DEEP ENSEMBLE-BASED EFFICIENT FRAMEWORK FOR NETWORK ATTACK DETECTION

Abstract

Networks are essential for numerous sports,
including corporate operations, instructional hobbies,
and each day long-distance conversation. Although
networks provide several advantages, additionally
they pose safety risks that can jeopardise statistics
confidentiality, integrity, and privacy. Network
threats, including virus, hacking, and phishing, are
increasing, leading to enormous economic and
reputational harm. The assignment proposes the
introduction of an automatic system utilizing artificial
intelligence (AI) to mitigate those protection
vulnerabilities. This generation is designed to
effectively become aware of and protect in opposition
to community threats, for this reason improving the
security of statistics and networked structures. The
mission implements an ensemble model that integrates
3 deep learning architectures: LSTM, RNN, and GRU.
These models collaborate employing majority
balloting standards to get extended accuracy inside the
identification of network attacks, therefore making
sure strong safety for networked environments.