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  Published Paper Details:

  Paper Title

A REVIEW OF MACHINE LEARNING-BASED TECHNIQUES FOR DETECTING CYBER ATTACKS OVER A NETWORK

  Authors

  Dr.T.Ravindar Reddy,  A.Ragavendra Rao,  Amitha Mishra,  Amireddy Manasa

  Keywords

Machine Learning, KDD, Cyber Security, Network, SVM, Random Forest.

  Abstract


In contrast to the past, advancements in computer and communications technology have resulted in substantial and extensive changes. Despite its benefits, innovation can also generate problems for individuals, businesses, and governments. Examples include data security, data protection, and other concepts. Digital fear-based authoritarianism, a severe problem that plagues contemporary society, is founded on these problems. The level of digital fear has risen to the point where it poses a threat to both public safety and national security as a result of the support and empowerment given to groups like criminal gangs, skilled hackers, and digital activists. To keep the business safe from internet attacks, intrusion detection systems (IDS) have also been created. The most current CICIDS2017 dataset accuracy ratings for port sweep detection were 97.80 percent and 69.79 percent. The SVM computation, which is currently in use, had an effect on these percentages. Instead of using SVM, we may use alternative algorithms, such as random forest, CNN, and ANN, which will provide accuracies similar to those of SVM (93.29%), CNN (63.52%), and Random Forest (99.93%).

  IJCRT's Publication Details

  Unique Identification Number - IJCRTV020039

  Paper ID - 231886

  Page Number(s) - 221-226

  Pubished in - Volume 5 | Issue 3 | August 2017

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  Dr.T.Ravindar Reddy,  A.Ragavendra Rao,  Amitha Mishra,  Amireddy Manasa,   "A REVIEW OF MACHINE LEARNING-BASED TECHNIQUES FOR DETECTING CYBER ATTACKS OVER A NETWORK", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.5, Issue 3, pp.221-226, August 2017, Available at :http://www.ijcrt.org/papers/IJCRTV020039.pdf

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ISSN: 2320-2882
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Journal Starting Year (ESTD) : 2013
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ISSN and 7.97 Impact Factor Details


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ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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