G. Padmavathi ¹, S. Shanmugavel ²

Affiliations
1 Department of Electronics and Communication Engineering, Sri Venkateswara College of Engineering, Sriperumbudur - 602117, Tamil Nadu, IN
2 Department of Electronics and Communication Engineering, National Engineering College, Kovilpatti - 628503, Tamil Nadu, IN

Abstract

Cognitive Radio is a technology for Next Generation Wireless Networks to support Dynamic Spectrum Access which addresses the spectrum scarcity and under utilization problems experienced by today’s wireless communication networks. Spectrum Sensing is an important function of cognitive radio that identifies the unused licensed frequency bands of Primary Users for opportunistic access by Secondary Users. In this paper, we have proposed a sensing technique incorporating wavelet denoising to enhance the sensing performance at low SNR regime. The combination of wavelet denoising preceeding energy detector, hard decision for local sensing and OR fusion rule at the fusion center for cooperative sensing has improved the sensing performance. We have quantified the performance improvement of the proposed technique in fading environments and studied the effect of wavelet denoising. Probability of missed detection has been reduced by 38% over Rayleigh Fading channel and 52% over Nakagami Fading channel compared to conventional sensing technique for ten users cooperation. Sensing time has been reduced by 1 msec and throughput improved by 50 Mbits/Sec/Hz compared to conventional energy detection based sensing method.

Keywords

Cognitive Radio, Cooperative Spectrum Sensing, Energy Detection, Hard Decision, Wavelet Denoising

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S. Divya ¹, G. Padmavathi ²

Affiliations
1 Faculty of Computer Science and Multimedia, Lincoln University College, Selangor, MY
2 Department of Computer Science, Avinashilingam Institute for Home Science and Higher Education for Women,University, Coimbatore - 641043, Tamil Nadu, IN

Abstract

Objectives: In the world of Internet today, most of the communications are done through Internet applications. Rapidly with the growth of Internet, the security threat on Internet is also increasing. Internet worms are one of the serious dangerous threats heavy financial losses. To overcome these damages, the proposed methodology provide better defense mechanism through Internet worm detection and containment schemes based on connection attempt failures characteristic. Method: The Internet worm detection is done using the Machine Learning Method based on Anomaly detection schemes and containment based on blocking schemes. The proposed kernelized Extreme Learning Machine with Automated Worm Containment Algorithm (kEA) method is used for detection and containment of malicious traffic from non-existing IP addresses based on connection attempt failures. Findings: Second channel based propagation through botnet worms propagates illegal traffic from malicious IP addresses through connection attempt failures. This traffic is transferred through TCP and UDP transmission schemes. The proposed work is used to identify the second channel propagating worms and containment of malicious traffic. Improvement: The proposed kernelized Extreme Learning Machine (kELM) method achieved detection accuracy improved by 23.67%. Then proposed kEA method blocks all the detected malicious IP addresses with 100% containment at the time span of 33 ms.

Keywords

Connection Attempt Failures, Kernelized ELM, Malicious IP.

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