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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM38099

Analyzing Cryptographic Techniques and Machine Learning Algorithms for Crime Prediction

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM38099

Volume:

8 |

10 |

Oct 2024

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM38099

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

USV

Udayveer Singh Virk

DV

Devansh Verma

GS

Gagandeep Singh

PSL

Prof. Sheetal Laroiya

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Abstract

This report discusses cryptography techniques. Network security is defined as “keeping information hidden and secure from unauthorized users,” whereas cryptography is defined as “the science of data protection.” The Fundamental Requirements for Data Transmission are addressed in this work and as well as security attacks such as Data Transmission Interruption, Interception, and Modification. The Cryptographic Framework is explained using a generalized function, in which data is encrypted and decrypted using techniques such as the RSA algorithm, Hash Functions, and other cryptographic algorithms.SVM algorithms to analysis and predict the future cyber crime. This report introduces Cryptography Techniques. Cryptography is “The science of protecting data” & Network Security “keeping information private and Secure from unauthorized Users”. This paper gives the Fundamental Requirements for the Data Transmission, the security attacks like Interruption, Interception and Modification of the data Transmission.

KEYWORDS

cryptography cybersecurity machine learning cybercrime prediction

Cite This Article

Udayveer Singh Virk, Devansh Verma, Gagandeep Singh, Prof. Sheetal Laroiya, "Analyzing Cryptographic Techniques and Machine Learning Algorithms for Crime Prediction," International Journal of Scientific Research in Engineering and Management, vol. 8, no. 10, Oct 2024. https://doi.org/10.55041/IJSREM38099

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Publication Date

10/22/2024

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Double-Blind Peer Review

Review Rounds: 2

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5

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The authors declare no conflict of interest related to this research work.

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