Tesi etd-09062022-133039 |
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Tipo di tesi
Tesi di laurea magistrale
Autore
SIMONI, MARCO
URN
etd-09062022-133039
Titolo
Graph-based android malware categorization based on Convolutional Neural Networks
Dipartimento
INGEGNERIA DELL'INFORMAZIONE
Corso di studi
ARTIFICIAL INTELLIGENCE AND DATA ENGINEERING
Relatori
relatore Prof. Cimino, Mario Giovanni Cosimo Antonio
relatore Alfeo, Antonio Luca
relatore Saracino, Andrea
relatore Alfeo, Antonio Luca
relatore Saracino, Andrea
Parole chiave
- android
- cnn
- cybersecurity
- graph
- machine learning
- malware
- rever engineering
Data inizio appello
23/09/2022
Consultabilità
Non consultabile
Data di rilascio
23/09/2025
Riassunto
My study will focus on the Classification of APKs in these different cate-
gories:
1. Benign APK : Harmless or well intentioned application, the opposite of
malicious APKs.
2. File Infector APK : A file infector is a type of malware that has the
capability to propagate by attaching its code to other programs or
files.
3. Riskware APK : Riskware is any potentially unwanted application that
is not classified as malware, but may utilize system resources in an
undesirable or annoying manner, and/or may pose a security risk.
4. Trojan APK : A trojan is a type of malware that performs activites with-
out the user’s knowledge.
5. Adware APK : Adware typically display advertising content to the user.
gories:
1. Benign APK : Harmless or well intentioned application, the opposite of
malicious APKs.
2. File Infector APK : A file infector is a type of malware that has the
capability to propagate by attaching its code to other programs or
files.
3. Riskware APK : Riskware is any potentially unwanted application that
is not classified as malware, but may utilize system resources in an
undesirable or annoying manner, and/or may pose a security risk.
4. Trojan APK : A trojan is a type of malware that performs activites with-
out the user’s knowledge.
5. Adware APK : Adware typically display advertising content to the user.
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