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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
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Volume Published : 10
Issue Published : 115
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| Paper Title: | Botnet Attack Detection In IOT Networks Using Deep Learning |
| Authors Name: | N.Naga Raju , A.Vijaya Prajwala , P.Naga Kavya Sri , B.Sri Chetan , G.Krishna Chaitanya |
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IJRTI_189721
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| Published Paper Id: | IJRTI2404117 |
| Published In: | Volume 9 Issue 4, April-2024 |
| DOI: | |
| Abstract: | Abstract— In the landscape of cybersecurity threats, botnets stand out as formidable weapons wielded by malicious actors to compromise network integrity, steal data, launch distributed denial-of-service (DDoS) attacks, and wreak havoc on unsuspecting systems. Among the various targets susceptible to these assaults, Internet of Things (IoT) networks emerge as particularly vulnerable due to their distributed nature and often lax security protocols. The ability to swiftly detect and mitigate these botnet intrusions is paramount to preserving the integrity and functionality of IoT ecosystems. Traditional methods for identifying botnet activities in IoT networks have proven to be both resource-intensive and less accurate, especially when faced with the challenges of handling large volumes of network traffic data. However, the advent of advanced machine learning techniques, particularly deep learning algorithms, presents a promising avenue for enhancing the efficiency and accuracy of botnet detection. In this paper, we propose a novel approach that harnesses the power of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) models in tandem with dimensionality reduction using Principal Component Analysis (PCA) to detect the presence of botnets within IoT networks. Our system not only achieves an impressive 96% accuracy rate but also minimizes resource consumption, making it a practical and effective solution for real-time botnet detection in IoT environments. |
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| Cite Article: | "Botnet Attack Detection In IOT Networks Using Deep Learning", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.9, Issue 4, page no.846 - 855, April-2024, Available :http://www.ijrti.org/papers/IJRTI2404117.pdf |
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| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2404117
Registration ID:189721
Published In: Volume 9 Issue 4, April-2024
DOI (Digital Object Identifier):
Page No: 846 - 855 Country: Vijayawada, NTR district, Andhra Pradesh, India Research Area: Information Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2404117 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2404117 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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