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ISSN 1004-9037
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Edited by: Editorial Board of Journal of Data Acquisition and Processing
P.O. Box 2704, Beijing 100190, P.R. China
Sponsored by: Institute of Computing Technology, CAS & China Computer Federation
Undertaken by: Institute of Computing Technology, CAS
Published by: SCIENCE PRESS, BEIJING, CHINA
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      09 May 2023, Volume 38 Issue 3
    Article

    MACHINE LEARNING TECHNOLOGY BASED DETECTION OF CYBER ATTACKS & NETWORK ATTACKS
    Bhagyashri Dhumal1, Prof. M. D. Rokade2, Dr. Sunil S. Khatal3
    Journal of Data Acquisition and Processing, 2023, 38 (3): 1301-1311 . 

    Abstract

    The increasing complexity and sophistication of cyber-attacks pose significant threats to network security and the confidentiality, integrity, and availability of sensitive data. To address this challenge, machine learning technology has emerged as a promising approach for the detection and mitigation of cyber-attacks. In this project, we aim to develop a machine learning-based system for the detection of cyber-attacks and network attacks. The project involves the collection and preprocessing of a diverse dataset comprising network traffic data, including both normal and attack instances. Various machine learning algorithms, including supervised and unsupervised techniques, will be explored to train models on the dataset. Feature selection and engineering methods will be employed to extract relevant features from the network traffic data. The trained models will be evaluated using appropriate metrics to assess their performance in accurately detecting cyber-attacks and distinguishing them from normal network behavior. The project will also investigate ensemble methods to enhance the robustness and accuracy of the detection system. Furthermore, the project aims to incorporate real-time monitoring capabilities to enable the system to detect and respond to emerging attacks promptly. A comprehensive evaluation will be conducted on a testbed environment, simulating various attack scenarios to validate the effectiveness and efficiency of the developed system. The outcome of this project will provide valuable insights into the application of machine learning technology in detecting and mitigating cyber-attacks. The developed system has the potential to enhance network security and protect critical infrastructures from the ever-evolving threat landscape. The results of this research will contribute to the advancement of machine learning-based security solutions and serve as a foundation for future developments in the field of cyber-security.

    Keyword

    Network Protocols, Wireless Network, Cyber-Crime, Cyber-Security System, Attacks, Intrusion Detection Attack (IDS), SQL Injection etc.


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