Explainable artificial intelligence-based intrusion detection systems = Selgitaval tehisintellektil baseeruvad ründetuvastussüsteemid
autor
juhendaja
vastutusandmed
Rajesh Kalakoti ; [supervisor: Sven Nõmm, co-supervisor: Hayretdin Bahşi ; Tallinn University of Technology, School of Information Technologies, Department of Software Science]
dissertatsiooni liik
doktoritöö
ülikool/teadusasutus
Tallinna Tehnikaülikool
ilmumiskoht
Tallinn
kirjastus/väljaandja
ilmumisaasta
leheküljed
xi, 309 p. : ill
seeria-sari
Tallinn University of Technology. Doctoral thesis = Tallinna Tehnikaülikool. Doktoritöö ; 82/2025
vormimärksõna
ISSN
2585-6898
2585-6901 (PDF)
ISBN
978-9916-80-402-5 (PDF)
978-9916-80-401-8
märkused
Autori publikatsioonide nimekiri leheküljel viii
Bibliogr. lk. 116-126
Kokkuvõte eesti keeles
Kättesaadav ka võrguteavikuna
Autori CV inglise ja eesti keeles, lk. 307-309
Thesis (Ph.D. (Information and Communication Technology)) : Tallinn University of Technology, 2025
leitav
Open Access
Open Access
teaduspublikatsioon
teaduspublikatsioon
klassifikaator
TTÜ struktuuriüksus
keel
inglise
- In-depth feature selection for the statistical machine learning-based botnet detection in IoT networks
- Enhancing IoT botnet attack detection in SOCs with an explainable active learning framework
- Explainable federated learning for Botnet Detection in IoT networks
- Improving IoT security with explainable AI : quantitative evaluation of explainability for IoT botnet detection
- Improving transparency and explainability of deep learning based IoT botnet detection using explainable artificial intelligence (XAI)
- Explainable transformer-based intrusion detection in Internet of Medical Things (IoMT) networks
- Comprehensive Feature Selection for Machine Learning-Based Intrusion Detection in Healthcare IoMT Networks
- Evaluating Explainable AI for Deep Learning-Based Network Intrusion Detection System Alert Classification
- Federated Learning of Explainable AI(FedXAI) for deep learning-based intrusion detection in IoT networks
- Synthetic Data-Driven Explainability for Federated Learning-based Intrusion Detection System
- Exploring the Impact of Feature Selection on Non-Stationary Intrusion Detection Models in IoT Networks
Kalakoti, R. Explainable artificial intelligence-based intrusion detection systems = Selgitaval tehisintellektil baseeruvad ründetuvastussüsteemid. Tallinn : TalTech Press, 2025. xi, 309 p. : ill. (Tallinn University of Technology. Doctoral thesis = Tallinna Tehnikaülikool. Doktoritöö ; 82/2025).