Machine Learning-based Android Intrusion Detection System
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916508908650496 |
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| author | Tahreem, Madiha Andleeb, Ifrah Hussain, Bilal Zahid Hameed, Arsalan |
| author_facet | Tahreem, Madiha Andleeb, Ifrah Hussain, Bilal Zahid Hameed, Arsalan |
| contents | The android operating system is being installed in most of the smart devices. The introduction of intrusions in such operating systems is rising at a tremendous rate. With the introduction of such malicious data streams, the smart devices are being subjected to various attacks like Phishing, Spyware, SMS Fraud, Bots and Banking-Trojans and many such. The application of machine learning classification algorithms for the security of android APK files is used in this paper. Each apk data stream was marked to be either malicious or non malicious on the basis of different parameters. The machine learning classification techniques are then used to classify whether the newly installed applications' signature falls within the malicious or non-malicious domain. If it falls within the malicious category, appropriate action can be taken, and the Android operating system can be shielded against illegal activities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_03894 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Machine Learning-based Android Intrusion Detection System Tahreem, Madiha Andleeb, Ifrah Hussain, Bilal Zahid Hameed, Arsalan Machine Learning Artificial Intelligence The android operating system is being installed in most of the smart devices. The introduction of intrusions in such operating systems is rising at a tremendous rate. With the introduction of such malicious data streams, the smart devices are being subjected to various attacks like Phishing, Spyware, SMS Fraud, Bots and Banking-Trojans and many such. The application of machine learning classification algorithms for the security of android APK files is used in this paper. Each apk data stream was marked to be either malicious or non malicious on the basis of different parameters. The machine learning classification techniques are then used to classify whether the newly installed applications' signature falls within the malicious or non-malicious domain. If it falls within the malicious category, appropriate action can be taken, and the Android operating system can be shielded against illegal activities. |
| title | Machine Learning-based Android Intrusion Detection System |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2412.03894 |