Amalgamating an intelligent variant of the gravitational search algorithm with decision trees for email spam detection

Sheetal Kundra (), Rama Kant, Sanjiv Kumar Singh (), Rupinder Saini (), Brijesh Kumar Gupta (), Vishnu Sandeep Reddy Manukonda () and Vishal Jain ()
Additional contact information
Sheetal Kundra: Department of Computer Science and Engineering, Guru Nanak Institute of Technology, Ibrahimpatnam, Hyderabad 501506, Telangana, India
Rama Kant: Department of Computer Science and Engineering, GL Bajaj Group of Institutions, Mathura 281406, Uttar Pradesh, India
Sanjiv Kumar Singh: Department of Computer Science and Engineering, GL Bajaj Group of Institutions, Mathura 281406, Uttar Pradesh, India
Rupinder Saini: Department of Computer Science and Engineering, Guru Nanak Institute of Technology, Ibrahimpatnam, Hyderabad 501506, Telangana, India
Brijesh Kumar Gupta: Department of Computer Science and Engineering, GL Bajaj Group of Institutions, Mathura 281406, Uttar Pradesh, India
Vishnu Sandeep Reddy Manukonda: Department of Computer Science and Engineering, Indian Institute of Information Technology, Agartala 799046, Tripura, India
Vishal Jain: Department of Computer Science and Engineering, School of Engineering and Technology, Sharda University, Greater Noida 201310, Uttar Pradesh, India

International Journal of Modern Physics C (IJMPC), 2025, vol. 36, issue 03, 1-20

Abstract: In today’s interconnected digital world, email remains a primary mode of digital interaction valued for its suitability in official, academic and business communications. However, despite its utility, email faces significant challenges due to the widespread presence of spam in various forms, such as phishing, suspicious attachments and deceptive content. This issue not only affects the efficiency and security of email communication but also poses a barrier to its reliability. Therefore, it is essential to devise effective methods to tackle the escalating count of spam emails. This research work presents an intriguing methodology to combat the persistent problem of email spam. The proposed method, abbreviated as AIGSADT, is an amalgamation of the intelligent variant of the gravitational search algorithm (IGSA) and decision trees (DTs). The machine learning-based DT algorithm is individually inadequate for dealing with the large amount of constructive data on a certain attribute due to its instability and ineffectiveness. The proposed AIGSADT approach integrates the IGSA algorithm, which is effective in handling large amounts of data to detect email spam. This is achieved by constructing decision trees employing gravitational forces as the means of information transfer through mass agents. Here, the intelligent factor of the IGSA algorithm prevents the trapping of GSA agents in local optima by updating their positions based on information provided by the best and worst agents. The performance of the presented AIGSADT approach is analyzed through experiments conducted on various categories available in the Ling spam dataset. These experimental evaluations aim to analyze the significance of different pre-processing modules across different dataset categories. The comparative analysis indicates the supremacy of the proposed approach compared to state-of-the-art methodologies.

Keywords: Decision tree; email spam detection; gravitational search algorithm; machine learning; metaheuristic; intelligent gravitational search algorithm; physics-inspired computation algorithm (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S0129183124501912
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wsi:ijmpcx:v:36:y:2025:i:03:n:s0129183124501912

Ordering information: This journal article can be ordered from

DOI: 10.1142/S0129183124501912

Access Statistics for this article

International Journal of Modern Physics C (IJMPC) is currently edited by H. J. Herrmann

More articles in International Journal of Modern Physics C (IJMPC) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().