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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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| Paper Title: | INTRUSION DETECTION SYSTEM |
| Authors Name: | B Anirudh , E Kavery , Rachana |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_189875
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| Published Paper Id: | IJRTI2405048 |
| Published In: | Volume 9 Issue 5, May-2024 |
| DOI: | |
| Abstract: | In the realm of cybersecurity, where safeguarding networks is paramount, machine learning (ML) has emerged as a crucial tool for intrusion detection. ML algorithms enable the classification of network traffic into normal patterns or indicative of specific attacks like Denial of Service (DoS), probing, User to Root (U2R), and Remote to Local (R2L) attacks. Using a dataset of network traffic attributes, we evaluate ML classifiers such as Naive Bayes, Decision Tree, Random Forest, Catboost, and XGBoost. Our rigorous evaluation methodology involves cross- validation to ensure robustness. We assess performance metrics like accuracy, precision, recall, and false alarm rates to identify the most effective approach for intrusion detection. This analysis aids cybersecurity practitioners in deploying ML-based intrusion detection systems effectively. Moreover, we extend our research to practical implementation by developing a real-time intrusion detection system (IDS). This system integrates packet sniffing with model inference, allowing continuous monitoring and classification of network traffic. In conclusion, our project contributes to enhancing intrusion detection capabilities by systematically evaluating ML algorithms' performance. By combining empirical analysis with practical implementation, we provide insights into effective strategies for fortifying defenses against cyber threats in today's dynamic threat landscape |
| Keywords: | cybersecurity, intrusion detection, machine learning, classifiers, network traffic, Denial of Service (DoS), probing, User to Root (U2R), Remote to Local (R2L), Naive Bayes, Decision Tree, Random Forest, Catboost, XGBoost, performance evaluation, real-time, packet sniffing |
| Cite Article: | "INTRUSION DETECTION SYSTEM", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.9, Issue 5, page no.325 - 331, May-2024, Available :http://www.ijrti.org/papers/IJRTI2405048.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: IJRTI2405048
Registration ID:189875
Published In: Volume 9 Issue 5, May-2024
DOI (Digital Object Identifier):
Page No: 325 - 331 Country: hyderabad, telangana, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2405048 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2405048 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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