Article: An empirical approach with machine intelligence for identification of threats: a cyber security 4.0 approach for safe communications Journal: International Journal of Cybernetics and Cyber-Physical Systems (IJCCPS) 2024 Vol.1 No.4 pp.289 - 320 Abstract: Cyber security has become a major concern in this digital era. Since, the cyber-attacks and their types are increasing at an immense rate, it is not humanly possible to monitor, identify and take actions against the attacks. With the current automation systems majorly relying on supervised learning algorithms where they have already seen the type of attacks to monitor and manage the attacks, these systems have been rendered inefficient by zero-day attacks. It is essential that the research teams start realising the immense potential of AI and utilise it to its full potential in the field of cyber security. If correctly applied, AI can help to detect and deal with the cyber attacks more efficiently and can help protect users that are not very security conscious and are not aware about the dangers of these security breaches. The authors have decided to utilise machine learning algorithms like Decision Trees and Knowledge Discovery in Database (KDD) to detect zero-day attacks as well as handle other common cyber attacks, the authors' team have designed an AI-based cyber security system that detects the attacks based on the anomalies occurring in the network usage, port usage and packet analysis. Inderscience Publishers - linking academia, business and industry through research

Title: An empirical approach with machine intelligence for identification of threats: a cyber security 4.0 approach for safe communications

Authors: Rohit Rastogi; Vaibhav Sharma; Vaibhav Gupta; Tushar Gupta

Addresses: Department of CSE, ABES Engineering College Ghaziabad, Ghaziabad, Uttar Pradesh, India ' Department of CSE, ABES Engineering College Ghaziabad, Ghaziabad, Uttar Pradesh, India ' Department of CSE, ABES Engineering College Ghaziabad, Ghaziabad, Uttar Pradesh, India ' Department of CSE, ABES Engineering College Ghaziabad, Ghaziabad, Uttar Pradesh, India

Abstract: Cyber security has become a major concern in this digital era. Since, the cyber-attacks and their types are increasing at an immense rate, it is not humanly possible to monitor, identify and take actions against the attacks. With the current automation systems majorly relying on supervised learning algorithms where they have already seen the type of attacks to monitor and manage the attacks, these systems have been rendered inefficient by zero-day attacks. It is essential that the research teams start realising the immense potential of AI and utilise it to its full potential in the field of cyber security. If correctly applied, AI can help to detect and deal with the cyber attacks more efficiently and can help protect users that are not very security conscious and are not aware about the dangers of these security breaches. The authors have decided to utilise machine learning algorithms like Decision Trees and Knowledge Discovery in Database (KDD) to detect zero-day attacks as well as handle other common cyber attacks, the authors' team have designed an AI-based cyber security system that detects the attacks based on the anomalies occurring in the network usage, port usage and packet analysis.

Keywords: supervised learning; unsupervised learning; KDD; knowledge discovery in database; phishing; smishing; DDoS.

DOI: 10.1504/IJCCPS.2024.145825

International Journal of Cybernetics and Cyber-Physical Systems, 2024 Vol.1 No.4, pp.289 - 320

Received: 06 Sep 2023
Accepted: 16 Mar 2024
Published online: 25 Apr 2025 *

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Title: An empirical approach with machine intelligence for identification of threats: a cyber security 4.0 approach for safe communications

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