Hybrid big bang–big crunch with ant colony optimization for email spam detection

Rathika Natarajan (), Abolfazl Mehbodniya (), Murugesan Ganapathy (), Rahul Neware (), Swimpy Pahuja (), Luis Vives () and Asha
Additional contact information
Rathika Natarajan: Department of Electronics and Communication Engineering, Jaya Institute of Technology, Thiruthani, Thiruvallur, Tamilnadu, India
Abolfazl Mehbodniya: Department of Electronics and Communications Engineering, Kuwait College of Science and Technology, Doha Area, 7th Ring Road, Kuwait
Murugesan Ganapathy: Department of Computer Science and Engineering, St. Joseph’s College of Engineering, Chennai 600119, Tamil Nadu, India
Rahul Neware: Department of Computing, Mathematics and Physics, Western Norway University of Applied Sciences, Bergen, Norway
Swimpy Pahuja: Department of Computer Science and Engineering, School of Engineering and Technology, CMR University, Bangalore, Karnataka 560043, India
Luis Vives: Department of Computer Science, Peruvian University of Applied Sciences, Lima 15023, Peru
Asha: Department of Computer Science, Bhaskaracharya College of Applied Sciences (University of Delhi), Sector-2, Phase-1, Dwarka, New Delhi 110075, India

International Journal of Modern Physics C (IJMPC), 2022, vol. 33, issue 04, 1-21

Abstract: Electronic mails (emails) have been widely adapted by organizations and individuals as efficient communication means. Despite the pervasiveness of alternate means like social networks, mobile SMS, electronic messages, etc. email users are continuously growing. The higher user growth attracts more spammers who send unsolicited emails to anonymous users. These spam emails may contain malware, misleading information, phishing links, etc. that can imperil the privacy of benign users. The paper proposes a self-adaptive hybrid algorithm of big bang–big crunch (BB–BC) with ant colony optimization (ACO) for email spam detection. The BB–BC algorithm is based on the physics-inspired evolution theory of the universe, and the collective interaction behavior of ants is the inspiration for the ACO algorithm. Here, the ant miner plus (AMP) variant of the ACO algorithm is adapted, a data mining variant efficient for the classification. The proposed hybrid algorithm (HB3C-AMP) adapts the attributes of B3C (BB–BC) for local exploitation and AMP for global exploration. It evaluates the center of mass along with the consideration of pheromone value evaluated by the best ants to detect email spam efficiently. The experiments for the proposed HB3C-AMP algorithm are conducted with the Ling Spam and CSDMC2010 datasets. Different experiments are conducted to determine the significance of the pre-processing modules, iterations, and population size on the proposed algorithm. The results are also evaluated for the AM (ant miner), AM2 (ant miner2), AM3 (ant miner3), and AMP algorithms. The performance comparison demonstrates that the proposed HB3C-AMP algorithm is superior to the other techniques.

Keywords: Big bang–big crunch; theory of universe; ant colony optimization; email spam; email spam detection; ant miner plus; meta-heuristic (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S0129183122500516
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:33:y:2022:i:04:n:s0129183122500516

Ordering information: This journal article can be ordered from

DOI: 10.1142/S0129183122500516

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 ().