Detecting Spam Email with Machine Learning Optimized with Harris Hawks optimizer (HHO) Algorithm

Mashaleh, Ashraf S.; Ibrahim, Noor Farizah; Al-Betar, Mohammed Azmi; Mustafa, Hossam M. J.; Yaseen, Qussai

doi:10.1016/j.procs.2022.03.087

Cited by 23 publications

(5 citation statements)

References 7 publications

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“…The email header information, including routing details. Spam Indicator A binary label indicating whether the email is spam (1) or not (0).…”

Section: Headermentioning

confidence: 99%

Enhancing Spam Detection: A Crow-Optimized FFNN with LSTM for Email Security

Alsudani,

Nasrawi,

Shattawi

et al. 2024

WJCMS

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Email security is paramount in today's digital landscape, as the proliferation of spam emails poses a significant threat to individuals and organizations alike. To combat this menace, this study introduces a novel approach that marries the power of Crow Search Optimization (CSO) with a Feedforward Neural Network (FFNN) and Long Short-Term Memory (LSTM) architecture to bolster spam detection. The proposed Crow-Optimized FFNN with LSTM (C-FFNN-LSTM) leverages CSO to fine-tune the neural network's parameters, optimizing its ability to distinguish between legitimate emails and spam. The CSO algorithm mimics the collaborative behavior of crows, thereby enhancing the model's convergence and robustness. Experimental results showcase the effectiveness of the C-FFNN-LSTM approach, achieving remarkable accuracy rates and reducing false positives. This innovation not only enhances email security but also offers a promising avenue for refining spam detection algorithms across various domains. In an era of ever-evolving cyber threats, the C-FFNN-LSTM framework stands as a beacon of improved email security, safeguarding digital communication channels. In our methodology, we attained an outstanding accuracy level of 99.1% during the testing phase.

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“…The email header information, including routing details. Spam Indicator A binary label indicating whether the email is spam (1) or not (0).…”

Section: Headermentioning

confidence: 99%

Enhancing Spam Detection: A Crow-Optimized FFNN with LSTM for Email Security

Alsudani,

Nasrawi,

Shattawi

et al. 2024

WJCMS

View full text Add to dashboard Cite

show abstract

“…The method achieved high accuracy in detecting spam emails, though it was suffering from high computational complexity. In contrast, [19] proposed a spam detection method based on the combination of the Harris Hawks Optimizer (HHO) and the KNN classifier. The method has demonstrated promising results in terms of accuracy and processing time.…”

Section: Related Workmentioning

confidence: 99%

An Improved Dandelion Optimizer Algorithm for Spam Detection: Next-Generation Email Filtering System

Tubishat,

Al-Obeidat,

Sadiq

et al. 2023

Computers

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Spam emails have become a pervasive issue in recent years, as internet users receive increasing amounts of unwanted or fake emails. To combat this issue, automatic spam detection methods have been proposed, which aim to classify emails into spam and non-spam categories. Machine learning techniques have been utilized for this task with considerable success. In this paper, we introduce a novel approach to spam email detection by presenting significant advancements to the Dandelion Optimizer (DO) algorithm. The DO is a relatively new nature-inspired optimization algorithm inspired by the flight of dandelion seeds. While the DO shows promise, it faces challenges, especially in high-dimensional problems such as feature selection for spam detection. Our primary contributions focus on enhancing the DO algorithm. Firstly, we introduce a new local search algorithm based on flipping (LSAF), designed to improve the DO’s ability to find the best solutions. Secondly, we propose a reduction equation that streamlines the population size during algorithm execution, reducing computational complexity. To showcase the effectiveness of our modified DO algorithm, which we refer to as the Improved DO (IDO), we conduct a comprehensive evaluation using the Spam base dataset from the UCI repository. However, we emphasize that our primary objective is to advance the DO algorithm, with spam email detection serving as a case study application. Comparative analysis against several popular algorithms, including Particle Swarm Optimization (PSO), the Genetic Algorithm (GA), Generalized Normal Distribution Optimization (GNDO), the Chimp Optimization Algorithm (ChOA), the Grasshopper Optimization Algorithm (GOA), Ant Lion Optimizer (ALO), and the Dragonfly Algorithm (DA), demonstrates the superior performance of our proposed IDO algorithm. It excels in accuracy, fitness, and the number of selected features, among other metrics. Our results clearly indicate that the IDO overcomes the local optima problem commonly associated with the standard DO algorithm, owing to the incorporation of LSAF and the reduction in equation methods. In summary, our paper underscores the significant advancement made in the form of the IDO algorithm, which represents a promising approach for solving high-dimensional optimization problems, with a keen focus on practical applications in real-world systems. While we employ spam email detection as a case study, our primary contribution lies in the improved DO algorithm, which is efficient, accurate, and outperforms several state-of-the-art algorithms in various metrics. This work opens avenues for enhancing optimization techniques and their applications in machine learning.

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“…In [35] proposed a new SDS that incorporates the Harris Hawks Optimizer (HHO). The HHO is used to remove redundant and unnecessary features that could obstruct performance.…”

Section: Related Workmentioning

confidence: 99%

Feature Selection by Multiobjective Optimization: Application to Spam Detection System by Neural Networks and Grasshopper Optimization Algorithm

et al. 2022

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Networks are strained by spam, which also overloads email servers and blocks mailboxes with unwanted messages and files. Setting the protective level for spam filtering might become even more crucial for email users when malicious steps are taken since they must deal with an increase in the number of valid communications being marked as spam. By finding patterns in email communications, spam detection systems (SDS) have been developed to keep track of spammers and filter email activity. SDS has also enhanced the tool for detecting spam by reducing the rate of false positives and increasing the accuracy of detection. The difficulty with spam classifiers is the abundance of features. The importance of feature selection (FS) comes from its role in directing the feature selection algorithm's search for ways to improve the SDS's classification performance and accuracy. As a means of enhancing the performance of the SDS, we use a wrapper technique in this study that is based on the multi-objective grasshopper optimization algorithm (MOGOA) for feature extraction and the recently revised EGOA algorithm for multilayer perceptron (MLP) training. The suggested system's performance was verified using the SpamBase, SpamAssassin, and UK-2011 datasets. Our research showed that our novel approach outperformed a variety of established practices in the literature by as much as 97.5%, 98.3%, and 96.4% respectively. INDEX TERMSSpam detection system (SDS), grasshopper optimization algorithm (GOA), feature selection (FS), multi-objective optimization (MOO), multilayer perceptron (MLP)

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Detecting Spam Email with Machine Learning Optimized with Harris Hawks optimizer (HHO) Algorithm

Cited by 23 publications

References 7 publications

Enhancing Spam Detection: A Crow-Optimized FFNN with LSTM for Email Security

Enhancing Spam Detection: A Crow-Optimized FFNN with LSTM for Email Security

An Improved Dandelion Optimizer Algorithm for Spam Detection: Next-Generation Email Filtering System

Feature Selection by Multiobjective Optimization: Application to Spam Detection System by Neural Networks and Grasshopper Optimization Algorithm

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