TY - GEN
T1 - Decision Making by Applying Machine Learning Techniques to Mitigate Spam SMS Attacks
AU - AbouGrad, Hisham
AU - Chakhar, Salem
AU - ABUBAHIA, AHMED
PY - 2023/4/17
Y1 - 2023/4/17
N2 - Due to exponential developments in communication networks and computer technologies, spammers have more options and tools to deliver their spam SMS attacks. This makes spam mitigation seen as one of the most active research areas in recent years. Spams also affect people’s privacy and cause revenue loss. Thus, tools for making accurate decisions about whether spam or not are needed. In this paper, a spam mitigation model is proposed to find spam from non-spam and the different processes used to mitigate spam SMS attacks. Also, anti-spam measures are applied to classify spam with the aim to have high classification accuracy performance using different classification methods. This paper seeks to apply the most appropriate machine learning (ML) techniques using decision-making paradigms to produce a ML model for mitigating spam attacks. The proposed model combines ML techniques and the Delphi method along with Agile to formulate the solution model. Also, three ML classifiers were used to cluster the dataset, which are Naive Bayes, Random Forests, and Support Vector Machine. These ML techniques are renowned as easy to apply, efficient and more accurate in comparison with other classifiers. The findings indicated that the number of clusters combined with the number of attributes has revealed a significant influence on the classification accuracy performance.
AB - Due to exponential developments in communication networks and computer technologies, spammers have more options and tools to deliver their spam SMS attacks. This makes spam mitigation seen as one of the most active research areas in recent years. Spams also affect people’s privacy and cause revenue loss. Thus, tools for making accurate decisions about whether spam or not are needed. In this paper, a spam mitigation model is proposed to find spam from non-spam and the different processes used to mitigate spam SMS attacks. Also, anti-spam measures are applied to classify spam with the aim to have high classification accuracy performance using different classification methods. This paper seeks to apply the most appropriate machine learning (ML) techniques using decision-making paradigms to produce a ML model for mitigating spam attacks. The proposed model combines ML techniques and the Delphi method along with Agile to formulate the solution model. Also, three ML classifiers were used to cluster the dataset, which are Naive Bayes, Random Forests, and Support Vector Machine. These ML techniques are renowned as easy to apply, efficient and more accurate in comparison with other classifiers. The findings indicated that the number of clusters combined with the number of attributes has revealed a significant influence on the classification accuracy performance.
KW - communication networks
KW - computer technologies
KW - spammers
KW - spam SMS attacks
KW - spam mitigation
KW - privacy
KW - revenue loss
KW - spam mitigation model
KW - anti-spam measures
KW - high classification accuracy
KW - different classification methods
KW - machine learning (ML) techniques
KW - decision-making paradigms
KW - ML techniques
KW - the Delphi method
KW - Agile
KW - classifiers
KW - classification accuracy performance
KW - Mobile Network Security and Privacy Solution
KW - Feature Classification Algorithms
KW - Spam Analytics Model
KW - Decision-Making Method
KW - Machine Learning Algorithms
KW - Mitigating Spam Techniques
U2 - 10.1007/978-3-031-30396-8_14
DO - 10.1007/978-3-031-30396-8_14
M3 - Conference proceeding (ISBN)
SN - 9783031303951
VL - 670
T3 - Lecture Notes in Networks and Systems
SP - 154
EP - 166
BT - Key Digital Trends in Artificial Intelligence and Robotics. ICDLAIR 2022. Lecture Notes in Networks and Systems
A2 - Troiano, Luigi
A2 - Vaccaro, Alfredo
A2 - Kesswani, Nishtha
A2 - Díaz Rodriguez, Irene
A2 - Brigui , Imene
A2 - Pastor-Escuredo, David
PB - Springer Cham
ER -