Handling imbalanced data in intrusion detection using time weighted Adaboost support vector machine classifier and crossover boosted Dwarf Mongoose Optimization algorithm

Cybersecurity threats pose a serious challenge in the present day and age, and Intrusion Detection Systems (IDS) have emerged as an effective solution to counter these threats. In this paper, a novel IDS is proposed that captures data from the NSL-KDD dataset and are preprocessed. The Kernel Princip...

Celý popis

Podrobná bibliografie

Vydáno v:	Applied soft computing Ročník 167; s. 112327
Hlavní autoři:	Chandrasekaran, Hemalatha, Murugesan, Kanipriya, Mana, Suja Cherukullapurath, Barathi, Bhagavathi Kannu Uma Anu, Ramaswamy, Sumathy
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier B.V 01.12.2024
Témata:	AdaBoost Attacks, crossover Dwarf mongoose Imbalanced Data Intrusion detection Support vector machine and global exploitation Dwarf mongoose AdaBoost Support vector machine and global exploitation Attacks, crossover Intrusion detection Imbalanced Data
ISSN:	1568-4946
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!