Wireless Environment Security: Challenges and Analysis Using Deep Learning

Wireless Environment Security: Challenges and Analysis Using Deep Learning

Vidushi (Banasthali Vidyapith, India), Manisha Agarwal (Banasthali Vidyapith, India), Aditya Khamparia (Lovely Professional University, India) and Naghma Khatoon (Usha Martin University, India)
DOI: 10.4018/978-1-7998-5068-7.ch004
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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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The present era is timed by emphasizing on the security due to advancement in technology that exceedingly trends the devices towards the connectivity through the wireless network. Nowadays, an enormous volume of information is transmitted with the help of wireless networks that has generated a myriad of privacy problems. The wireless system enhances the technological flexibility and makes the environment more convenient to work (Aliu, 2012). Along with advantages, it also increases risk of security. It directly invites and open the security threats that may affect day to day life and breaches privacy. Wireless network provides convenience like searching the web, interconnection, etc. however, it is certain that some problems of security, data breach or privacy may come into the picture. Several complications are faced by society like academic loss, economic loss and many personal life problems. Problem of privacy breach is unavoidable, this can be disastrous and life changing event. Thus, security has paramount importance along with improvements in technology. Lack of security can be a barrier in the way of technology usage and advancement. Because security is a necessary evil, it opens the door for the academic researchers, scientists, as well as industrial researchers.

Deep learning seems as a possible solution to analyze security problems. Machine learning technique by using past data and experience will lead to improvement. It can learn without any requirement of human. Machine learning (Alpaydin, 2020) technology has shown great impact and achievement in the area of artificial intelligence. Deep learning (Shokri, 2015) is also a part and member of machine learning. Significant use and success of machine and deep learning have been observed in many applications like recognition of an object, in medical science, feature prediction or detection, jamming, etc.

The existing serious problem of malicious URL detection is understandable and has been deeply analyzed in this document using deep learning model. Because of the active adoption of deep learning in different above-mentioned fields, this letter uses this learning for detecting the malicious URL. Therefore, the objective of this paper is investigating the deep learning potential in terms of accuracy and to measure loss occurred while detecting malicious URL.

In deep learning model, this research presents a convolutional network model (Zhao, 2013) for malicious URL detection. However, this document shows the potential as well as loss in training together with validation phase. This letter presents analysis of a convolutional network that is a deep learning model for the detection of malicious URL in the wireless network. Deep learning is a technique which mimics brain learning process. It tries to apply the working technique of brain to the machine. Deep learning thus adds intelligence and makes the model more powerful. Deep learning tries to simulate behavior of the brain therefore possess capability of data processing. The human brain works smoothly even in a complex situation, the brain takes a decision based on inputs collected by the sense organs. Deep learning seems eye catching method as it achieves similar capability like brain. For accomplishing this aim numerous layers of the neural network are present in deep learning. These layers help to solve complicated and compound problems. Deep learning has the ability to learn from experience, so more experience means more learning. Learning from the past is used to frame the model to train the dataset. With the completion of the neural network training, suitable decision can be taken to get an intellectual reward. The implementation of this complete idea in the real world situations has shown remarkable success, such as face identification, interpretation of language, drug discovery, etc. The astonishing outcome have been shown by deep learning dealing with real problems.

Deep learning is not a new branch, rather it is a subcategory of machine learning. With the help of cascading layers, it retrieves useful features that are further used in a powerful decision-making task. Prediction, analysis, detection, recognition, and many such complicated tasks need complex decision making. The only correct decision will lead to successful completion of the task. Otherwise unpredictable problems can arise. So It can be said that, success can only be achieved by the right decision. For instance, incorrect weather prediction or incorrect predictions in business can lead to huge damage in terms of not only monetary but life also. In brief, deep learning can represent the complex environment, can extract abstract information and last but not least a good decision-maker.

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