2018
DOI: 10.1109/tii.2017.2789219
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Significant Permission Identification for Machine-Learning-Based Android Malware Detection

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Cited by 496 publications
(274 citation statements)
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References 24 publications
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“…[26][27][28] This algorithm is context-sensitive and flow-sensitive-based analysis algorithm. It basically performs two major steps: 1) Identify at each "echo" location what is being written; 2) Determine the call order of each "echo" function.…”
Section: Http Response Estimationmentioning
confidence: 99%
“…[26][27][28] This algorithm is context-sensitive and flow-sensitive-based analysis algorithm. It basically performs two major steps: 1) Identify at each "echo" location what is being written; 2) Determine the call order of each "echo" function.…”
Section: Http Response Estimationmentioning
confidence: 99%
“…Li et al proposed a malware detection system based on permission usage analysis by significant permission identification technique. 3 levels of pruning by mining the permission data are developed to identify the most significant permissions [56]. These recently proposed techniques can be incorporated with vehicular networks to mitigate intruder attacks.…”
Section: Attacks Related Tomentioning
confidence: 99%
“…8 This model allows structural matching to identify similar binary code. 15 For semantic-based methods, Pewny et al introduced an approach to transform instruction sequence into a set of equation representing memory operations. 9 Kong and Yan presented an automated malware classification framework, where the graph distance is computed based on FCG matching, and converted FCG matching to discrete optimization problem with relaxed constraint.…”
Section: Related Workmentioning
confidence: 99%