Machine learning-based detection and characterization of evolving threats in mobile and IoT Systems = Masinõppepõhine arenevate ohtude tuvastamine ning kirjeldamine mobiilseadmete ja värkvõrkude jaoks
juhendaja
vastutusandmed
Alejandro Guerra Manzanares ; [supervisors: Hayretdin Bahsi, Sven Nõmm, Marcin Luckner ; Department of Software Science, School of Information Technologies, Tallinn University of Technology]
dissertatsiooni liik
doktoritöö
ülikool/teadusasutus
Tallinna Tehnikaülikool
ilmumiskoht
Tallinn
kirjastus/väljaandja
ilmumisaasta
seeria-sari
Tallinn University of Technology. Doctoral thesis = Tallinna Tehnikaülikool. Doktoritöö ; 54/2022
vormimärksõna
ISSN
2585-6898
ISBN
978-9949-83-898-1
märkused
Thesis (Ph.D. in Computer Science (Cybersecurity)) : Tallinn University of Technology, 2022
Autori publikatsioonide nimekiri leheküljel 9-10
Bibliogr. p. 132-140
Kokkuvõte eesti keeles
Autori CV inglise ja eesti keeles, lk. 412-415
Saadaval ka võrguteavikuna
leitav
Open Access
Open Access
teaduspublikatsioon
teaduspublikatsioon
klassifikaator
TTÜ struktuuriüksus
keel
inglise
- Hybrid feature selection models for machine learning based botnet detection in IoT networks
- In-depth feature selection and ranking for automated detection of mobile malware
- Time-frame analysis of system calls behavior in machine learning-based mobile malware detection
- Towards the integration of a post-hoc interpretation step into the machine learning workflow for IoT botnet detection
- MedBIoT : generation of an IoT Botnet Dataset in a medium-sized IoT network
- KronoDroid : time-based hybrid-featured dataset for effective android malware detection and characterization
- Android malware concept drift using system calls : detection, characterization and challenges
- Concept drift and cross-device behavior : challenges and implications for effective android malware detection
- Cross-device behavioral consistency : benchmarking and implications for effective android malware detection
- Leveraging the first line of defense : a study on the evolution and usage of android security permissions for enhanced android malware detection
- On the relativity of time : implications and challenges of data drift on long-term effective android malware detection
- Using MedBIoT dataset to build effective machine learning-based IoT botnet detection systems
- On the application of active learning for efficient and effective Iot botnet detection
- On the application of active learning to handle data evolution in Android malware detection
Guerra Manzanares, A. Machine learning-based detection and characterization of evolving threats in mobile and IoT Systems = Masinõppepõhine arenevate ohtude tuvastamine ning kirjeldamine mobiilseadmete ja värkvõrkude jaoks. Tallinn : TalTech Press, 2022. (Tallinn University of Technology. Doctoral thesis = Tallinna Tehnikaülikool. Doktoritöö ; 54/2022).