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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | The Power of Data: Machine Learning in Cyber Attack Classification |
| Authors Name: | Bandi Harshavardhan Reddy , Tadapaneni Snehitha , Gopa Laasya Lalitha Priya , Mohana Sundari L |
| Download E-Certificate: | Download |
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IJNRD_219551
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| Published Paper Id: | IJNRD2404898 |
| Published In: | Volume 9 Issue 4, April-2024 |
| DOI: | |
| Abstract: | Classifying cyber attacks by utilizing supervised machine learning algorithms. The model is designed to classify diverse types of Cyber- attacks by using a large dataset with various factors which collectively determine the type of attack. Rather than malware, phishing, and Distributed Denial of Service attacks, we are detecting attacks like Brute Force attack, HTTP-DoS attack, ICMP flood attack, Port Scan, Web Crawling and if it is normal. Feature extraction techniques are applied to both network traffic data and behavioral attributes, facilitating the training of a robust classification model. We have used various supervised machine learning algorithms like Gaussian Naive Bayes Classifier, Passive Aggressive Classifier, Decision Tree, Random Forest, Logistic Regression and Gradient Boosting Classifier. The training process involves labeling historical attack instances, enabling the model to discern intricate patterns and subtle differentiators among attack types. The accuracy tells us how precisely the model is predicting the attack which is trained. We have made a comparison study which compares all the top most machine learning algorithms used for classification to know which algorithm is giving more accurate output and displayed in the report.Through this research, our thesis represents the proactive identification and mitigation of cyber-attacks, ultimately fortifying digital security frameworks. |
| Keywords: | Supervised Machine Learning, Cyber attacks, Feature extraction, Training process, Comparison study, accuracy |
| Cite Article: | "The Power of Data: Machine Learning in Cyber Attack Classification", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 4, page no.i890-i908, April-2024, Available :http://www.ijnrd.org/papers/IJNRD2404898.pdf |
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| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 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:IJNRD2404898 Registration ID: 219551 Published In: Volume 9 Issue 4, April-2024 DOI (Digital Object Identifier): Page No: i890-i908 Country: Tirupati, Andhra pradesh, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2404898 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2404898 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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