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Wireless Environment Security: Challenges and Analysis Using Deep Learning

Wireless Environment Security: Challenges and Analysis Using Deep Learning

Vidushi, Manisha Agarwal, Aditya Khamparia, Naghma Khatoon
Copyright: © 2020 |Pages: 19
ISBN13: 9781799850687|ISBN10: 1799850684|ISBN13 Softcover: 9781799852759|EISBN13: 9781799850694
DOI: 10.4018/978-1-7998-5068-7.ch004
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MLA

Vidushi, et al. "Wireless Environment Security: Challenges and Analysis Using Deep Learning." Deep Learning Strategies for Security Enhancement in Wireless Sensor Networks, edited by K. Martin Sagayam, et al., IGI Global, 2020, pp. 65-83. https://doi.org/10.4018/978-1-7998-5068-7.ch004

APA

Vidushi, Agarwal, M., Khamparia, A., & Khatoon, N. (2020). Wireless Environment Security: Challenges and Analysis Using Deep Learning. In K. Sagayam, B. Bhushan, A. Andrushia, & V. Albuquerque (Eds.), Deep Learning Strategies for Security Enhancement in Wireless Sensor Networks (pp. 65-83). IGI Global. https://doi.org/10.4018/978-1-7998-5068-7.ch004

Chicago

Vidushi, et al. "Wireless Environment Security: Challenges and Analysis Using Deep Learning." In Deep Learning Strategies for Security Enhancement in Wireless Sensor Networks, edited by K. Martin Sagayam, et al., 65-83. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-5068-7.ch004

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Abstract

The communication through the wireless environment is open, which completely differs from the wired. This open environment communication can be accessed by users including illegitimate and thus increases the vulnerability for malicious attacks. For that reason, motivation comes to study about the different possible security challenges, threats, and to devise powerful, efficient, and improved required solution to improve the various security vulnerabilities. This chapter presents the challenges regarding security and the security requirement in the wireless type communication. The research performs the analysis of deep learning for detecting malicious websites. These websites are responsible to disrupt normal system working and can control the complete system and its resources by installing malware on to the respective machine. To elucidate the constructive and effective way towards the detection of malicious URL, the study uses convolutional neural networks.

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