Velagapudi Ramakrishna Siddhartha Engineering College

About: Velagapudi Ramakrishna Siddhartha Engineering College is a based out in . It is known for research contribution in the topics: Computer science & Antenna (radio). The organization has 1307 authors who have published 1155 publications receiving 6163 citations.

Design, Simulation of a Compact Triangular Shaped Dual-Band Microstrip Antenna for 2.4 GHz Bluetooth/WLAN and UWB Applications

Abstract: In this research article, a compact (12 × 20 mm2) triangular shaped monopole dual-band antenna is presented for 2.4 GHz Bluetooth/WLAN and ultrawideband (UWB) applications. The proposed geometry consist of a simple triangular shaped radiating patch for achieving UWB characteristics and a quarter wavelength inverted L shaped strip attached to the patch for achieving second operating band at 2.45 GHz for Bluetooth/WLAN applications. The first operating band characteristics can be controlled by changing the electrical length of the strip. To enhance the impedance bandwidth of second operating band, an equilateral triangular shaped cut has been introduced in the patch. The measured results (S11 ≤ ?10 dB) demonstrates that the proposed antenna exhibit dual frequency operation from 2.4 to 2.52 GHz (Bandwidth of 120 MHz) and from 3.2 to 10.6 GHz (Bandwidth of 7.4 GHz). The proposed very small dual-band antenna has omnidirectional radiation patterns, peak gains and radiation efficiencies across both the operating bands.

Malicious URL Detection using Logistic Regression

Abstract: One of the major challenges faced by the Internet in the present day is to deal with achieving web security from ever-rising diverse types of threats. Machine learning algorithms offer promising techniques to detect malicious websites performing unethical anonymous activities on the Internet. Attackers have been found to continuously evolve with updated techniques to attack web users using malicious Uniform Resource Locators (URLs). The main objective of such attacks is to gain financial benefits through acquiring personal information. In the present research, a machine learning (ML)-based approach is proposed to identify malicious users from URL data. An ML model is implemented using Logistic Regression to detect malicious URLs. The data set used in the study is collected from well-known sources like PhishTank, Kaggle.com, and Github.com. Our novel framework is further evaluated against traditional malicious URL models and our results highlight positive steps forward of the proposed approach.