Abstract
For a reliable and convenient system, it is essential to build a secure system that will be protected from outer attacks and also serve the purpose of keeping the inner data safe from intruders. A juice jacking is a popular and spreading cyber-attack that allows intruders to get inside the system through the web and theive potential data from the system. For peripheral communications, Universal Serial Bus (USB) is the most commonly used standard in 5G generation computer systems. USB is not only used for communication, but also to charge gadgets. However, the transferal of data between devices using USB is prone to various security threats. It is necessary to maintain the confidentiality and sensitivity of data on the bus line to maintain integrity. Therefore, in this paper, a juice jacking attack is analyzed, using the maximum possible means through which a system can be affected using USB. Ten different malware attacks are used for experimental purposes. Various machine learning and deep learning models are used to predict malware attacks. An extensive experimental analysis reveals that the deep learning model can efficiently recognize the juice jacking attack. Finally, various techniques are discussed that can either prevent or avoid juice jacking attacks.
Highlights
Juice jacking is a well-known cyber-attack used to attack Universal Serial Bus (USB)enabled devices such as mobiles, tablets, and laptops
As the data and information transmitted through the plain text and encryption techniques are applied to secure the bus line [35,36,37], both the USB device and the host controller built a secure channel to improve the USB line using the concept of encryption
Fx denotes the xth frequency recorded. This block can be identified as a peak and the time of the peak measurement is retained if the resulting RMSE is higher than a predetermined threshold
Summary
Juice jacking is a well-known cyber-attack used to attack Universal Serial Bus (USB)enabled devices such as mobiles, tablets, and laptops. It generally utilizes the charging port of a given device; whenever someone connects a given device to the system using this port, the hackers obtain all their personal information or may upload some malware onto the device. Images, music, and SMS are just a few of the items that may be accessible once the device is associated with a computer Both data and power transfer can be accomplished using USB connectors [2,5]. Various supervised learning models, such as decision trees, linear regression, machine learning, support vector machines, and neural networks, have been employed in IoT cybersecurity applications to predict threats
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