Open AccessInductive and Transductive Transfer Learning for Zero-day Attack Detection
Author(s) -
Nerella Sameera,
Andhavarapu Bhanusri,
M. Shashi
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.k1758.0981119
Subject(s) - zero (linguistics) , computer science , transfer of learning , domain adaptation , artificial intelligence , domain (mathematical analysis) , machine learning , labeled data , pattern recognition (psychology) , mathematics , mathematical analysis , philosophy , linguistics , classifier (uml)
Upon application of supervised machine learning techniques Intrusion Detection Systems (IDSs) are successful in detecting known attacks as they use predefined attack signatures. However, detecting zero-day attacks is challenged because of the scarcity of the labeled instances for zero-day attacks. Advanced research on IDS applies the concept of Transfer Learning (TL) to compensate the scarcity of labeled instances of zero-day attacks by making use of abundant labeled instances present in related domain(s). This paper explores the potential of Inductive and Transductive transfer learning for detecting zero-day attacks experimentally, where inductive TL deals with the presence of minimal labeled instances in the target domain and transductive TL deals with the complete absence of labeled instances in the target domain. The concept of domain adaptation with manifold alignment (DAMA) is applied in inductive TL where the variant of DAMA is proposed to handle transductive TL due to non-availability of labeled instances. NSL_KDD dataset is used for experimentation