MALICIOUS ACTIVITY DETECTION

Registration ID: IJNRD_190480

Published ID: IJNRD2304157

DOI: Click Here to Get

Annesha Senguptha , Dr D RamaKrishna , P Krishna Aditya , K Vijay Naga Kumar , Srajan Sadal

Because of the rapid changes and variations in the internet over the past several years, the accuracy in detecting harmful behaviour online may be seen as a highly complex issue. In terms of spotting harmful behaviour, classification algorithms' potential is thriving. This research study adds to the use of several categorization algorithms for the purpose of detecting harmful activity in various internet zones. Algorithms for categorization include decision trees, support vector machines, and random forests. The information, which includes a number of characteristics, was gathered from several cyber security research organisations. The recall, precision, f1-score accuracy, and execution time of the classification algorithms were evaluated and trained using a variety of testing and training conditions. Decision trees are among the quickest models to run in terms of execution time, and decision tree classifiers are the superior choice in terms of run time.

Published Paper   E-Certificate

Annesha Senguptha, Dr D RamaKrishna, P Krishna Aditya, K Vijay Naga Kumar, & Srajan Sadal (April-2023). MALICIOUS ACTIVITY DETECTION. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(4), b477-b483. https://ijnrd.org/papers/IJNRD2304157.pdf

Volume 8 Issue 4, April-2023

Pages : b477-b483

Paper Reg. ID: IJNRD_190480

Published Paper Id: IJNRD2304157

Downloads: 000121982

Research Area: Computer Science & Technology 

Country: Visakhapatnam, Andhra Pradesh, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2304157.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2304157

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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