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Quick and Easy On-Device Malware Detection on Android(arxiv.org)

24 points by msaiman 1 year ago flag hide 10 comments

bigtechdude 1 year ago next
Fascinating approach to on-device malware detection! I wonder what false-positive rate they have.
- securityguru 1 year ago next
  The false-positive rate would mostly depend on the type of machine learning algorithms and techniques used for detecting malware. I'm curious about whether they experimented with deep learning models like LSTM and CNN.
andthecode 1 year ago prev next
As someone who does Android dev, I'm glad this approach is quick and easy to integrate! Can't wait to apply this to some of my apps.
- microkernelnerd 1 year ago next
  One thing to be cautious about with this method, is the performance overhead of constantly analyzing APKs on-device. It's essential to make sure that the system remains resource-friendly.
antivirusspecialist 1 year ago prev next
In my experience, statistics blocklists work better in actual real-world scenarios than merely hunting signatures. Dynamic analysis is the future for malware detection!
- h4ckth3box 1 year ago next
  While I agree with what you've mentioned, the limitation of on-device detection methods does arise when there's no internet connection to fetch the blocklists or for doing dynamic analysis.
aiwhiz 1 year ago prev next
This paper makes me wonder if researchers will soon start using on-device ML models to detect malware and phishing on other platforms such as iOS and desktop systems. Exciting times ahead!
- ximenatrevino 1 year ago next
  I completely agree, it's just a matter of time before implementing ML models for on-device phishing and malware detection on other platforms like a norm.
securemaven 1 year ago prev next
How would this technique display alerts to end-users without causing unnecessary panic when they see a warning in their apps? False-positive alerts can negatively impact user trust.
- geekyminds 1 year ago next
  Enable alerts only when the likelihood of malware is considerably high and definitely not whenever vastly different behaviors are observed (as this could be an update or a feature change).

bigtechdude 1 year ago next
Fascinating approach to on-device malware detection! I wonder what false-positive rate they have.
- securityguru 1 year ago next
  The false-positive rate would mostly depend on the type of machine learning algorithms and techniques used for detecting malware. I'm curious about whether they experimented with deep learning models like LSTM and CNN.
andthecode 1 year ago prev next
As someone who does Android dev, I'm glad this approach is quick and easy to integrate! Can't wait to apply this to some of my apps.
- microkernelnerd 1 year ago next
  One thing to be cautious about with this method, is the performance overhead of constantly analyzing APKs on-device. It's essential to make sure that the system remains resource-friendly.
antivirusspecialist 1 year ago prev next
In my experience, statistics blocklists work better in actual real-world scenarios than merely hunting signatures. Dynamic analysis is the future for malware detection!
- h4ckth3box 1 year ago next
  While I agree with what you've mentioned, the limitation of on-device detection methods does arise when there's no internet connection to fetch the blocklists or for doing dynamic analysis.
aiwhiz 1 year ago prev next
This paper makes me wonder if researchers will soon start using on-device ML models to detect malware and phishing on other platforms such as iOS and desktop systems. Exciting times ahead!
- ximenatrevino 1 year ago next
  I completely agree, it's just a matter of time before implementing ML models for on-device phishing and malware detection on other platforms like a norm.
securemaven 1 year ago prev next
How would this technique display alerts to end-users without causing unnecessary panic when they see a warning in their apps? False-positive alerts can negatively impact user trust.
- geekyminds 1 year ago next
  Enable alerts only when the likelihood of malware is considerably high and definitely not whenever vastly different behaviors are observed (as this could be an update or a feature change).