Matches in SemOpenAlex for { <https://semopenalex.org/work/W4387587680> ?p ?o ?g. }
Showing items 1 to 67 of
67
with 100 items per page.
- W4387587680 endingPage "1" @default.
- W4387587680 startingPage "1" @default.
- W4387587680 abstract "In the dynamic landscape of cyber threats, multi-stage malware botnets have surfaced as significant threats of concern. These sophisticated threats can exploit Internet of Things (IoT) devices to undertake an array of cyberattacks, ranging from basic infections to complex operations such as phishing, cryptojacking, and distributed denial of service (DDoS) attacks. Existing machine learning solutions are often constrained by their limited generalizability across various datasets and their inability to adapt to the mutable patterns of malware attacks in real world environments, a challenge known as model drift. This limitation highlights the pressing need for adaptive Intrusion Detection Systems (IDS), capable of adjusting to evolving threat patterns and new or unseen attacks. This paper introduces MalBoT-DRL, a robust malware botnet detector using deep reinforcement learning. Designed to detect botnets throughout their entire lifecycle, MalBoT-DRL has better generalizability and offers a resilient solution to model drift. This model integrates damped incremental statistics with an attention rewards mechanism, a combination that has not been extensively explored in literature. This integration enables MalBoT-DRL to dynamically adapt to the ever-changing malware patterns within IoT environments. The performance of MalBoT-DRL has been validated via trace-driven experiments using two representative datasets, MedBIoT and N-BaIoT, resulting in exceptional average detection rates of 99.80% and 99.40% in the early and late detection phases, respectively. To the best of our knowledge, this work introduces one of the first studies to investigate the efficacy of reinforcement learning in enhancing the generalizability of IDS." @default.
- W4387587680 created "2023-10-13" @default.
- W4387587680 creator A5011631485 @default.
- W4387587680 creator A5059656651 @default.
- W4387587680 creator A5072706685 @default.
- W4387587680 creator A5088873861 @default.
- W4387587680 date "2023-01-01" @default.
- W4387587680 modified "2023-10-14" @default.
- W4387587680 title "MalBoT-DRL: Malware Botnet Detection Using Deep Reinforcement Learning in IoT Networks" @default.
- W4387587680 doi "https://doi.org/10.1109/jiot.2023.3324053" @default.
- W4387587680 hasPublicationYear "2023" @default.
- W4387587680 type Work @default.
- W4387587680 citedByCount "0" @default.
- W4387587680 crossrefType "journal-article" @default.
- W4387587680 hasAuthorship W4387587680A5011631485 @default.
- W4387587680 hasAuthorship W4387587680A5059656651 @default.
- W4387587680 hasAuthorship W4387587680A5072706685 @default.
- W4387587680 hasAuthorship W4387587680A5088873861 @default.
- W4387587680 hasBestOaLocation W43875876801 @default.
- W4387587680 hasConcept C105795698 @default.
- W4387587680 hasConcept C110875604 @default.
- W4387587680 hasConcept C119857082 @default.
- W4387587680 hasConcept C136764020 @default.
- W4387587680 hasConcept C154945302 @default.
- W4387587680 hasConcept C165696696 @default.
- W4387587680 hasConcept C22735295 @default.
- W4387587680 hasConcept C27158222 @default.
- W4387587680 hasConcept C33923547 @default.
- W4387587680 hasConcept C35525427 @default.
- W4387587680 hasConcept C38652104 @default.
- W4387587680 hasConcept C38822068 @default.
- W4387587680 hasConcept C41008148 @default.
- W4387587680 hasConcept C541664917 @default.
- W4387587680 hasConcept C97541855 @default.
- W4387587680 hasConceptScore W4387587680C105795698 @default.
- W4387587680 hasConceptScore W4387587680C110875604 @default.
- W4387587680 hasConceptScore W4387587680C119857082 @default.
- W4387587680 hasConceptScore W4387587680C136764020 @default.
- W4387587680 hasConceptScore W4387587680C154945302 @default.
- W4387587680 hasConceptScore W4387587680C165696696 @default.
- W4387587680 hasConceptScore W4387587680C22735295 @default.
- W4387587680 hasConceptScore W4387587680C27158222 @default.
- W4387587680 hasConceptScore W4387587680C33923547 @default.
- W4387587680 hasConceptScore W4387587680C35525427 @default.
- W4387587680 hasConceptScore W4387587680C38652104 @default.
- W4387587680 hasConceptScore W4387587680C38822068 @default.
- W4387587680 hasConceptScore W4387587680C41008148 @default.
- W4387587680 hasConceptScore W4387587680C541664917 @default.
- W4387587680 hasConceptScore W4387587680C97541855 @default.
- W4387587680 hasLocation W43875876801 @default.
- W4387587680 hasOpenAccess W4387587680 @default.
- W4387587680 hasPrimaryLocation W43875876801 @default.
- W4387587680 hasRelatedWork W1996006176 @default.
- W4387587680 hasRelatedWork W2038807247 @default.
- W4387587680 hasRelatedWork W2097156747 @default.
- W4387587680 hasRelatedWork W2147314218 @default.
- W4387587680 hasRelatedWork W2292210693 @default.
- W4387587680 hasRelatedWork W2294483539 @default.
- W4387587680 hasRelatedWork W2378449000 @default.
- W4387587680 hasRelatedWork W2559738661 @default.
- W4387587680 hasRelatedWork W2929621094 @default.
- W4387587680 hasRelatedWork W4285325964 @default.
- W4387587680 isParatext "false" @default.
- W4387587680 isRetracted "false" @default.
- W4387587680 workType "article" @default.