on time delay on spectrum access, but discrete random jamming detection is challenging. This article proposes an autoencoder deep learning architecture-based jamming attack detection in cognitive radio. The jamming detection problem is modeled as anomaly detection. The autoencoder architecture is used to detect the jammer anomaly of the jammer. The proposed system involves the simulation of a random jamming attack and detecting it at a particular time instant information that may help mitigate the jammer attack .the proposed mechanism able to detect the jammer with 89% of accuracy.

scholarly journals A Machine Learning Approach for Backoff Manipulation Attack Detection in Cognitive Radio

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 227349-227359
Author(s):  
Wassim Fassi Fihri ◽  
Hassan El Ghazi ◽  
Badr Abou El Majd ◽  
Faissal El Bouanani
Keyword(s):  
Machine Learning ◽  
Cognitive Radio ◽  
Attack Detection ◽  
Learning Approach ◽  
Machine Learning Approach

scholarly journals Semi-supervised machine learning for primary user emulation attack detection and prevention through core-based analytics for cognitive radio networks

2019 ◽  
Vol 15 (9) ◽  
pp. 155014771986036 ◽  
Author(s):  
Sundar Srinivasan ◽  
KB Shivakumar ◽  
Muazzam Mohammad
Keyword(s):  
Machine Learning ◽  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Primary User ◽  
Radio Networks ◽  
Attack Detection ◽  
Supervised Machine Learning ◽  
Machine Language ◽  
Detection Accuracy ◽  
Primary User Emulation

Cognitive radio networks are software controlled radios with the ability to allocate and reallocate spectrum depending upon the demand. Although they promise an extremely optimal use of the spectrum, they also bring in the challenges of misuse and attacks. Selfish attacks among other attacks are the most challenging, in which a secondary user or an unauthorized user with unlicensed spectrum pretends to be a primary user by altering the signal characteristics. Proposed methods leverage advancement to efficiently detect and prevent primary user emulation future attack in cognitive radio using machine language techniques. In this paper novel method is proposed to leverage unique methodology which can efficiently handle during various dynamic changes includes varying bandwidth, signature changes etc… performing learning and classification at edge nodes followed by core nodes using deep learning convolution network. The proposed method is compared with that of two other state-of-art machine learning-based attack detection protocols and has found to significantly reduce the false alarm to secondary network, at the same time improve the overall detection accuracy at the primary network.

scholarly journals A jamming attack detection technique for opportunistic networks

2021 ◽  
pp. 100464
Author(s):  
Jagdeep Singh ◽  
Isaac Woungang ◽  
Sanjay Kumar Dhurandher ◽  
Khuram Khalid
Keyword(s):  
Opportunistic Networks ◽  
Attack Detection ◽  
Detection Technique ◽  
Jamming Attack

scholarly journals Experimental demonstration of confidential communication with quantum security monitoring

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yupeng Gong ◽  
Adrian Wonfor ◽  
Jeffrey H. Hunt ◽  
Ian H. White ◽  
Richard V. Penty
Keyword(s):  
Continuous Variable ◽  
Attack Detection ◽  
Excess Noise ◽  
Experimental Demonstration ◽  
Light Power ◽  
Classical Communication ◽  
Security Monitoring ◽  
Security Issues ◽  
Jamming Attack ◽  
Receiver System

AbstractSecurity issues and attack management of optical communication have come increasingly important. Quantum techniques are explored to secure or protect classical communication. In this paper, we present a method for in-service optical physical layer security monitoring that has vacuum-noise level sensitivity without classical security loopholes. This quantum-based method of eavesdropping detection, similar to that used in conventional pilot tone systems, is achieved by sending quantum signals, here comprised of continuous variable quantum states, i.e. weak coherent states modulated at the quantum level. An experimental demonstration of attack detection using the technique was presented for an ideal fibre tapping attack that taps 1% of the ongoing light in a 10 dB channel, and also an ideal correlated jamming attack in the same channel that maintains the light power with excess noise increased by 0.5 shot noise unit. The quantum monitoring system monitors suspicious changes in the quantum signal with the help of advanced data processing algorithms. In addition, unlike the CV-QKD system which is very sensitive to channel excess noise and receiver system noise, the quantum monitoring is potentially more compatible with current optical infrastructure, as it lowers the system requirements and potentially allows much higher classical data rate communication with links length up to 100 s km.

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