Memetic algorithm for short messaging service spam filter using text normalization and semantic approach

Abstract

Today’s popularity of the short messages services (SMS) has created a propitious environment for spamming to thrive. Spams are unsolicited advertising, adult-themed or inappropriate content, premium fraud, smishing and malware. They are a constant reminder of the need for an effective spam filter. However, SMS limitations of 160-charcaters and 140-bytes size as well as its being rippled with slangs, emoticons and abbreviations further inhibits effective training of models to aid accurate classification. The study proposes Genetic Algorithm Trained Bayesian Network solution that seeks to normalize noisy feats, expand text via use of lexicographic and semantic dictionaries that uses word sense disambiguation technique to train the underlying learning heuristics. And in turn, effectively help to classify SMS in spam and legitimate classes. Hybrid model comprises of text preprocessing, feature selection as well as training and classification section. Study uses a hybrid Genetic Algorithm trained Bayesian model for which the GA is used for feature selection; while, the Bayesian algorithm is used as classifier.

Forging a deep learning neural network intrusion detection framework to curb the distributed denial of service attack

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SMS Spam Detection Based on Fuzzy Rules and Binary Particle Swarm Optimization

“…Arnold and Andrew in 2020 introduced a hybrid model for SMS spam detection in which a genetic algorithm (GA) is utilized for feature selection, and the Bayesian network is adopted for classification. The results clarify that the performance of a blend of the GA and Bayesian network surpasses the performance of the Bayesian network alone [15].…”

Empirical Evidence of Socially-Engineered Attack Menace Among Undergraduate Smartphone Users in Selected Universities in Nigeria

“…With advances in ICT beaming continually its potentials on users across the world, its sine-qua-non effect is the myriad of threats that exploit inherent vulnerabilities in the associated techs. These threats manifest in various forms or wayspresenting itself as misleading items of benefits to unsuspecting users, aimed at defrauding them [2,5].…”

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