Training Neural Networks by Enhance Grasshopper Optimization Algorithm for Spam Detection System

Ghaleb, Sanaa A. A.; Mohamad, Mohd Saberi; Fadzli, Syed Abdullah; Ghanem, Waheed Ali H. M.

doi:10.1109/access.2021.3105914

Cited by 13 publications

(6 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With each new generation, the MOBGOA algorithm generates new solutions (a new subset of features is generated). It is entered into the MLP that is trained by the best enhanced GOA (which is introduced in our previous work [41]). The method is employed by employing the novel feature set, and feedback on the method is obtained from the performance of the E2GOAMLP algorithm, which calculates the three objectives and arranges a new solution.…”

Section: ) Wrapper Feature Selection Methods Using Egoamlpmentioning

confidence: 99%

“…It is an evaluation procedure that begins with using the E2GOAMLP algorithm to train multilayered neural networks. The E2GOAMLP algorithm is better understood by the interested reader compared to earlier research [41]. The feature selection process is one of the most crucial processes, and the feature selection algorithm is based on a wrapper algorithm.…”

Section: B the Feature Selection Phase 1) Design Of Multi-objective B...mentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2022

Self Cite

View full text Add to dashboard Cite

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)

show abstract

Section: ) Wrapper Feature Selection Methods Using Egoamlpmentioning

confidence: 99%

Section: B the Feature Selection Phase 1) Design Of Multi-objective B...mentioning

confidence: 99%

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

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…The use of reinforcement learning has been investigated for its ability to optimize spam detection strategies over time [42], [43], [15], [44], [45]. Studies have also shown the effectiveness of neural network architectures in detecting phishing websites, a common form of spam [46], [47], [48]. The application of AI for real-time spam detection in social media platforms has demonstrated promising results, highlighting the scalability of these models [15], [40].…”

Section: B Ai and Machine Learning Models In Spam Detectionmentioning

confidence: 99%

Comparative Analysis of ChatGPT-4 and Google Gemini for Spam Detection on the SpamAssassin Public Mail Corpus

Mardiansyah,

Surya

2024

Preprint

View full text Add to dashboard Cite

This study addresses the critical challenge of spam detection in the realm of cybersecurity, motivated by the escalating sophistication of spamming techniques and their significant implications for communication security. With the advent of advanced artificial intelligence (AI) models, this research compares the efficacy of two leading models, ChatGPT-4 by OpenAI and Google Gemini, in identifying spam within the widely recognized SpamAssassin public mail corpus. Through a meticulous methodology that includes preprocessing of the dataset, application of standardized evaluation metrics (accuracy, precision, recall, and F1-score), and a detailed performance analysis, this study unveils the distinct capabilities of each model in spam detection. ChatGPT-4 demonstrates a balanced performance with high precision and recall, making it suitable for general spam detection tasks. In contrast, Google Gemini excels in recall, highlighting its potential in scenarios where capturing the maximum number of spam emails is paramount, despite a slightly higher tendency to misclassify legitimate emails as spam. These findings contribute valuable insights into the comparative strengths and application contexts of ChatGPT-4 and Google Gemini, offering a nuanced understanding of their roles in enhancing spam detection mechanisms. The study underscores the significance of selecting AI models based on specific spam detection needs and sets a foundation for future research aimed at advancing AI-driven spam detection solutions.

show abstract

“…www.ijacsa.thesai.org Spam e-mail has several detrimental effects, including increased communications overload and cybercrime, as shown by the study of S. A. A. Ghaleb et al [7]. Spam email has become a key weapon for assaults such as cross-site scripting, malware infection, phishing, and cross-site request forgery, etc., which is the most dangerous feature of spam email.…”

Section: Parallel Recent Research Outcomesmentioning

confidence: 99%

Detection of Severity-based Email SPAM Messages using Adaptive Threshold Driven Clustering

Venugopal¹,

Bhaskari²,

Seetaramanath³

2022

IJACSA

View full text Add to dashboard Cite

The classification of emails is one crucial part of the email filtering process, as emails have become one of the key methods of communication. The process for identifying safe or unsafe emails is complex due to the diversified use of the language. Nonetheless, most of the parallel research outcomes have demonstrated significant benchmarks in identifying email spam. However, the standard processes can only identify the emails as spam or ham. Henceforth, a detailed classification of the emails has not been achieved. Thus, this work proposes a novel method for the identification of the emails into various classes using the proposed deep clustering process with the help of the ranking of words into severity. The proposed work demonstrates nearly 99.4% accuracy in detecting and classifying the emails into a total of five classes.

show abstract

Training Neural Networks by Enhance Grasshopper Optimization Algorithm for Spam Detection System

Cited by 13 publications

References 66 publications

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

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

Comparative Analysis of ChatGPT-4 and Google Gemini for Spam Detection on the SpamAssassin Public Mail Corpus

Detection of Severity-based Email SPAM Messages using Adaptive Threshold Driven Clustering

Contact Info

Product

Resources

About