A survey of techniques for improving efficiency of mobile web browsing

Mittal, Sparsh; Mattela, Venkat

doi:10.1002/cpe.5126

Cited by 4 publications

(2 citation statements)

References 77 publications

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“…To achieve this, you should avoid using lengthy and overused software and codes and try to find the most efficient algorithm for purpose. Lightweight applications are also called Lite, an acronym for lightweight [9][10][11] [12].…”

Section: Lightweight Applicationsmentioning

confidence: 99%

Lightweight Steganography Technique for Smartphone

Mohammed¹,

Ahmed²

2019

IJCA

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Steganography is an embedded technique that hides the message in a digital medium for the purpose of safely transferring the message. The objective of this paper is to develop and improve lightweight hiding techniques used in the systems of smartphones, by improving the well-known Least Significant Bit (LSB) algorithm by changing the attribute that is related to its work and suggesting some additions in a way that does not affect processing speed or memory usage and increases security. It also provides a large area for hiding a large secrete message with very little impact on the cover relative to the size of the cover used. This paper proposes several additions and modifications to the algorithm, including: using Pseudo Random Number Generator (PRNG) to determine pixels locations in the cover. Coding process was also proposed for the purpose of increasing randomization and provide statistical distribution unrelated to the original message. The LSB hiding process enables the use of less important bits per pixel. In this case, a distribution of hidden bits within one pixel was suggested by different distributions in each hiding process, making data of repeated hiding useless for the attackers. Various measures (MSE, PSNR, BER, and SSIM) were adopted for the purpose of measuring the quality and efficiency of the proposed algorithm through several experiments including cover image sizes in different dimensions with a variety of data for secret messages, whose results were better than other values.

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Section: Lightweight Applicationsmentioning

confidence: 99%

Lightweight Steganography Technique for Smartphone

Mohammed¹,

Ahmed²

2019

IJCA

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“…A smartphone is a device that consolidates robust hardware sensors and a versatile mobile operating system into one unit [5,8,23]. Smartphones contain various sensors: magnetometer, accelerometer, a gyroscope that their software can utilize [33], and remote connectivity conventions, for example, Bluetooth, Wi-Fi, and satellite navigation [7,11].…”

Section: Introductionmentioning

confidence: 99%

Betalogger: Smartphone Sensor-based Side-channel Attack Detection and Text Inference Using Language Modeling and Dense MultiLayer Neural Network

Javed

Rehman

Khan

et al. 2021

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

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With the recent advancement of smartphone technology in the past few years, smartphone usage has increased on a tremendous scale due to its portability and ability to perform many daily life tasks. As a result, smartphones have become one of the most valuable targets for hackers to perform cyberattacks, since the smartphone can contain individuals’ sensitive data. Smartphones are embedded with highly accurate sensors. This article proposes BetaLogger , an Android-based application that highlights the issue of leaking smartphone users’ privacy using smartphone hardware sensors (accelerometer, magnetometer, and gyroscope). BetaLogger efficiently infers the typed text (long or short) on a smartphone keyboard using Language Modeling and a Dense Multi-layer Neural Network (DMNN). BetaLogger is composed of two major phases: In the first phase, Text Inference Vector is given as input to the DMNN model to predict the target labels comprising the alphabet, and in the second phase, sequence generator module generate the output sequence in the shape of a continuous sentence. The outcomes demonstrate that BetaLogger generates highly accurate short and long sentences, and it effectively enhances the inference rate in comparison with conventional machine learning algorithms and state-of-the-art studies.

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