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All Journal The Indonesian Journal of Computer Science
Andy Victor Amanoul
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Enhanced Intrusion Detection System Using Deep Learning Algorithms : A Review Andy Victor Amanoul; Adnan Mohsin Abdulazeez
The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i3.4002

Abstract

Intrusion Detection Systems (IDS) are crucial for protecting network infrastructures from advanced cyber threats. Traditional IDS, largely reliant on static signature detection, fail to effectively counter novel cyber attacks, leading to high false positive rates and missed zero-day exploits. This study investigates the integration of deep learning technologies into IDS to enhance their detection capabilities. By employing advanced deep learning frameworks, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) and other algorithms , the research explores their efficacy in identifying complex data patterns and anomalies. Furthermore, the use of big data analytics is assessed for its potential to significantly augment the predictive power of these systems, aiming to set new benchmarks in cybersecurity defenses tailored for contemporary threats.
Co-Authors Adnan Mohsin Abdulazeez