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Computational System to Classify Cyber Crime Offenses using Machine Learning

Author

Listed:
  • Rupa Ch

    (Department of Computer Science, VR Siddhartha Engineering College, Vijayawada 520007, India)

  • Thippa Reddy Gadekallu

    (School of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, India)

  • Mustufa Haider Abidi

    (Raytheon Chair for Systems Engineering, Advanced Manufacturing Institute, King Saud University, Riyadh 11421, Saudi Arabia)

  • Abdulrahman Al-Ahmari

    (Raytheon Chair for Systems Engineering, Advanced Manufacturing Institute, King Saud University, Riyadh 11421, Saudi Arabia)

Abstract

Particularly in the last decade, Internet usage has been growing rapidly. However, as the Internet becomes a part of the day to day activities, cybercrime is also on the rise. Cybercrime will cost nearly $6 trillion per annum by 2021 as per the cybersecurity ventures report in 2020. For illegal activities, cybercriminals utilize any network computing devices as a primary means of communication with a victims’ devices, so attackers get profit in terms of finance, publicity and others by exploiting the vulnerabilities over the system. Cybercrimes are steadily increasing daily. Evaluating cybercrime attacks and providing protective measures by manual methods using existing technical approaches and also investigations has often failed to control cybercrime attacks. Existing literature in the area of cybercrime offenses suffers from a lack of a computation methods to predict cybercrime, especially on unstructured data. Therefore, this study proposes a flexible computational tool using machine learning techniques to analyze cybercrimes rate at a state wise in a country that helps to classify cybercrimes. Security analytics with the association of data analytic approaches help us for analyzing and classifying offenses from India-based integrated data that may be either structured or unstructured. The main strength of this work is testing analysis reports, which classify the offenses accurately with 99 percent accuracy.

Suggested Citation

  • Rupa Ch & Thippa Reddy Gadekallu & Mustufa Haider Abidi & Abdulrahman Al-Ahmari, 2020. "Computational System to Classify Cyber Crime Offenses using Machine Learning," Sustainability, MDPI, vol. 12(10), pages 1-16, May.
  • Handle: RePEc:gam:jsusta:v:12:y:2020:i:10:p:4087-:d:359076
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    References listed on IDEAS

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    1. Harshita Patel & Dharmendra Singh Rajput & G Thippa Reddy & Celestine Iwendi & Ali Kashif Bashir & Ohyun Jo, 2020. "A review on classification of imbalanced data for wireless sensor networks," International Journal of Distributed Sensor Networks, , vol. 16(4), pages 15501477209, April.
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    Cited by:

    1. William Akoto, 2024. "Who spies on whom? Unravelling the puzzle of state-sponsored cyber economic espionage," Journal of Peace Research, Peace Research Institute Oslo, vol. 61(1), pages 59-71, January.
    2. Mustufa Haider Abidi & Muneer Khan Mohammed & Hisham Alkhalefah, 2022. "Predictive Maintenance Planning for Industry 4.0 Using Machine Learning for Sustainable Manufacturing," Sustainability, MDPI, vol. 14(6), pages 1-27, March.

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