Boyer Moore string-match framework for a hybrid short message service spam filtering technique

Abstract

Advances in technology and the proliferation of mobile device have continued to advance the ubiquitous nature of computing alongside their many prowess and improved features it brings as a disruptive technology to aid information sharing amongst many online users. This popularity, usage and adoption ease, mobility, and portability of the mobile smartphone devices have allowed for its acceptability and popularity. Mobile smartphones continue to adopt the use of short messages services accompanied with a scenario for spamming to thrive. Spams are unsolicited message or inappropriate contents. An effective spam filter studies are limited as short-text message service (SMS) are 140bytes, 160-characters, and rippled with abbreviation and slangs that further inhibits the effective training of models. The study proposes a string match algorithm used as deep learning ensemble on a hybrid spam filtering technique to normalize noisy features, expand text and use semantic dictionaries of disambiguation to train underlying learning heuristics and effectively classify SMS into legitimate and spam classes. Study uses a profile hidden Markov network to select and train the network structure and employs the deep neural network as a classifier network structure. Model achieves an accuracy of 97% with an error rate of 1.2%.

Improved services traceability and management of a food value chain using block-chain network: a case of Nigeria

“…Several studies to enhance information-rich supply value chains have led to advances in mathematical modeling, information management, supply chain management strategies, data mining, etc. These were implemented via tech-rich and tech-dependent structures such as virtualization, the internet of things (IoT), radio frequency identifications (RFIDs), and in recent times, the use of blockchain [5], [6]. In Nigeria, agriculture has since become the primary source of livelihood for many of its citizens.…”

“…Here, a node is referred to as a computing device (physical or virtual) that is part of the blockchain network. Its benefits are poised from the inherent characteristics and feature it possesses such as security, decentralization, smart contracts and portfolios, and auditability [3], [6], [10].…”

Empirical evidence of phishing menace among undergraduate smartphone users in selected universities in Nigeria

“…Socially-engineered attacks use technical subterfuge to defraud a victim of their data by posing as a trusted identity. The messages (also called spam) involve harmless advertising via unsolicited emails, SMS, or network messages, and contain mechanisms to exploit recipient data [7], [8]. The adoption of spam is due to its low-volume-and-high-value target successes, and distribution ease [9].…”

Evidence of personality traits on phishing attack menace among selected university undergraduates in Nigerian

“…Socially-engineered attacks (e.g., spam) often involve harmless advertising via unsolicited emails/short message service (SMS) as network messages. They may contain viruses designed to retrieve a user's personal data [7], [8]. The nature of spam is such that they are structured as a target mechanism for high-value victims and sent out to target high-volume unsuspecting victims besides distribution ease [9].…”