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Malware Detection In Android Systems: Using Machine Learning and Deep Learning

  • Unique Paper ID: 163159
  • Volume: 10
  • Issue: 11
  • PageNo: 2879-2884
  • Abstract:
  • The increasing acceptance of the Android platform has led to a surge of malicious software, which poses an extreme risk to user confidentiality and device security. Traditional signature-based detection systems cannot keep up with the rapid expansion and sophistication of Android malware, which calls for more creative and adaptable protection measures. This paper investigates state-of-the art machine learning strategies for An- droid malware recognition. It discusses how various algorithms can be applied, how well they extract and learn from different feature sets, and how challenging it can be to deal with malware that is obfuscated and polymorphic. It also discusses malware developers adversarial tactics and the limitations of machine learning approaches in this never-ending arms race. To increase the precision of detection and response. We can observe numerous effective antimalware present in the internet which can effectively tackles the malware attacks and threats. So . I have examine some of them with their working algorithms from the internet to stop such attacks. According to our research from the internet, some deep learning techniques and android malware samples are essential for defending such malware attacks. Our motive is to find the hazardous malwares before installation of malware applications in android systems. This approach can detect whether the android application is infected or not, which reduces the severe risks and damages.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 10
  • Issue: 11
  • PageNo: 2879-2884

Malware Detection In Android Systems: Using Machine Learning and Deep Learning

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