Android malicious software detection method based on combined feature mode

Abstract

The invention discloses an Android malicious software detection method based on a combined feature mode. Firstly, a certain amount of Android malicious software and Android benign software training samples are acquired to construct a training sample set; authority features and sensitive API features of each training sample are analyzed and combined to generate feature vectors of the training samples; the feature vectors of all the training samples serve as input to train an ELM, and the ELM is obtained; to-be-detected Android software serves as a test sample, and the authority feature and thesensitive API feature of the test sample are analyzed and combined to generate the feature vector of the test sample; the feature vector of the test sample is input into the ELM, and finally, whetherthe test sample is Android malicious software or not is judged by the ELM. The method has the advantages of being high in Android malicious software detection accuracy and short in learning time.

Description

technical field [0001] The invention belongs to the field of mobile security, in particular to a malicious software detection technology based on combined feature patterns. Background technique [0002] The fiery mobile Internet has led a new wave of the Internet, and Android has become the most popular mobile operating system in the mobile Internet era. While Android is popular, its own security problems are also becoming more and more prominent. For example, security loopholes are frequently exploited, the number of malicious applications is explosively increasing, and user privacy and property are facing serious security threats. Therefore, the research on the security of Android applications has important academic and application value. [0003] Android malware has had a great impact on the daily use of users. Common problems include: harassing calls and text messages, slow or dead mobile phones, unreasonable consumption of traffic, notification bars and pop-up ads, mal...

AI Technical Summary

Problems solved by technology

These problems are all caused by the malicious behavior of malware, which mainly include: a) by hijacking the SMS and dialing of the system, automatically dialing the deduction number in the background or sending a deduction SMS to maliciously absorb fees; b) silently obtaining private information in the background, Leaking private information to fraudulent gangs via the Internet or text messages to lure fraud; c) Influence user use by consuming system resources, locking mobile phones, etc., and profiting for malicious apps by destroying system rights; d) Stealing private information leads to account being banned Stealing and subsequent concurrency issues

[0004] For malware detection, there are currently some good detection technologies on the market and in the research field; the detection technologies of mainstream common commercial security detection software on the market use the signature database of known malicious samples to identify malicious apps, but due to Malicious samples mutate so quickly that the recognition accuracy of this detection method is not satisfactory

Images