Advanced International Journal for Research

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Intelligent Spam Detection Framework Based on Genetic Algorithm and Machine Learning

Author(s) Mrs. D Donthi Sadhana, A.Nagarjuna Reddy, Dr. V. Anantha Krishna, Dr. U. Srilakshmi
Country India
Abstract The digital communication has improved quickly, and a large number of unsolicited messages, or spam, may overload email systems, drop productivity, and even affect cyber security. Spam or unwanted messages that are unsolicited and can be usually harmful has become a significant issue to email systems and electronic communication platforms. This paper presents an Intelligent Spam Detection Framework (ISDF) which is an efficient spam communication detection and filtering framework that operates by integrating Genetic Algorithms (GA) and Machine Learning (ML) algorithms. The framework employs this to optimize the process of selecting features by the use of genetic algorithm, where only the most relevant and discriminative features are employed in training the machine learning model. This will decrease the size of data leading to enhanced model performance and it would also accelerate the detection. The system will combine the conventional machine learning algorithms such as Random Forests, Naive Bayes , and Support Vector Machines (SVM) to mark a message spam or not, by integrating the characteristics of linguistic, metadata, and context features. Genetic algorithm has great optimization ability which makes the detection process more accurate as it evolves and adapts the set of features as the time progresses. The proposed framework ISDF is then evaluated on a number of benchmark spam datasets and the findings reveal that the proposed structure has had a higher accuracy, better precision and recall than the traditional spam detection techniques. This smart system is a scalable way of managing spam on a large number of digital communication mediums to guarantee better user experience and greater safety. The proposed Genetic Algorithm (GA)-optimized spam detection method is systematically compared with the established machine learning classifiers e.g. XGBoost, Multinomial Naïve Bayes, and Linear Support Vector Machine (SVM). The experimental results prove that the existing models were apt to 97.8% of Multinomial Naïve Bayes, 98.9% of Linear SVM, and 99.2% of XGBoost. On the contrary, the best accuracy value of the proposed Genetic Algorithm-optimized model of 99.5 was the highest. In this comparative study, the incorporation of Genetic Algorithm-based optimization can be observed to increase the efficiency in feature selection and parameter tuning, thus, increasing classification efficiency and general predictiveness. The results show that suggested approach excels the conventional machine-learning models when it comes to spam identification in terms of both accuracy and strength.
Keywords Spam Detection, Machine Learning, Genetic Algorithm, Feature Selection, Text Classification, Email Filtering.
Field Computer > Network / Security
Published In Volume 7, Issue 4, July-August 2026
Published On 2026-07-07
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E-ISSN 3048-7641
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