Summary of Machine Learning-based Android Intrusion Detection System, by Madiha Tahreem et al.
Machine Learning-based Android Intrusion Detection System
by Madiha Tahreem, Ifrah Andleeb, Bilal Zahid Hussain, Arsalan Hameed
First submitted to arxiv on: 5 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The application of machine learning classification algorithms for securing android APK files is investigated in this paper. The increasing threats to smart devices, such as Phishing, Spyware, SMS Fraud, Bots, Banking-Trojans, and others, necessitate robust security measures. To combat these malicious data streams, the authors propose a classification approach that categorizes newly installed applications into malicious or non-malicious domains based on various parameters. By leveraging machine learning techniques, this system can accurately detect and prevent potential attacks, thus safeguarding the Android operating system from illegal activities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Android devices are becoming increasingly popular, but they’re also vulnerable to cyber threats like malware and phishing scams. This paper uses machine learning algorithms to analyze and classify new apps installed on these devices. By identifying malicious software, this approach can help protect users’ data and prevent attacks. The technique is effective in detecting potential dangers and preventing harm. |
Keywords
» Artificial intelligence » Classification » Machine learning
