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INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016 open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15 |
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| Paper Title: | Enhancing Cyber Attack Resilience Using Machine Learning |
| Authors Name: | K. Aakanksha Reddy , S. Radha , G. Dhruti , CH. Shyli |
| Unique Id: | IJSDR2404202 |
| Published In: | Volume 9 Issue 4, April-2024 |
| Abstract: | The project focuses on bolstering threat detection efficacy within Internet of Things (IoT) systems through an intelligent approach. IoT systems, comprising devices, sensors, networks, and software, often grapple with security vulnerabilities exploitable by attackers. Leveraging machine learning algorithms and principal component analysis (PCA), the study targets the identification of Distributed Denial of Service (DDoS) attacks, a prevalent menace to IoT systems. Principal component analysis aids in data dimensionality reduction, streamlining datasets while preserving critical information. Evaluation encompasses metrics like accuracy, precision, recall, and F1-Score to gauge model performance accurately. Employing CICIDS 2017 and CSE-CIC-IDS 2018 datasets, the models are rigorously trained and tested. The proposed approach exhibits superior performance and diminished training time compared to prior methodologies, showcasing its efficacy in bolstering threat detection within IoT systems. We further enhance, our project integrates ensemble techniques such as Voting Classifier (RF + Adaboost) and Stacking Classifier (RF + MLP with LightGBM), culminating in a refined and precise predictive model achieving 100 percentage of accuracy. This research not only advances threat detection capabilities but also underscores the potential of ensemble methods in fortifying IoT system security. |
| Keywords: | Machine learning, principal component analysis, Internet of Things, DDoS attack. |
| Cite Article: | "Enhancing Cyber Attack Resilience Using Machine Learning", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.9, Issue 4, page no.1386 - 1390, April-2024, Available :http://www.ijsdr.org/papers/IJSDR2404202.pdf |
| Downloads: | 000338173 |
| Publication Details: | Published Paper ID: IJSDR2404202 Registration ID:211091 Published In: Volume 9 Issue 4, April-2024 DOI (Digital Object Identifier): Page No: 1386 - 1390 Publisher: IJSDR | www.ijsdr.org ISSN Number: 2455-2631 |
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